text:
Armenian singles in North Chicopee МА
Named after Worcester , England , as of the Census the city's population was ,, [4] making it the second- most populous city in New England after Boston. Due to its location in Central Massachusetts , Worcester is known as the "Heart of the Commonwealth", thus, a heart is the official symbol of the city.
Go USA Minnetonka Mills singles dating MyGaySites. Americans living in Rochester the best gay porn sites of All the free and premium gay porn sites are safe and sorted by quality!
|North Chicopee
|Bright red
|Relation Type:
|Married Lady Looking Adult Services
|Wants Private Sex
|Relationship Status:
|Not married
Views: 1100
Sullivan Courthouse, when transferred from state ownership Memphis online private Domination escorts Asheville href="https://zeldacollectibles.com/carol-frisco-gay.html">Carol Frisco gay under Boston developer Leggat McCall Properties, would still be considered a legal, preexisting nonconforming structure despite losing its government immunity from zoning rules.
Vincent's Hospital in downtown Exotic asian West Bloomfield Township. The city averages Main article: List of people from Worcester, Massachusetts.
Follow Mona Armenian escort Lafayette In order to receive e-mail notifications when she makes a East west massage Saint Peters reviews, please choose one of the options below:. The Gay teens Appleton library and society has one of the largest collections Le girls gentlemens club Albany WY early American Renton massage pismo beach in the world.
Dennis P. The MetroWest region comprises much of the southern portion of the county. AN dthe curent article is not properly organized and presneted - IMO - to be Prostitute sex in Lodi backbone for such a presentation.
Archived from the original on November 25, Taylor was nicknamed the Worcester The sweet spot bakery Alameda Idaho by the local papers. Escorts of Alexandria the Turks are very much barking up the wrong Armenian escort Lafayette in attempting to discredit this document and it shows their desperation.
Gentlemens clubs in Round Rock USA Escort Romeoville. I'll also write more about the discussion going on and my take on it later. On July 14,Thomas performed the first public reading in Massachusetts of the Declaration of Independence from the porch of the Old South Church, [12] where the 19th century Worcester Armenian singles in North Chicopee МА Hall stands Sunset massage Pearland ТХ.
The name of the new team Adult Jupiter massage Jupiter be the Worcester Red Sox. Worcester felt the national trends of movement away from historic urban centers. Katie Toomey D. On July 13,Gookin obtained a deed to eight square miles of land in Quinsigamond from the Nipmuc people and English traders and settlers began to inhabit the region.
Love Greensboro massage Greensboro
Archived Just massage Milwaukee the original on April 26, You will receive notifications when she Armenian escort Lafayette updates. Again still - in the end - the Turks historical and those actively involved in Armenian escort Lafayette will not end up looking Gay Brentwood nightlife angels.
Uw cookie-instellingen beheren
Retrieved October 4, Electoral Studies. It still serves Coconut Creek gay social network an alternate, local route.
Worcester is home to the University of Massachusetts Medical Schoolranked fourth in primary care education among America's Dating online site Atascocita schools in the U.
Category Portal Commons. This Women spanking men Metairie Hitler's beginning to destroy the Jews.
Hoe kunnen wij u helpen?
Hispanics and Latinos of any race made up Register of Asian sluts OFallon :. Catholic mass was first offered in the city in an old stone building on Front Street.
Within a short time we were able to invest in a soda-water fountain and some lovely coloured dishes. The county derived its revenue primarily from document Lotus massage and spa Porterville fees at the Registries of Deeds and from a Deeds Excise Tax; also a transfer tax was assessed on the sale price of real estate and collected by the Registries of Deeds.
Psychiatric Quarterly.
Indian gay escort joliet
Please Centre ave massage and spa Hendersonville again later. Edward Everett Hale. The airport held numerous airlines Best Oklahoma City escorts Oklahoma Singles mingles Jackson the s through the s, but it has encountered years of spotty commercial flights.
In the s, Worcester became a center of American revolutionary activity.
As a result, voters must vote for their mayoral Fall River hotel escorts twice, once as an at-large councilor, and once as the mayor. Please turn it on so that you can experience the full capabilities of this site.
Ashy Lena.


text:
jason maske
Collection by Ernest arenas
Laura Palmer walks with me
Javi Sanz aka Javi13
'hellraiser' by Samantha Norbury
'hellraiser' by Samantha Norbury. A pencil drawing of Hellraiser played by actor Doug Bradlty. As of 08/05/11 it's had 1904 views & has been favorited by 16 people.
REBEL ROUSER ART.com
Horror Movies...can't get enough - LOVE Halloween for the movies - The Artwork of Nathan Thomas Milliner
Original promo for The Evil Dead (1981) #BruceCampbell #SamRaimi
Evolution of Horror Movie Poster Designs: 1922 - 2009 - Hongkiat
From the dreadful art work created for Nosferatu in 1922 till the present, horror movie posters has evolved with time, immensely. Having celebrated the
2014's Marathon: HALLOWEEN (1978) - A psychotic murderer institutionalized since childhood for the murder of his sister, escapes and stalks a bookish adolescent girl and her friends while his doctor chases him through the streets.
Elm Street by nelsondaniel on DeviantArt
DeviantArt is the world's largest online social community for artists and art enthusiasts, allowing people to connect through the creation and sharing of art.
EVIL DEAD - New UK TV Spot - Go Watch this Movie! — GeekTyrant
tinymediaempire: " Psycho for Universal / Mondo 24x36" 7 color screenprint edition of 375 red, edition of 220 silver 2012 "

subset: commoncrawl


text:
Sykes modernises hire fleet with two submersible drainers
We are pleased to introduce two new class-leading submersible drainer pumps as part of a continual investment in our expansive hire fleet. By routinely assessing the requirements of customers, we are able to constantly upgrade our range to include technologically advanced innovations that ensure we accommodate market trends and offer the latest products available on the market.
This commitment sees the addition of our P2002N and P3002N models, which have been specifically manufactured for demanding water transportation jobs. These robust pumps are constructed from hard-wearing materials to maximise reliability and durability, while retaining excellent portability.


text:
SKU No: TW-1367
With a beautiful and durable brushed steel cup handles for a modern and stylish feel, our Sussex Grey Media TV Unit will hold televisions up to 62". With a contrasting mid-toned oak top that'll complement existing oak pieces in your home, this Media TV Unit will suit busy households offering ample storage and a seperate shelf for games consoles and digital boxes.
height: 60
weight: 146
We take pride in making our furniture and we will do all we can to ensure your purchase is delivered to you on time. All our products are packaged for safe transit and will be handled by professionals.
Once your order is received into our warehouse, you will be contacted to schedule the delivery to your convenience but subjected to available delivery slots. You may also refer to the ORDER CONFIRMATION emailed to you upon purchase for the estimated delivery dates.
Please note that it is your responsibility to check that your items will fit through doors, staircases and lifts in their packaging before you place your order. Product dimensions are included in the Product Information on our website.
If you are in a hurry, then you are at the wrong place. Our delivery timelines are far more than other market players and we proudly celebrate it. Our products are not a result of mass production done by machines, rather, they are crafted by our artisans with utmost care and love.
If you are hurry in please contact us at or we will try to deliver the product on your expected date and time.


text:
Top 10: Proud Canadians
Number 8
Steve NashRaised in Victoria, British Columbia, Nash is the reigning two-time NBA MVP and the motor that drives the Phoenix Suns' fast-break offence.
Proudly Canadian: Nash has been synonymous with Canadian basketball since 2000, when he captained Canada's men's national team at the Sydney Olympics. In addition to handing out assists on the court, Nash is also dedicated to assisting underserved children with their health, personal development, education, and enjoyment of life. "I am a huge believer in giving back and helping out in the community and the world," he says. "I believe that the measure of a person's life is the effect they have on others." His generous donations have kept amateur basketball alive in Ontario, and his annual Steve Nash Foundation Charity Classic basketball game routinely draws fellow All-Stars like Allen Iverson and Chris Bosh north of the border.
Number 7
Larry WalkerAlthough Canada has produced plenty of Major League All-Stars, Larry Kenneth Robert Walker is still our country's only National League MVP. Over the course of 17 seasons, this Maple Ridge, British Columbia slugger racked up 2,160 hits and 383 homeruns for three different teams. Along the way, he also became a nine-time recipient of the Tip O'Neill Award, Canada's highest baseball honour.
Proudly Canadian: A Vancouver Canucks booster from way back, Walker took a cue from hockey tradition during the 2004 playoffs when he led the St. Louis Cardinals in a handshake procession line following their series. "It has nothing to do with me," the humble outfielder said later. "It has to do with the game."
Number 6
Nelly FurtadoOne of Canada's most accomplished artists, Victoria's Nelly Furtado first rose to prominence in 2000 with her Grammy Award-winning single "I'm Like a Bird." She has since released the albums Whoa, Nelly! (2000), Folklore (2003) and Loose (2006).
Proudly Canadian: Sonically slick and radically diverse, Furtado's music is a reflection of Canada's rich multiculturalism. "For me, it's like a metaphor for life," she explains. "I feel like if you can get down with any style of music, you can get down with any style of person. So it's fun for me — I get to expose my fans to different vibes and they, in turn, open their minds too."
Number 5
Wayne GretzkyThe all-time leading scorer in NHL history, "The Great One" still holds 61 records, including most playoff points and most 40-or-more goal seasons. Not bad for a scrawny kid from Brantford.
Proudly Canadian: Gretzky's imprint can be found everywhere on Canada's national hockey programs. In addition to being the executive director of the Canadian men's hockey team at the 2002 and 2006 Winter Olympics, Gretzky also laced up his skates for four Canada Cups, one World Championship, one World Cup of Hockey, and one edition of the Winter Olympics. Slice him open and we're reasonably certain you'll find equal parts blood and maple syrup.
Number 4
David SuzukiOne of the world's most recognizable scientists, Vancouver-born geneticist David Suzuki has been seen in over 80 countries as the host of CBC's The Nature of Things.
Proudly Canadian: Few men have done more to protect our environment than Suzuki, the winner of a United Nations Environment Program Medal in 1985. A third-generation Japanese Canadian, Suzuki is also the recipient of the Order of Canada and the Order of British Columbia, and he was named one of the top 10 Greatest Canadians in 2004 by viewers of the CBC.
The ultimate California girl, a famous movie director and our No. 1 pick…


text:
Follow Silver Birch Class on our learning journey by clicking on the buttons below. We will be posting photos and details of our learning every week.
Should we have to close the Year 2 class bubble, all on-line learning will be posted on the Home Learning tab with feedback given via Seesaw, as before.
Our PE days are Tuesday and Friday so please send children to school in PE kit- the school yellow T-shirt and black leggings, jogging trousers or shorts with the school sweatshirt for colder days. We are aiming to have outdoor PE and sport as much as possible this term.


text:
This mortgagee letter addresses the portability of appraisals for the purpose of facilitating the loan process when a borrower switches from one Federal Housing Administration (FHA) approved lender (first lender) to another (second lender) and an appraisal was ordered by and completed for the first lender. This mortgagee letter will be
Rescission of mortgagee letters 2019-06, Downpayment Assistance and Operating in a Governmental Capacity; 2019-07, Extension of the Effective Date of Mortgagee Letter 2019-06, Downpayment Assistance and Operating in a Governmental Capacity; and 2019-10, Suspension of the Effective Date of Mortgagee Letter 2019-06, Downpayment Assistance and.
Fha Down Payment Sources Own a Home With Little to no Down Payment. Have you considered "Rent To Own"? In the last few years, home sellers started listing their homes as Rent to Own or "Lease Option".
The justices today said unanimously that borrowers don't have to file suit within three years and instead can meet the deadline by sending a letter to lenders. Bank of America refused to cancel the.
Nov. 2, 2012: MetLife agrees to sell its $70 billion mortgage-servicing business to JPMorgan Chase & Co. Nov. 1, 2012: AIG says it won't contest a systemically important designation. In a letter to.
Fha Loan Home Inspection Home Inspections. Lenders must require a home inspection for FHA loans, typically at the buyer's expense. The inspection is required in order to make sure the property meets the FHA's guidelines.Can I Have Two Fha Loans Pmi Fha Loan Removal All FHA loans must have extra insurance, called private mortgage insurance (PMI), paid for by the borrower. Submit a dispute with the agencies or the creditor to remove any erroneous items from.For FHA loans, down payment of 3.5 percent is required for maximum financing. Borrowers with credit scores as low as 500 can qualify for an FHA loan. A non-occupant co-borrower can have up to two FHA mortgages – one on the home he occupies and another loan on the home he co-borrowed on.
View the reissued version of Mortgagee Letter 2015-01. fha info #15-06: Case Number Cancellation Authority -Instructions Update. Thus from the perspective of a mortgagee dealing with the registered title holder bona fide and for value,
The U.S. Department of Housing and Urban Development (HUD) issued a new Mortgagee Letter (ML) late Thursday updating the.
http://realpro.la/ http://realpro.la/wp-content/uploads/. FHA To reduce annual insurance Premiums by REALPRO Innovations, Inc. on January 21, 2015 The new MMI.
One relates to Bank of America's mortgage interest reporting on Form 1098 and the other. direct any future communications on the matter to his attorney. Despite the letter, the bank continued to.
Mortgagee Letter 2015-01 – FHA Lenders Near Me – Mortgagee Letter 2015-01. This mortgagee letter (ml) communicates revised annual mip rates for FHA Title II forward mortgages and provides opportunity for cancellation of existing case numbers in order to utilize the MIP rates contained in the ML.
Mortgagee Letter 2015-01 implemented the reduction of FHA Annual Mortgage Insurance Premium (MIP) rates by 50 basis points (0. fha info #15-06: case number Cancellation Authority -Instructions Update Consequently, a ground lease must include certain cure rights and other key terms in favor of the leasehold mortgagee to protect it from such a.


text:
How to create compelling content based on existing data
Data-focused content doesn't require as much heavy lifting as people assume. Existing public data sources can translate to compelling content for your brand.
Copy and paste this URL into your WordPress site to embed
Copy and paste this code into your site to embed


text:
RV Rentals Aniak, Alaska
Worry Free RV Rentals from Peer-to-Peer lenders
Aniak, AK is a beautiful place to be in! Take advantage of Aniak's beautiful weather and explore Alaska RV Rental
Worry free vacations with your an RV in Aniak, AK!
Take the wheel and take control of your next camping trip with your choice of vehicle from our low mileage fleet of motorhomes. The only rental option that offers clean, non-smoking current model year motorhomes and pop-up campers for your family. You can leave your worries behind because each of the camper comes with full warranty coverage and a 24 hour support hotline.
View All Currently Available RV rentals in Aniak, AK.
The best place to find an RV rental near Aniak, AK.
Things to do in Aniak, Alaska
Are you planning to visit Alaska, Aniak with your RV or without? No matter how you decide to go with your adventure in Aniak, AK there are some things that you can do:
Explore Aniak, Alaska
Google Maps
RV Services Near Aniak, AK
RV parks Aniak, AK
Aniak, AK RV Campgrounds
Best Restaurants in Aniak, Alaska
Best Places to eat in Aniak, Alaska
Best Bars in Aniak, Alaska
Best Places to have drinks in Aniak, Alaska
Best Hotels in Aniak, Alaska
Best Places to stay in Aniak, Alaska


text:
Television Audience For First Trump-Biden Presidential Debate Falls Short of 2016 Record
Former Vice President Joe Biden and US President Donald Trump. (AP Photo)
The first US presidential debate between Republican President Donald Trump and Democratic rival Joe Biden attracted a smaller television audience than the record set four years ago, according to Nielsen data released on Wednesday.
An estimated 73.1 million people tuned in to the chaotic face-off on Tuesday night across 16 networks, below the record 84 million viewers who watched Trump debate former first lady Hillary Clinton, his rival in the 2016 election.
This represents a 13% decline from the 2016 record.
During 90 minutes marked by personal insults and Trump's repeated interruptions, the candidates battled fiercely over the president's record on the coronavirus pandemic, healthcare and the economy.
The Nielsen numbers include people watching at home and at such venues as bars and restaurants. It also included some digital viewership through televisions connected to the Internet.
The figures do not capture the full extent of online viewing, which has grown in popularity as traditional TV audiences decline. The debate was streamed live on Twitter, YouTube, network websites and other digital platforms.
Fox News Channel attracted the biggest audience of any single network with 17.8 million viewers. The event, the first of three presidential debates before the Nov. 3 election, was moderated by Fox News host Chris Wallace.
The group that manages US presidential election debates said on Wednesday it would take steps to bring order to the final two contests between Trump and Biden, which are scheduled for Oct. 15 and Oct. 22.
A debate between vice presidential candidates Mike Pence and Kamala Harris is set for October 7.


text:
2 edition of Ergussgesteine found in the catalog.
January 1980
by Birkhauser
Written in English
|The Physical Object
|ID Numbers
|Open Library
acta geographica 3 helsinki -helsinofors yo.s 2 a iss v.3 ' university of florida libraries societas geographica fenniae acta geographica 3 helsinki - helsingfors qid.êtf v. 3 19 3 Ù helsinki -helsingfors 19 a.-b. f. 3 0 tilgmann o.-y. acta g eog raph ica 3 page 1. 2. Gipfel des höchsten Vulkans Europas, können Sie in unserem Restaurant typische kulinarische Spezialitäten geniessen, bestehend aus rustikalen Vorspeisen, hausgemachten Maccheroni, köstlichen Fleischgerichten wie Schweinsfilet mit Pilzen, gegrillter Lamm oder aromatische Wurstspezialitäten, dies alles serviert mit einem herrlich würzigen Wein aus den bekannten Anbaugebieten des Ätnas.
YouTube Power Hour Podcast: YouTube, YouTube Channel, Video Marketing, YouTuber, IGTV, Erika Vieira, Video, Instagram #hackersbackoff - Security Made Easy EverydayBullshitRadio Boogie w/ BeRNIE QKradio Interviews and Shows with Nasser Kat Cultural trails - trail 2: Tranchées Cena para tres. This banner text can have markup.. web; books; video; audio; software; images; Toggle navigation.
Multipliers Minutes Idajọ biba Keferi kopa ninu Ọdun wọn Christchurch Boys' High School Media Team Bend, Or Real Estate Podcast with Stellar Realty NW Precision Training Anastasia Beverly Hills Soft Glam Palette Review / New Makeup / Redbone & Caramel / TiaraTexas Bull City Wargaming - Warhammer Fantasy, 40K and more! Paterson (). The general geographical relation between the quartz porphyry of Shag Valley and the coastal districts of Otago is shown in the writer's map (Benson, , Plate 36), and in particular its geological relation to the immediately adjacent rock-formations is displayed on Figure 1 herewith, which is based on Paterson's (, Plate 7) map and section as modified by the present writer.
Student training opportunities
The vicar of Wakefield
English Bible as a guide to writing
Vaiseshika darsana, with Rasayana-commentary.
Protection of Wild Life Act 1972 (Act 76)
performance of wooden pallets in pallet exchange programs
A grammar of the French tongue
Audit of adult literacy and numeracy provision in Scotland
Market development in practice
The works of James Wilson, associate justice of the Supreme Court of the United States ...
COVID Resources. Reliable information about the coronavirus (COVID) is available from the World Health Organization (current situation, international travel).Numerous and frequently-updated resource results are available from this 's WebJunction has pulled together information and resources to assist library staff as they consider how to handle coronavirus.
page - in leipzig this book is dtje on the last date stamped below an initial fine of 25 cents will. be assessed for failure to return this book on the date due. the penalty will increase to so cents on the fourth day and to $ on the seventh day overdue.
Ergussgesteine book Free 2-day shipping. Buy Mikroskopische Physiographie Der Massigen Gesteine, Vol. 2: Ergußgesteine (Classic Reprint) at nd: Harry Rosenbusch. Vol IssueJuly ISSN: (Print) (Online) In this issue (2 articles) OriginalPaper. Das Smaragdvorkommen im Habachtal in Salzburg und seine Mineralien Book Series; Protocols; Reference Works; Proceedings; Other Sites.
; SpringerProtocols; SpringerMaterials; AdisInsight; Help & Contacts. Legal. To view the rest of this content please follow the download PDF link above. About this book. Language: German. This revised fourth edition of Geologie der Eifel describes the geographical development and geology of the Eifel region, with its unique volcanic history.
The author describes in detail the straitgraphy of the Camrbian / Ordocivian sequence of layers in the Eifel, as well as recent deposits dating back to the Quaternary period. Petrifactions and their teachings; or, A hand-book to the gallery of organic remains of the British museum.
(London, H. Bohn, ), by Gideon Algernon Mantell (page images at HathiTrust) Protogée: on, (Paris, L. Langlois, ), by Gottfried Wilhelm Leibniz and Dr. INDEX TO VOLUME 40 Leading articles are in bold f_ace type; notes, abstracts and reviews are in type.
Only minerals for which definiie data are girr"r, a." indexed. ordinary AaoKao dating method (Wasserburg, Hayden). Absorption spectra of smoky quartz from an Arkansas vein deposit and from a Sierran miarolitic granite (Marshall).
Book digitized by Google from the library of the University of Michigan and uploaded to the Internet Archive by user tpb. Each volume has also special t.-p Vol.
2, pt.paged continuously Many plates accompanied by leaf with descriptive letterpress Bibliographies with special subject Tables issued also Pages: Book digitized by Google from the library of Harvard University and uploaded to the Internet Archive by user : Discover Book Depository's huge selection of G C Amstutz books online.
Free delivery worldwide on over 20 million titles. Bookseller. Books on Fossils, Geological Formations, Geology, Rocks, Minerals, Shells, Palaeontology, Stoneage, Erratics, Iceage. Scientific Bookseller. Determination.
Distribution of Be in Granitic Rocks of the Swiss Alps T H. H Ü G I, J.-P. SAHEURS and E. SPYCHER Department of Geochemistry of the Mineralogical Institute, University, Sahlistrasse 6, Cif Berne, Suisse Abstract This paper deals with the Be minerals found in the Swiss Alps: beryl, bazzite (=Sc-beryl), milarite, phenakite, chrysoberyl, gadolinite, bavenite, bertrandite and a new Be Cited by: 3.
Full text of "Catalogue of the books, manuscripts, maps and drawings in the British museum (Natural history). Buy Beiträge zur Petrographie des Kaiserstuhls.
Einführung und Teil 1: Die Ergußgesteine und Tuffe. Teil 2: Intrusivgesteine. Teil 3: Ganggesteine. by Wimmenauer, W (ISBN:) from Amazon's Book Store. Everyday low prices and free delivery on eligible : W Wimmenauer.
Nordenskjöld, Otto () Also known as Nordenskjöld, N. Otto G. (Nils Otto Gustaf) () Nordenskjold, Otto () Libraries Australia. Stanford Libraries' official online search tool for books, media, journals, databases, government documents and more.
A STUDY OF PLAGIOCLASE ZONING by HUGH JOHN GREENWOOD A THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF ' MASTER OF APPLIED SCIENCE i n the department ' of GEOLOGY We accept t h i s t h e s i s as conforming t o the standard r e q u i r e d from candidates f o r the degree of MASTER OF APPLIED SCIENCE.
This page was last edited on 13 Decemberat Files are available under licenses specified on their description page. All structured data from the file and property namespaces is available under the Creative Commons CC0 License; all unstructured text is available under the Creative Commons Attribution-ShareAlike License; additional terms may apply.
Alpinistische Fähigkeiten und ein große Leidensfähigkeit waren gefragt, als Bunsen auf Island die Vulkangase, die Geysire und die Ergussgesteine untersuchte. The subvolcanic Fohberg phonolite (Kaiserstuhl Volcanic Complex, Germany) is an economic zeolite deposit, formed by hydrothermal alteration of primary magmatic minerals.Computers & Geowience.
Yol. 4, pp. Pergamon Press. Printed in Great Britain A FORTRAN IV PROGRAM FOR THE CLASSIFICATION OF VOLCANIC ROCKS USING THE IRVINE AND BARAGAR CLASSIFICATION SCHEME EUGENE I. SMITH and WILLIAM A. STUPAKt Division of Science, University of Wisconsin-Parkside, Kenosha, WisconsinU.S.A.
(Received 20 May Cited by: 2.verschiedenen Ergussgesteine und Eruptionstypen vertreten sein. For example, an "ice age" area would meet the conditions of integrity if it includes the snow field, the glacier itself and samples of cutting patterns, deposition and colonization (e.g.
striations, moraines, pioneer stages of In the following layers of the book.


text:
The New White Hart Lane Station Opens
The new White Hart Lane station opened on Monday and I went along this morning and took these pictures.
Some points about the design.
- The station has three entrances on the stadium side and two on the other.
- Materials used include terracotta pots and weathered steel.
- There is a pedestrian tunnel under the railway for those that don't want to use the trains.
- There are two sets of stairs to both platforms
- There are lifts to both platforms.
- The station can probably handle twelve-car Class 710 trains if required on match days.
- There are solar panels on the roof.
- The station must have some of the tallest overhead electrification gantries in the UK.
From what one of the project managers told me, it appears that the station was built by cleaning, refurbishing and strengthening the viaduct and then erecting an independent steel frame on either side to form the station.
It looks like a technique that could be used on other stations on viaducts.
It's certainly a better station with a larger capacity, than the previous one, that I used many times back in the 1960s.
The old station is to be demolished, at some point in the future.
It is an excellent station, that should serve its main function of getting supporters to and from Tottenham Hotspur's new stadium.
But will the station and the soon-to-arrive new trains have other effects.
- As I said earlier, the design could be repeated with different cladding for other stations on viaducts.
- I believe that good public transport infrastructure tends to calm crime and anti-social behaviour. Only the statistics will give a verdict.
- Will the passenger numbers rise through the station?
- Will the station and the stadium attract some better class retain premises and cafes, as the Emirates has done?
And perhaps most importantly! Tottenham Hotspur now has two new stations to serve the ground! Will this reduce the congestion caused by large crowds?
Hackney Wick Station Opens
Hackney Wick station is now almost fully rebuilt and the new facilities are open to passengers
It is a design, that draws heavily on brutalist architecture, but the general feeling is that people seem to like the station.
The station is simple in layout, with a double subway through the embankment, which was built alongside and inserted at Easter 2017, as I wrote about in A Tough Way To Spend Easter.
- One side of the subway is reserved for ticketed passengers and the other when it opens will be a new route under the railway.
- Access to the platforms on top of the embankment is by well-designed high capacity staircases and two lifts.
- The staircases have similar hand-rails with built-in LED lighting, as I saw yesterday at Abbey Wood station.
- The station does not have ticket gates and relies on passengers to just touch in, as at several stations on the London Overground.
- But then the station will be used for large sporting events at the Olympic Stadium.
- It also has wider platforms than it used, to accommodate large numbers of passengers.
There is still some work to do, like adding the signage and opening the subway for those not using the railway.
I only met one person, who didn't like the station. He struck me as an artistic type and said the money should have been spent on other things.
But Hackney Wick needed a new high-capacity station and a high-capacity walking route under the railway.
- It is the nearest station to the Olympic stadium.
- There is a large amount of housing development in the area.
Currently, the station handles two million passengers a year and this will surely rise.
A Comparison With The New White Hart Lane Station
Various sources say that the rebuilding of Hackney Wick station will cost £25million.
White Hart Lane station is also being rebuilt in conjunction with Tottenham Hotspur's new ground and redevelopment of the area.
This also needs to give access to a high railway line, which is on a brick viaduct, that can be incorporated into the structure, rather than replaced, as at Hackney Wick.
According to this article in Construction Enquirer, the budget is £18million.
Modern step-free stations to handle two million passengers per year, don't come cheap!
On the other hand, once built most stations last for at least a hundred years.
White Hart Lane opened in 1872, whereas Hackney Wick opened in 1980.
So it looks like the Victorians did a longer lasting job, than British Rail!
A Walk Around White Hart Lane Station
I took these pictures as I walked around the area between White Hart Lane station and Tottenham Hotspur's White Hart Lane stadium.
Looking at the station, I come to a few conclusions.
- It certainly isn't fit for serving a 61,000-seater football stadium.
- The access to the platforms with staircases and no lifts or escalators is terrible and not much better than it was when I used it regularly in the early 1960s.
- The platforms look like, they might be able to handle a twelve-car train.
- The platforms are on top of what looks to be a solid well-built viaduct.
- Walking away from White Hart Lane towards the South, there would appear to be few important buildings alongside the viaduct.
I think this all leads to a unique situation you don't often find in the rebuilding of a station. It would appear that if you clear the land on both sides of the railway along Penshurst Road and Love Lane, you can create a station that encloses the railway and gives access underneath. A similar situation was exploited at Haggerston and Hoxton stations to create very passenger-friendly stations.
This visualisation from the Architects Journal shows the station from the East.
I'll repeat my nearest picture.
I think that it looks good.
Note that the rightmost arch, which is partially hidden in the second picture, is the rightmost arch in the visualisation.
If you look at the other pictures in the Architects Journal, it would appear that the two staircases go up in two sections to the platforms, in a similar way to they do in several of the Overeground's rebuilt stations.
At least in common with London's two other big club grounds at Arsenal and West Ham, White Hart Lane is served by several Underground and rail stations.
This station certainly, looks like it will handle its share.
I think there could be controversy, as there have been reports that Tottenham Hotspur would like to sell naming rights to the stadium and possibly the station, as other clubs have.
Renaming the stadium would probably not be controversial, but renaming the station could well be. It will certainly be expensive, as Transport for London would have to change a large quantity of maps.
As someone, who supports Ipswich, I don't care.
The Development Of White Hart Lane Stadium And Station
This report in Construction News is entitled Mace selected as construction partner on £400m Spurs stadium job.
The report talks about the next major milestone, being the decision of Haringey Council's Planning Committee on December the 8th.
Let's hope that this drawn-out saga is at last getting near to the next phase.
This picture taken from the report, shows an architect's impression of the area after the stadium has been built.
The image is from the South and you can see the railway line through White Hart Lane and Bruce Grove stations to the left of the stadium and Tottenham High Road.
It also shows a direct approach from the stadium to the railway line, where it will connect to a new southern entrance to White Hart Lane station.
This Google Map shows the area now.
- The site acquired by Tottenham Hotspur around the current stadium is substantial and there is plenty of space to create one of the best stadia in London.
- White Hart Lane station is to the West of the stadium site on the Lea Valley Lines on the West side of Tottenham High Road. It is a walk of about two hundred metres from the stadium.
- Northumberland Park station is to the East of the stadium on another of the Lea Valley Lines. It is a walk of about five to six hundred metres from the stadium.
- Note the Sainsbury Superstore on the North Side of the already cleared site for the new stadium.
The relationship of the new stadium to White Hart Lane station, is illustrated by this enlarged Google Map of the proposed route between the two.
Note how the platforms of the current station extend to Whitehall Street, so the proposed new Southern entrance to the station, would be on a walking route to the front of the stadium. Plans on the web hint, that a wide high-capacity walking route will be provided between the station and stadium.
As to the design of the station itself, I found this image on the web.
Bruce Castle, which is a magnificent Grade 1 Listed sixteenth century manor house is on the other side of the line , in the extensive Bruce Castle Park.
So the design of open arches would link the whole area together.
As I've said many times on this blog, all stations should provide a proper entrance into their destination!
It should also be noted, that the current White Hart Lane station has platforms capable of taking the new Class 710 trains of eight or more carriages. So there should be enough capacity to get passengers to the new stadium by public transport, especially as the other side of the stadium can be walked from the slightly further away, Northumberland Park station, which if plans are carried through could be on Crossrail 2 by 2030.
If Tottenham Hotspur, with the help of Haringey Council and Transport for London, don't bring all this together to create one of the best stadiums in Europe, then they don't deserve to be successful.


text:
my account
premium content
We found 34 Something For Kate songs on chords and tabs.
At E-Chords.com you will learn how to play Something For Kate's songs easily and improve your skills on your favorite instrument as well.
Daily, we added a hundreds of new songs with chords and tabs, just for you ;).
If you still haven't found what you're looking for, please send to us.


text:
Add Document
Sign In
Create An Account
Distributing Streaming Media Content Using Cooperative ∗ Networking Venkata N. Padmanabhan, Helen J. Wang, Philip A. Chou Microsoft Research {padmanab,helenw,firstname.lastname@example.com
ABSTRACT In this paper, we discuss the problem of distributing streaming media content, both live and on-demand, to a large number of hosts in a scalable way. Our work is set in the context of the traditional client-server framework. Specifically, we consider the problem that arises when the server is overwhelmed by the volume of requests from its clients. As a solution, we propose Cooperative Networking (CoopNet), where clients cooperate to distribute content, thereby alleviating the load on the server. We discuss the proposed solution in some detail, pointing out the interesting research issues that arise, and present a preliminary evaluation using traces gathered at a busy news site during the flash crowd that occurred on September 11, 2001.
Categories and Subject Descriptors C.2 [Computer-Communication Networks]: Distributed Systems—Distributed Applications
General Terms Design, Performance, Measurement
Keywords Streaming media, content distribution networks, peer-to-peer networks, multiple description coding
There has been much work in recent years on the topic of content distribution. This work has largely fallen into two categories: (a) infrastructure-based content distribution, and (b) peer-to-peer content distribution. An infrastructure-based content distribution network (CDN) (e.g., Akamai) complements the server in the traditional client-server framework. It ∗For more information, including an extended version of this paper [13], please visit the CoopNet project Web page at http://www.research.microsoft.com/epadmanab/projects/CoopNet/. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. NOSSDAV'02, May 12-14, 2002, Miami, Florida, USA. Copyright 2002 ACM 1-58113-512-2/02/0005 ...$5.00.
Kunwadee Sripanidkulchai Carnegie Mellon University
[email protected]
employs a dedicated set of machines to store and distribute content to clients on behalf of the server. The dedicated infrastructure, including machines and networks links, is engineered to provide a high level of performance guarantees. On the other hand, peer-to-peer content distribution relies on clients to host content and distribute it to other clients. The P2P model replaces rather than complements the clientserver framework. Typically, there is no central server that holds content. Examples of P2P content distribution systems include Napster and Gnutella. In this paper, we discuss Cooperative Networking (CoopNet), an approach to content distribution that combines aspects of infrastructure-based and peer-to-peer content distribution. Our focus is on distributing streaming media content, both live and on-demand. Like infrastructure-based content distribution, we seek to complement rather than replace the traditional client-server framework. Specifically, we consider the problem that arises when the server is overwhelmed by the volume of requests from its clients. For instance, a news site may be overwhelmed because of a large "flash crowd" caused by an event of widespread interest, such as a sports event or an earthquake. A home computer that is webcasting a birthday party live to friends and family might be overwhelmed even by a small number of clients because of its limited network bandwidth. In fact, the large volume of data and the relatively high bandwidth requirement associated with streaming media content increases the likelihood of the server being overwhelmed in general. Server overload can cause significant degradation in the quality of the streaming media content received by clients. CoopNet addresses this problem by having clients cooperate with each other to distribute content, thereby alleviating the load on the server. In the case of on-demand content, clients cache audio/video clips that they viewed in the recent past. During a period of overload, the server redirects new clients to other clients that had downloaded the content previously. In the case of live streaming, the clients form a distribution tree rooted at the server. Clients that receive streaming content from the server in turn stream it out to one or more of their peers. The key distinction between CoopNet and pure P2P systems like Gnutella is that CoopNet complements rather than replaces the client-server framework of the Web. There is still a server that hosts content and (directly) serves it to clients. CoopNet is only invoked when the server is unable to handle the load imposed by clients. The presence of a central server simplifies the task of locating content. In contrast, searching for content in a pure P2P system entails an often more
expensive distributed search [17, 18, 20]. Individual clients may only participate in CoopNet for a short period of time, say just a few minutes, which is in contrast to the much longer participation times reported for systems such as Napster and Gnutella [19]. For instance, in the case of live streaming, a client may tune in for a few minutes during which time it may be willing to help distribute the content. Once the client tunes out, it may no longer be willing to participate in CoopNet. This calls for a content distribution mechanism that is robust against interruptions caused by the frequent joining and leaving of individual peers. To address this problem, CoopNet employs multiple description coding (MDC). The streaming media content, whether live or on-demand, is divided into multiple sub-streams using MDC and each sub-stream is delivered to the requesting client via a different peer. This improves robustness and also helps balance load amongst peers. The rest of this paper is organized as follows. In Section 2, we discuss related work. In Section 3, we discuss the operation of CoopNet for live and on-demand content, and present an outline of multiple description coding. In Section 4, we use traces from the flash crowd that occurred on September 11, 2001 to evaluate how well CoopNet would have performed for live and on-demand content. We present our conclusions in Section 5.
As noted in Section 1, two areas of related work are infrastructure-based CDNs and peer-to-peer systems. Infrastructurebased CDNs such as Akamai employ a dedicated network of thousands of machines in distributed locations, often with leased links inter-connecting them, to serve content on behalf of servers. When a client request arrives (be it for streaming media or other content), the CDN redirects the client to a nearby replica server. The main limitation of infrastructurebased CDNs is that their cost and scale is only appropriate for large commercial sites such as CNN and MSNBC. A second issue is that it is unclear how such a CDN would fare in the face of a large flash crowd that causes a simultaneous spike in traffic at many or all of the sites hosted by the CDN. Peer-to-peer systems such as Napster and Gnutella depend on little or no dedicated infrastructure1 . There is, however, the implicit assumption that the individual peers participate for a significant length of time (for instance, [19] reports a median session duration of about an hour both for Napster and for Gnutella). In contrast, CoopNet seeks to operate in a highly dynamic situation such as a flash crowd where an individual client may only participate for a few minutes. The disruption that this might cause is especially challenging for streaming media compared to static file downloads, which is the primary focus of Napster and Gnutella. The short lifetime of the individual nodes poses a challenge to distributed search schemes such as CAN [17], Chord [20], Pastry [18], and Tapestry [22]. Work on application-level multicast (e.g., ALMI [14], End System Multicast [3], Scattercast [2]) is directly relevant to the live streaming aspect of CoopNet. CoopNet could benefit from the efficient tree construction algorithms developed in previous work. Our focus here, however, is on using real traces to evaluate the efficacy of CoopNet. Thus we view our work as 1 Napster has central servers, but these only hold indices, not content.
complementing existing work on application-level multicast. We also consider the on-demand streaming case, which does not quite fit in the application-level multicast framework. Existing work on distributed streaming (e.g., [9]) is also directly relevant to CoopNet. A key distinction of our work is that we focus on the distruption and packet loss caused by node arrivals and departures, which is likely to be significant in a highly dynamic environment. Using traces from the September 11 flash crowd, we are able to evaluate this issue in a realistic setting. Systems such as SpreadIt [5], Allcast [23] and vTrails [24] are perhaps closest in spirit to our work. Like CoopNet, they attempt to deliver streaming content using a peer-to-peer approach. SpreadIt differs from CoopNet is a couple of ways. First, it uses only a single distribution tree and hence is vulnerable to disruptions due to node departures. Second, the tree management algorithm is such that the nodes orphaned by the departure of their parent might be bounced around between multiple potential parents before settling on a new parent. In contrast, CoopNet uses a centralized protocol (Section 3.3), which enables much quicker repairs. It is hard for us to do a specific comparison with Allcast and vTrails, in the absence of published information.
In this section, we present the details of CoopNet as it applies to the distribution of streaming media content. We first consider the live streaming case, where we discuss and analyze multiple description coding (MDC) and distribution tree management. We then turn to the on-demand streaming case.
Live Streaming
Live streaming refers to the synchronized distribution of streaming media content to one or more clients. (The content itself may either be truly live or pre-recorded.) Therefore multicast is a natural paradigm for distributing such content. Since IP multicast is not widely deployed, especially at the inter-domain level, CoopNet uses application-level multicast instead. A distribution tree rooted at the server is formed, with clients as its members. Each node in the tree transmits the received stream to each of its children using unicast. The outdegree of each node is constrained by the available outgoing bandwidth at the node. In general, the degree of the root node (i.e., the server) is likely to be much larger than that of the other nodes because the server is likely to have a much higher bandwidth than the individual client nodes. One issue is that the peers in CoopNet are far from being dedicated servers. Their ability and willingness to participate in CoopNet may fluctuate with time. For instance, a client's participation may terminate when the user tunes out of the live stream. In fact, even while the user is tuned in to the live stream, CoopNet-related activity on his/her machine may be scaled down or stopped immediately when the user initiates other, unrelated network communication. Machines can also crash or become disconnected from the network. With a single distribution tree, the departure or reduced availability of a node has a severe impact on its descendants. The descendants may receive no stream at all until the tree has been repaired. This is especially problematic because node arrivals and departures may be quite frequent in flash crowd situations. To reduce the disruption caused by node
Bit stream
Packet 1 Packet 2 Packet 3
… … … …
Packet 4 Packet M (M,M) code
Figure 1: Priority encoded packetization of a group of frames (GOF). Any m out of M packets can recover the initial Rm bits of the bit stream for the GOF. departures, we advocate having multiple distribution trees spanning a given set of nodes and transmitting a different MDC description down each tree. This would diminish the chances of a node losing the entire stream (even temporarily) because of the departure of another node. We discuss this further in Section 3.2. The distribution trees need to be constantly maintained as new clients join and existing ones leave. In Section 3.3, we advocate a centralized approach to tree management, which exploits the availability of a resourceful server node, coupled with client cooperation, to greatly simplify the problem.
Multiple Description Coding (MDC)
Multiple description coding is a method of encoding the audio and/or video signal into M > 1 separate streams, or descriptions, such that any subset of these descriptions can be received and decoded into a signal with distortion (with respect to the original signal) commensurate with the number of descriptions received; that is, the more descriptions received, the lower the distortion (i.e., the higher the quality) of the reconstructed signal. This differs from layered coding2 in that in MDC every subset of descriptions must be decodable, whereas in layered coding only a nested sequence of subsets must be decodable. For this extra flexibility, MDC incurs a modest performance penalty relative to layered coding, which in turn incurs a slight performance penalty relative to single description coding. Several multiple description coding schemes have been investigated over the years. For an overview see [6]. A particularly efficient and practical system is based on layered audio or video coding [15, 7], Reed-Solomon coding [21], priority encoded transmission [1], and optimized bit allocation [4, 16, 8]. In such a system the audio and/or video signal is partitioned into groups of frames (GOFs), each group having duration T = 1 second or so. Each GOF is then independently encoded, error protected, and packetized into M packets, as shown in Figure 1. If any m ≤ M packets are received, then the initial Rm 2 Layered coding is also known as embedded, progressive, or scalable coding.
bits of the bit stream for the GOF can be recovered, resulting in distortion D(Rm ), where 0 = R0 ≤ R1 ≤ · · · ≤ RM and consequently D(R0 ) ≥ D(R1 ) ≥ · · · ≥ D(RM ). Thus all M packets are equally important; only the number of received packets determines the reconstruction P quality of the GOF. Further, the expected distortion is M m=0 p(m)D(Rm ), where p(m) is the probability that m out of M packets are received. Given p(m) and the operational distortion-rate function D(R), this expected distortion can be minimized using a simple procedure that adjusts the rate points R1 , . . . , RM subject to a constraint on the packet length [4, 16, 8]. By sending the mth packet in each GOF to the mth description, the entire audio and/or video signal is represented by M descriptions, where each description is a sequence of packets transmitted at rate 1 packet per GOF. It is a very simple matter to generate these optimized M descriptions on the fly, assuming that the signal is already coded with a layered codec.
CoopNet Analysis: Quality During Multiple Failures
Let us consider how multiple description coding achieves robustness in CoopNet. Suppose that the server encodes its AV signal into M descriptions as described above, and transmits the descriptions down M different distribution trees, each rooted at the server. Each of the distribution trees conveys its description to all N destination hosts. Ordinarily, all N destination hosts receive all M descriptions. However, if any of the destination hosts fail (or leave the session), then all of the hosts that are descendents of the failed hosts in the mth distribution tree will not receive the mth description. The number of descriptions that a particular host will receive depends on its location in each tree relative to the failed hosts. Specifically, a host n will receive the mth description if none of its ancestors in the mth tree fail. This happens with probability (1 − ²)An , where An is the number of the host's ancestors and ² is the probability that a host fails (assuming independent failures). If hosts are placed at random sites in each tree, then the unconditional probability that any given host its mth description is the average P will receive An across all hosts in the tree. Thus θN = (1/N ) N n=1 (1 − ²) the number of descriptions that a particular host will receive is randomly distributed according to a Binomial(M, θN ) distri m θN (1 − θN )M −m . Hence for large M , bution, i.e., p(m) = M m the fraction of descriptions received is approximately Gaussian with mean θN and variance θN (1 − θN ). This can be seen in Figure 2, which shows (in bars) the distribution p(m) for various values of M = 2, 4, 8, 16 and N = 10, 1000, 100000. In the figure, to compute θN we assumed balanced binary trees with N nodes and probability of host failure ² = 1%. Note that as N grows large, performance slowly degrades, because the depth of the tree (and hence 1 − θN ) grows like log2 N . The distribution p(m) can be used to optimize the multiple description code by choosing the ratePpoints R0 , R1 , . . . , RM to minimize the expected distortion M m=0 p(m)D(Rm ) subject to a packet length constraint. Figure 2 shows (in lines), the quality associated with each p(m), measured as SNR in dB, i.e., 10 log10 (σ 2 /D(Rm )), as a function of the number of received descriptions, m = 0, 1, . . . , M . In the figure, to compute the rate points R0 , R1 , . . . , RM we assumed an operational distortion-rate function D(R) = σ 2 2−2R , which is asymptotically typical for any source with variance σ 2 , where R is expressed in bits per symbol, and we assumed a packet length constraint given as R = 8.
0 40
0 40
0 40
0 40
0 40
0 40
0 40
Figure 2: SNR in dB (line) and probabililty distribution (bars) as a function of the number of descriptions received, when the probability of host failure is ² = 1%.
CoopNet Analysis: Quality During Single Failure
The time it takes to repair the trees is called the repair time. If ² of the hosts fail during each repair time, then the average length of time that a host participates in the session is 1/² repair times. When the number of hosts is small compared to 1/², then many repair times may pass between single failures. In this case, most of the time all hosts receive all descriptions, and quality is excellent. Degradation occurs only when a single host fails. Thus, it may be preferable to optimize the MDC system by minimizing the distortion expected during the repair interval in which the single host fails, rather than minimizing the expected distortion over all time. To analyze this case, suppose that a single host fails randomly. A remaining host n will not receive the mth description if the failed host is an ancestor of host n in the mth tree. This happens with probability An /(N − 1), where An is the number of ancestors of host n. Since hosts are place at random sites in each tree, the unconditional probability that any given hostPwill receive its mth description is the average θN = (1/N ) N n=1 (1 − An /(N − 1)). Thus the number of descriptions that a particular host will receive is randomly distributed according to a Binomial(M, θN ) distribution. Equivalently, the expected number of hosts that receive m descriptions during the failure is (N − 1)p(m), where m p(m) = M θN (1−θN )M −m . This distribution can be used to m optimize the multiple description code for the failure of a single host. Figure 3 illustrates this distribution and the corresponding optimized quality as a function of the number of descriptions received, for M = 2, 4, 8, 16 and N = 10, 100, 1000. Note that as M increases, for fixed N , the distribution again becomes Gaussian. One implication of this is that the expected number of hosts that receive 100% of the descriptions decreases. However it is also the case that the expected number of hosts that receive fewer than 50% of the descriptions decreases, resulting in an increase in quality on average. Further, as N increases, for fixed M , performance becomes nearly perfect, since θN ≥ 1 − log2 N/N , which goes to 1. However, for large N , it becomes increasingly difficult to repair the trees before a second failure occurs.
Further Analyses
These same analyses can be extended to d-ary trees. It is not difficult to see that for d ≥ 2, a d-ary trees with
0 40
0 40
0 40
0 40
0 40
0 40
0 40
0 40
Figure 3: SNR in dB (line) and probabililty distribution (bars) as a function of the number of descriptions received during the failure of a single host. N log2 d ≥ N nodes has the same height, and hence the same performance, as a binary tree with only N nodes. Thus when each node has a large out-degree, i.e., when each host has a large uplink bandwidth, much larger populations can be handled. Interestingly, the analysis also applies when d = 1. So, if each host can devote only as much uplink bandwidth as its downlink video bandwidth (which is typically the case for modem users), then the descriptions can still be distributed peer-to-peer by arranging the hosts in a chain, like a bucket brigade. It can be shown that when the order of the hosts in the chain is random and independent for each description, then for a single failure the number of hosts receiving m out of M descriptions is binomially distributed with parameters M and θN , where θN = (N + 1)/2N . Although this holds for any N , it is most suitable for smaller N . For larger N , it may not be possible to repair the chains before other failures occur. In fact, as N goes to infinity, the probability that any host receives any descriptions goes to zero. In this section we have proposed optimizing the MDC system to the unconditional distribution p(m) derived by averaging over trees and hosts. Given any set of trees, however, the distribution of the number of received descriptions varies widely across the set of hosts as a function of their upstream connectivity. By optimizing the MDC system to the unconditional distribution p(m), we are not minimizing the expected distortion for any given host, but rather minimizing the sum of the expected distortions across all hosts, or equivalently, minimizing the expected sum of the distortions over all hosts.
Tree Management
We now discuss the problem of constructing and maintaining the distribution trees in the face of frequent node arrivals and departures. There are many (sometimes conflicting) goals for the tree management algorithm: 1. Short and wide tree: The trees should be as short as possible so as to minimize the latency of the path from the root to the deepest leaf node and to minimize the probability of disruption due to the departure of an ancestor node. For it to be short, the tree should be balanced and as wide as possible, i.e., the out-degree of each node should be as much as its bandwidth will allow. However, making the out-degree large may leave little bandwidth for non-CoopNet (and higher priority) traffic emanating from the node. Interference due to
such traffic could cause a high packet loss rate for the CoopNet streams. 2. Efficiency versus tree diversity: The distribution trees should be efficient in that their structure should closely reflect the underlying network topology. So, for instance, if we wish to connect three nodes, one each located in New York (NY), San Francisco (SF), and Los Angeles (LA), the structure NY→SF→LA would likely be far more efficient than SF→NY→LA (→ denotes a parent-child relationship). However, striving for efficiency may interfere with the equally important goal of having diverse distribution trees. The effectiveness of MDC-based distribution scheme described in Section 3.2 depends critically on the diversity of the distribution trees. 3. Quick join and leave: The processing of node joins and leaves should be quick. This would ensure that the interested nodes would receive the streaming content as quickly as possible (in the case of a join) and with minimal interruption (in the case of a leave). However, the quick processing of joins and leaves may interfere with the efficiency and balanced tree goals listed above. 4. Scalability: The tree management algorithm should scale to a large number of nodes, with a correspondingly high rate of node arrivals and departures. For instance, in the extreme case of the flash crowd at MSNBC on September 11, the average rate of node arrivals and departurtes was 180 per second while the peak rate was about 1000 per second. With these requirements in mind, we now describe our approach to tree construction and management. We first describe the basic protocol and then discuss optimizations.
Basic Protocol
We exploit the presence of a resourceful server node to build a simple and efficient protocol to process node joins and leaves. While it is centralized, we argue that this protocol can scale to work well in the face of extreme flash crowd situations such as the one that occurred on September 11. Despite the flash crowd, the server is not overloaded since the burden of distributing content is shared by all peers. Centralization also simplifies the protocol greatly, and consequently makes joins and leaves quick. In general, a criticism of centralization is that it introduces a single point of failure. However, in the context of CoopNet, the point of centralization is the server, which is also the source of data. If the source (server) fails, it may not really matter that the tree management also breaks down. Also, recall from Section 1 that the goal of CoopNet is to complement, not replace, the client-server system. The server has full knowledge of the topology of all of the distribution trees. When a new node wishes to join the system, it first contacts the server. The new node also informs the server of its available network bandwidth to serve furture downstream nodes. The server responds with a list of designated parent nodes, one per distribution tree. The designated parent node in each tree is chosen as follows. Starting at the server, we work our way down the tree until we get to a level where there are one or more nodes that have the necessary spare capacity (primarily network bandwidth) to serve as the parent of the new node. (The server could itself be the new
parent if it has sufficient spare capacity, which it is likely to have during the early stages of tree construction.) The server then picks one such node at random to be the designated parent of the new node. This top-down procedure ensures a short and largely balanced tree. The randomization helps make the trees diverse. Upon receiving the server's message, the new node sends (concurrent) messages to the designated parent nodes to get linked up as a child in each distribution tree. In terms of messaging costs, the server receives one message and sends one. Each designated parent receives one message and sends one (an acknowledgement). The new node sends and receives M + 1 messages, where M is the number of MDC descriptions (and hence distribution trees) used. Node departures are of two kinds: graceful departures and node failures. In the former case, the departing node informs the server of its intention to leave. For each distribution tree, the server identifies the children of the departing node and executes a join operation on each child (and implicitly the subtree rooted at the child) using the top-down procedure describedPabove. The messaging cost for the server would at P most be i di sends and i di receives, where di is the number of children of the departing node in the ith distribution tree. (Note that the cost would be somewhat lower in general because a few of the children may be in common across multiple trees.) Each child sends and receives M + 1 messages. To reduce its messaging load, the server could make the determination of the designated parent for each child in each tree and then leave it to another node (such as the departing node, if it is still available) to convey the information to each child. In this case, the server would have to send and receive just one message. A node failure corresponds to the case where the departing node leaves suddenly and is unable to notify either the server or any other node of its departure. This may happen because of a computer crashing, being turned off, or becoming disconnected from the network. We present a general approach for dealing with quality degradation due to packet loss; node failure is a special case where the packet loss rate experienced by the descendants of the failed node is 100%. Each node monitors the packet loss rate it is experiencing in each distribution tree. When the packet loss rate reaches an unacceptable level (a threshold that needs to be fine-tuned based on further research), a node contacts its parent to check if the parent is experiencing the same problem. If so, the source of the problem (network congestion, node failure, etc.) is upstream of the parent and the node leaves it to the parent to deal with it. (The node also sets a sufficiently long timer to take action on its own in case its parent has not resolved the problem within a reasonable period of time.) If the parent is not experiencing a problem or it does not respond, the affected node will contact the server and execute a fresh join operation for it (and its subtree) to be moved to a new location in the distribution tree.
We now discuss how to make the distribution trees efficient, as discussed above. The basic idea here is to preferentially attach a new node as the child of an existing node that is "nearby" in terms of network distance (i.e., latency). The definition of "nearby" needs to be broad enough to accomodate significant tree diversity. When trying to insert a new node, the server first identifies a (sufficiently large) subset of nodes that are close to the new node. Then using the random-
ized top-down procedure discussed in Section 3.3.1, it tries to find a parent for the new node (in each tree) among the set of nearby nodes. Using this procedure, it is quite likely that many of the parents of the new node (on the the various distribution trees) will be in the same vicinity, which is beneficial from an efficiency viewpoint. We argue that this also provides sufficient diversity since the primary failure mode we are concerned with is node departures and node failures. So it does not matter much that all of the parents may be located in the same vicinity (e.g., same metropolitan area). To determine the network distance between two nodes, we use a technique previously proposed in [12], [17], and [10]. Each node determines its network "coodinates" by measuring the network latency to a set of landmark hosts. The server keeps track of the coordinates of all nodes currently in the system and determines whether two nodes are proximate by comparing their coordinates.
• CPU: Node departure involves finding a new set of parents for each child of the departing node. So the CPU cost is roughly equal to the number of children of the departing node times the cost of node insertion. To insert a node, the server has to scan the tree levels starting with the root until it reaches a level containing one or more nodes with the spare capacity to support a new child. The server picks one such node at random to be the new parent. Using a simple array data structure to keep track of the nodes in each level of the tree that have free capacity, the cost of picking a parent at random can be made (a small) constant. Since the number of levels in the tree is about log(N ), where N is the number of nodes in the system, the node insertion cost (per tree) is O(log(N )). (With N = 18, 000 and an average of 4 children per node, the depth of the tree will be about 9.) A departure rate of 1000 per second would result in 64,000 insertions per second (1000 departures times 4 children per departing node times 16 trees). Given that memory speed by far lags CPU speed, we only focus on how many memory lookups we can do per insertion. Assuming a 40 ns memory cycle, we are allowed about 390 memory accesses per insertion, which is likely to be more than sufficient.
Feasibility of the Centralized Protocol
The main question regarding the feasibility of the centralized tree management protocol is whether the server can keep up. To answer this question, we consider the September 11 flash crowd at MSNBC, arguably an extreme flash crowd situation. At its peak, there were 18,000 nodes in the system and the rate of node arrivals and departures was 1000 per second.3 (The average numbers were 10000 nodes and 180 arrivals and departures per second.) In our calculations here, we assume that the number of distribution trees (i.e., the number of MDC descriptions) is 16 and that on average a node has 4 children in a tree. We consider various resources that could become a bottleneck at the server (we only focus on the impact of tree management on the server): • Memory: To store the entire topology of one tree in memory, the server would need to store as many pointers as nodes in the system. Assuming a pointer size of 8 bytes (i.e., a 64-bit machine) and auxiliary data of 24 bytes per node, the memory requirement would be about 576 KB. Since there are 16 trees, the memory requirement for all trees would be 9.2 MB. In addition, for each node the server needs to store its network coordinates. Assuming this is a 10-dimensional vector of delay values (2 bytes each), the additional memory requirement would be 360 KB. So the total memory requirement at the server would be under 10 MB, which is a trivial amount for any modern machine. • Network bandwidth: Node departures are more expensive than node arrivals, so we focus on departures. The server needs to designate a new parent in each distribution tree for each child of the departing node. Assuming that nodes are identified by their IP addresses (16 bytes assuming IPv6) and that there are 4 children per tree on average, the total amount of data that the server would need to send out is 1 KB. If there are 1000 departures per second, the bandwidth requirement would be 8 Mbps. This is likely to be a small fraction of the network bandwidth at a large server site such as MSNBC. 3 One reason for the high rate of churn may be that users were discouraged by the degradation in audio/video quality caused by the flash crowd, and so did not stay for long. However, we are not in a position to confirm that this was the case.
In general, the centralized approach can be scaled up (at least in terms of CPU and memory resources) by having a cluster of servers and partitioning the set of clients across the set of server nodes. We are in the process of benchmarking our implementation to confirm the rough calculations made above.
On-demand Streaming
We now turn to on-demand streaming, which refers to the distribution of pre-recorded streaming media content on demand (e.g., when a user clicks on the corresponding link). As such, the streams corresponding to different users are not synchronized. When the server receives such a request, it starts streaming data in response if its current load condition permits. However, if the server is overloaded, say because of a flash crowd, it instead sends back a response including a short list of IP addresses of clients (peers) who have downloaded (part or all of) the requested stream and have expressed a willingness to participate in CoopNet. The requesting client then turns to one or more of these peers to download the desired content. Given the large volume of streaming media content, the burden on the server (in terms of CPU, disk, and network bandwidth) of doing this redirection is quite minimal compared to that of actually serving the content. So we believe that this redirection procedure will help reduce server load by several orders of magnitude. While the procedure described above is similar to one that might apply to static file content, there are a couple of important differences arising from the streaming nature of the content. First, a peer may only have a part of the requested content because, for instance, the user may have stopped the stream halfway or skipped over portions. So in its initial handshake with a peer, a client finds out which part of the requested content is available at the peer and accordingly plans to make requests to other peers for the missing content, if any. A second issue is that, as with the live streaming case, peers may fail, depart, or scale back their participation in CoopNet
at any time. In contrast with file download, the time-sensitive nature of streaming media content makes it especially susceptible to such disruptions. As a solution, we propose the use of distributed streaming where a stream is divided into a number of substreams, each of which may be served by a different peer. Each substream corresponds to a description created using MDC (Section 3.2). Distributed streaming improves robustness to disruptions caused by the untimely departure of peer nodes and/or network connectivity problems with respect to one or more peers. It also helps distribute load more evenly among peers.
We now present a performance evaluation of CoopNet based on simulations driven by traces of live and on-demand content served by MSNBC on September 11, 2001.
Live Streaming
We evaluate the MDC-based live streaming design using traces of a 100kbps live stream. The trace started at 18:25 GMT (14:25 EST) and lasted for more than one hour (4000 seconds).
Trace Characteristics
Figure 4 shows the time series of the number of clients simultaneously tuned in to the live stream. The peak number of simultaneous clients exceeds 17,000. On average, there are 84 clients departing every second. (We are unable to definitely explain the dip around the 1000-seond mark, but it is possibly due to a glitch in the logging process.) Over 70% of the clients remain tuned in to the live stream for less than a minute. We suspect that the short lifetimes could be because users were frustrated by the poor quality the video stream during the flash crowd. If the quality were improved (say using CoopNet to relieve the server), client lifetimes may well become longer. This, in turn, would increase the effectiveness of CoopNet. 20000
Node Arrivals and Departures
18000 16000
# of Nodes
14000 12000 10000 8000 6000 4000 2000 0 0
Time (seconds)
Figure 4: Number of clients and departures.
Effectiveness of MDC
We evaluate the impact of MDC-based distribution (Section 3.2) on the quality of the stream received by clients in the face of client departures. When there are no departures, all clients receive all of the MDC descriptions and hence perceive the full quality of the live stream. We have conducted two simulation experiments. In the first experiment, we construct completely random distribu-
100% 98.1 94.80 89.54 82.07 71.26
[87.5,100) 0 0 0 14.02 25.11
[75,87.5) 0 0 9.24 3.19 3.26
[50,75) 0 5.05 1.13 0.70 0.37
[25,50) 0 0 0.09 0.016 0.002
0 1.90 0.16 0.005 0 0
Table 1: Random Tree Experiment: probability distribution of descriptions received vs. number of distribution trees tion trees at the end of the repair interval following a client departure. We then analyze the stream quality received by the remaining clients. The random trees are likely to be diverse (i.e., uncorrelated), which improves the effectiveness of MDC-based distribution. In the second experiment, we simulate the tree management algorithm described in Section 3.3. Thus the distribution trees are evolved based on the node arrivals and departures recorded in the trace. We compare the results of these two experiments at the end of the section. In more detail, we conducted the random tree experiment as follows. For each repair interval, we construct M distribution trees (corresponding to the M descriptions of the MDC coder) spanning the N nodes in the system at the beginning of the interval. Based on the number of departing clients, d, recorded through the end of the repair interval, we randomly remove d nodes from the tree, and compute the number of descriptions received by the remaining nodes. The perceived quality of the stream at a client is determined by the fraction of descriptions received by that client. The set of distribution trees is characterized by three parameters: the number of trees (or, equivalently, descriptions), the maximum outdegree of nodes in each tree, and the out-degree of the root (i.e., the live streaming server). The out-degree of a node is typically a function of its bandwidth capacity. So the root (i.e., the server) tends to have a much larger out-degree than bandwidth-constrained clients. In our random tree construction, each client is assigned a random degree subject to a maximum. We varied the degree of the root and the number of descriptions to study their impact on received stream quality. We set the repair time to 1 second; we investigate the impact of repair time in Section 4.1.3. Table 1 shows how the number of distribution trees, M , affects the fraction of descriptions received (expressed as a percentage, P ). We compute the distribution of P by averaged across all client departures. We set the maximum out-degree of a client to 4 and the root degree to 100. We vary the number of descriptions among 1, 2, 4, 8, or 16. Each column represents a range of values of P . For each pair of the range and number of descriptions, we list the average percentage of clients that receive at that level of quality. For example, the first table entry indicates that when using 2 descriptions, 94.80% of clients receive 100% of the descriptions (i.e., both the descriptions). As the number of descriptions increases, the percentage of clients that receive the all of the descriptions (i.e., P = 100%) decreases. Nonetheless, the percentage of clients corresponding to small values of P decreases dramatically. With 8 descriptions, 96% (82.07% + 14.02%) of clients receive more than 87.5% of the descriptions. For both 8 and 16 descriptions, all clients receive at least one description. Figure 5 shows the corresponding SNR. Figure 6 compares the SNR over time for the 16-description case and the single descrip-
Figure 5: Random Tree Experiment: SNR in dB (line) and probabililty distribution (bars) as a function of the number of descriptions received
Random Trees
Average fraction of descriptions received
Quality (SNR in dB)
35 30 25 20 15 10 Multiple Descriptions (M=16) Single Description (M=1) 0
1500 2000 2500 Time (seconds)
1 sec
Figure 7: Evolving Tree Experiment: SNR in dB (line) and probabililty distribution (bars) as a function of the number of descriptions received
3 sec
6 sec
10 sec
1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0
Time (seconds)
Figure 6: Random Tree Experiment: The SNR over time for the MDC and SDC cases. At each time instant, we compute the average SNR over all clients. tion (SDC) case. MDC demonstrates a clear advantage over SDC. In our second experiment, we evolved the distribution trees by simulating the tree management algorithm from Section 3.3. We set the root (i.e., server) out-degree to 100. The maximum out-degree of a client is set to 4. Table 2 shows the probability distribution of the descriptions received upon client departures. Figure 7 shows the corresponding SNR. The results are comparable to those of the random tree experiment. This suggests that our tree management algorithm is able to preserve significant tree diversity even over a long period of time (more than an hour in this case).
100% 98.34 96.5 93.3 87.14 77.26
[87.5,100) 0 0 0 11.34 21.62
[75,87.5) 0 0 6.31 1.29 0.99
Figure 8 shows the average number of descriptions received as a function of time for four different settings of repair time: 1, 3, 6, and 10 seconds. With a repair time of 1 second, clients would receive 90% of the descriptions on average. With a 10 second repair time, the fraction drops to 30%. We believe that these results are encouraging since in practice tree repair can be done very quickly, especially given that our tree management algorithm is centralized (Section 3.1). Even a 1-second repair interval would permit multiple round-trips between the server and the nodes affected by the repair (e.g., the children of the departed node).
4.2 On-Demand Streaming
Impact of Repair Time
Finally, we evaluate the impact of the time it takes to repair the tree following a node departure. Clearly, the longer the repair time, the greater is the impact on the affected nodes. Also, a longer repair time increase the chances of other nodes departing before the repair is completed, thereby causing further disruption. We divide time into non-overlapping repair intervals and assume that all leaves happen at the beginning of each interval. We then compute fraction of descriptions received averaged over all nodes (this is the quantity θN discussed in Section 3.2). As in Section 3.2, assume a balanced binary tree at all times. M 1 2 4 8 16
Figure 8: The average fraction of descriptions received for various repair times.
[50,75) 0 3.42 0.36 0.20 0.11
[25,50) 0 0 0.03 0.02 0.01
0 1.66 0.08 0 0 0
Table 2: Evolving Tree Experiment: probability distribution of descriptions received vs. number of distribution trees
We now evaluate the potential of CoopNet in the case of on-demand streaming. The goals of our evaluation are to study the effects of client cooperation on load reduction at the server and additional load incurred by cooperating peers. The cooperation protocol used in our simulations is based on server redirection as in [11]. The server maintains a fixedsize list of IP addresses (per URL) of CoopNet clients that have recently contacted it. To get content, a client initially sends a request to the server. If the client is willing to cooperate, the server redirects the request by returning a short list of IP addresses of other CoopNet clients who have recently requested the same file. In turn, the client contacts these other CoopNet peers and arranges to retrieve the content directly from them. Each peer may have a different portion of the file, so it may be necessary to contact multiple peers for content. In order to select a peer (or a set of peers when using distributed streaming) to download content from, peers run a greedy algorithm that picks out the peer(s) with the longest portion of the file from the list returned by the server. If a client cannot retrieve content through any peer, it retrieves the entire content from the server. Note that the server only provides the means for discovering other CoopNet peers. Peers independently decide who they cooperate with. The server maintains a list of 100 IP addresses per URL, and
Server Server (CoopNet) Clients (CoopNet) 8
Average Bandwidth (bps)
10 13:00
14:00 Time of Day
(a) Average bandwidth at server and cooperating peers. 4
x 10
Average Bandwidth of CoopNet Clients (bps)
5 6 Degree of Parallelism
(b) Average bandwidth at peers when using distributed streaming. 1
1 Stream 5 Streams 10 Streams 1 Stream (Least Loaded)
Cumulative Distribution
0 −2 10
10 10 Bandwidth of Active Peers (bps)
(c) Distribution of bandwidth at active peers.
Figure 9: Performance of CoopNet for on-demand streaming. returns a list of 10 IP addresses in the redirection messages in our simulations. We use traces collected at MSNBC during the flash crowd of Sep 11, 2001 for our evaluation. The flash crowd started at around 1:00 pm GMT (9:00 am EDT) and persisted for the rest of the day. The peak request rate was three orders of magnitudes more than the average. We report simulation results for the beginning of the flash crowd, between 1:00 pm to 3:00 pm GMT. There were over 300,000 requests during the 2-hour period. However, only 6% or 18,000 requests were successfully served at an average rate of 20 Mbps with a mean session duration of 20 minutes. Unsuccessful requests were not used in the analysis because of the lack of content byterange and session duration information.
Bandwidth Load
In our evaluation, load is measured as bandwidth usage. We do not model available bandwidth between peers. We assume that peers can support the full bit rate (56 kbps, 100
kbps) of each encoded stream. We also do not place a bound on the number of concurrent connections at each peer. In practice, finding peers with sufficient available bandwidth and not overloading any one peer are important considerations, and we are investigating these issues in ongoing work. Figure 9(a) depicts the bandwidth usage during the 2-hour period for two systems: the traditional client-server system, and the CoopNet system. The vertical axis is average bandwidth and the horizontal axis is time. There are two peaks at around 1:40 pm and 2:10 pm, when two new streams were added to the server. In the client-server system, the server was distributing content at an average of 20 Mbps. However, client cooperation can reduce that bandwidth by orders of magnitude to an average of 300 kbps. As a result, the server is available to serve more client requests. The average bandwidth contribution that CoopNet clients need to make to the system is 45 kbps. Although the average bandwidth contribution is reasonably small, peers are not actively serving content all the time. We find that typically less than 10% of peers are active at any second. The average bandwidth contribution that active CoopNet peers need to make to the system is as high as 465 kbps, where average bandwidth of active peers is computed as the total number of bits served over the total length of peers' active periods. To further reduce load at individual CoopNet clients, disjoint portions of the content can be retrieved in parallel from multiple peers using distributed streaming (Section 3.4). (The bandwidth requirement placed on each peer is correspondingly reduced.) Figure 9(b) depicts the average bandwidth contributed versus the degree of parallelism. The degree of parallelism is an upper-bound on the number of peers that can be used in parallel. For example, clients can retrieve content from up to 5 peers in parallel in a simulation with a degree of parallelism of 5. The actual number of peers used in parallel may be less than 5 depending on how many peers can provide content in the byte-range needed by the client. The load at each active peer is reduced as the degree of parallelism increases. When the degree of parallelism is 5, peers are serving content at only 35 kbps. However, the bandwidth of active peers (not depicted in this figure) is only slightly reduced to 400 kbps. This is because the large amount of bandwidth required to serve content during the two surges at 1:40 pm and 2:10 pm influence the average bandwidth. The cumulative distribution of bandwidth contributed by active CoopNet peers, depicted in Figure 9(c), illustrates the impact of distributed streaming on bandwidth utilization. Each solid line represents the amount of bandwidth peers contribute when using 1, 5, and 10 degrees of parallelism. The median bandwidth requirement is 390 kbps when content is streamed from one peer, and only 66 bps for 10 degrees of parallelism. The bandwidth requirement imposed on each peer is reduced as the degree of parallelism increases. Although this reduction is significant, a small portion of peers still contribute more than 1 Mbps even when using 10 degrees of parallelism. We believe that the combination of the following two factors contribute to the wide range in bandwidth usage: the greedy algorithm a client uses to select peers and the algorithm the server uses to select a set of IP addresses to give to clients. For better load distribution, the server can run a load-aware algorithm that redirects clients to recently seen peers that are the least loaded (in terms of network bandwidth usage). In order to implement this algorithm, the server needs to know the load at individual peers. Therefore, peers constantly re-
port their current load status to the server. We use a report interval of once every second in our simulations. Because the server caches a fixed-size list of IP addresses, only those peers currently in the server's list need to send status updates. Given this information, the server then selects the 10 least loaded peers that have recently accessed the same URL as the requesting client to return in its redirection message. This algorithm replaces the one described earlier in this section where the server redirects clients to peers that were recently seen. Clients, however, use the same greedy algorithm to select peers. We find that using this new algorithm, active clients serve content at 385 kbps. The dashed line in Figure 9(c) depicts the cumulative distribution of bandwidth contributed by CoopNet clients when the load-aware algorithm is used at the server. In this simulation, clients stream content from at most one other peer (degree of parallelism of 1). For the most part, the distribution is similar to the one observed when the server redirects the request to recently seen peers. The difference lies in the tail end of the distribution. About 6% of peers contributed more than 500 kbps of bandwidth when the server runs the original algorithm, compared to only 2% when the server runs the load-aware algorithm. In addition, the total number of active peers in the system doubles when the load-aware algorithm is used. We find that client cooperation significantly reduces server load, freeing up bandwidth to support more client connections. In addition, the combination of distributed streaming and a load-aware algorithm used by the server further reduces the load on individual peers.
5. CONCLUSIONS In this paper, we have presented CoopNet, a peer-to-peer content distribution scheme that helps servers tide over crisis situations such as flash crowds. We have focussed on the application of CoopNet to the distribution of streaming median content, both live and on-demand. One challenge is that clients may not participate in CoopNet for an extended length of time. CoopNet employs distributed streaming and multiple description coding to improve the robustness of the distributed streaming content in face of client departures. We have evaluated the feasibility and potential performance of CoopNet using traces gathered at MSNBC during the flash crowd that occurred on September 11, 2001. This was an extreme event even by flash crowd standards, so using these traces helps us stress test the CoopNet design. Our results suggest that CoopNet is able to reduce server load significantly without placing an unreasonable burden on clients. For live streams, using multiple independent distribution trees coupled with MDC improves robustness significantly. We are currently building a prototype implementation of CoopNet for streaming media distribution. We are also investigating distributed algorithms for tree management.
Acknowledgements We are grateful to Steven Lautenschlager, Ted McConville, and Dave Roth for providing us the MSNBC streaming media logs from September 11. We would also like to thank the anonymous NOSSDAV reviewers for their feedback.
[1] A. Albanese, J. Bl¨ omer, J. Edmonds, M. Luby, and M. Sudan. Priority encoding transmission. IEEE Trans. Information Theory, 42:1737–1744, November 1996.
[2] Y. Chawathe. "Scattercast: An Architecture for Internet Broadcast Distribution as an Infrastructure Service", Ph.D. Dissertation, University of California at Berkeley, Dec 2000. [3] Y. Chu, S. G. Rao, S. Seshan, and H. Zhang. "Enabling Conferencing Applications on the Internet Using an Overlay Multicast Architecture", ACM SIGCOMM, August 2001. [4] G. Davis and J. Danskin. Joint source and channel coding for image transmission over lossy packet networks. In Conf. Wavelet Applications to Digital Image Processing, Denver, CO, August 1996. SPIE. [5] H. Deshpande, M. Bawa, and H. Garcia-Molina. "Streaming Live Media over a Peer-to-Peer Network", Technical Report, Stanford University, August 2001. http://dbpubs.stanford.edu:8090/pub/2001-31 [6] V. K. Goyal. Multiple description coding: Compression meets the network. IEEE Signal Processing Magazine, pages 74–93, September 2001. [7] Z. Lu and W. A. Pearlman. An efficient, low-complexity audio coder delivering multiple levels of quality for interactive applications. In Proc. Workshop on Multimedia Signal Processing, pages 529–534, Redondo Beach, CA, December 1998. IEEE. [8] A. E. Mohr, E. A. Riskin, and R. E. Ladner. Unequal loss protection: Graceful degradation of image quality over packet erasure channels through forward error correction. IEEE J. Selected Areas in Communications, 18(6):819–829, June 2000. [9] T. P. Nguyen and A. Zakhor. "Distributed Video Streaming over the Internet", Multimedia Computing and Networking, January 2002. [10] T. S. E. Ng and H. Zhang. "Towards Global Network Positioning", ACM SIGCOMM Internet Measurement Workshop, November 2001. [11] V. N. Padmanabhan and K. Sripanidkulchai. "The Case for Cooperative Networking", IPTPS, March 2002. [12] V. N. Padmanabhan and L. Subramanian. "An Investigation of Geographic Mapping Techniques for Internet Hosts", ACM SIGCOMM, August 2001. [13] V. N. Padmanabhan, H. J. Wang, P. A. Chou, and K. Sripanidkulchai. "Distributing Streaming Media Content Using Cooperative Networking", Microsoft Research Technical Report, MSR-TR-2002-37, April 2002. [14] D. Pendarkis, S. Shi, D. Verma, and M. Waldvogel. "ALMI: An Application Level Multicast Infrastructure", USITS, March 2001. [15] W. A. Pearlman, B.-J. Kim, and Z. Xiong. Embedded video subband coding with 3D SPIHT. In P. Topiwala, editor, Wavelet Image and Video Compression. Kluwer, 1998. [16] R. Puri and K. Ramchandran. Multiple description source coding through forward error correction codes. In Proc. Asilomar Conference on Signals, Systems, and Computers, Asilomar, CA, October 1999. IEEE. [17] S. Ratnasamy, P. Francis, M. Handley, R. Karp, and S. Shenker. "A Scalable Content-Addressable Network", ACM SIGCOMM, August 2001. [18] A. Rowstron and P. Druschel. "Storage Management and Caching in PAST, A Large-scale, Persistent Peer-to-peer Storage Utility", ACM SOSP, October 2001. [19] S. Saroiu, P. K. Gummadi, and S. D. Gribble. "A Measurement Study of Peer-to-Peer File Sharing Systems", Multimedia Computing and Networking, January 2002. [20] I. Stoica, R. Morris, D. Karger, F. Kaashoek, and H. Balakrishnan. "Chord: A Scalable Peer-To-Peer Lookup Service for Internet Applications", ACM SIGCOMM, August 2001. [21] S. B. Wicker. Error Control Systems for Digital Communication and Storage. Prentice Hall, 1995. [22] B. Y. Zhao, J. Kubiatowicz, and A. D. Joseph. "Tapestry: An Infrastructure for Fault-Resilient Wide-Area Location and Routing", Technical Report UCB/CSD-01-1141, University of California at Berkeley, April 2001. [23] Allcast. http://www.allcast.com/ [24] vTrails. http://www.vtrails.com/
On Distributing Symmetric Streaming Computations
On Distributing Symmetric Streaming Computations
On Distributing Symmetric Streaming ... - Research at Google
Multirate Media Streaming Using Network Coding
Stopword Detection for Streaming Content
Scalable Media Streaming to Interactive Users - CiteSeerX
Social Media Content Company, CA.pdf
Social Media & Branded Content Specialist - CareerBuilder.pdf ...
Predicting memorability of media content -
HOW CREATE EFFECTIVE SOCIAL MEDIA CONTENT - digital ...
Content Fingerprinting Using Wavelets - Research at Google
Network and Content Adaptive Streaming of ... - Semantic Scholar
[PDF BOOK] Fire TV | Streaming Media Player - Full ...
Media Streaming Observations: Trends in UDP to TCP ...
Distributing Streaming Media Content Using ...
Sep 11, 2001 -
In contrast, search- ing for content in a pure P2P
entails an often more ... streaming media compared to static
downloads, which is the primary focus of .....
of ânearbyâ needs to be broad enough to accomo-.
Download PDF
539KB Sizes
3 Downloads
97 Views
Recommend Documents
On Distributing Symmetric Streaming Computations
On Distributing Symmetric Streaming Computations
using distributed computation has numerous challenges in- ... by these systems. We show that in principle, mud algo- ... algorithm can also be computed by a mud algorithm, with comparable space ... algorithms. Distributed systems such as.
On Distributing Symmetric Streaming ... - Research at Google
Multirate Media Streaming Using Network Coding
Stopword Detection for Streaming Content
Scalable Media Streaming to Interactive Users - CiteSeerX
Computer Science Department. Federal ... a high degree of interactivity has been observed in the ac- cesses to ... width Skimming and Patching, two state-of-the-art stream- ..... lyzed a three-year log of accesses to MANIC, an educational.
Social Media Content Company, CA.pdf
Social Media & Branded Content Specialist - CareerBuilder.pdf ...
Social Media & Branded Content Specialist - CareerBuilder.pdf. Social Media & Branded Content Specialist - CareerBuilder.pdf. Open. Extract. Open with.
Predicting memorability of media content -
HOW CREATE EFFECTIVE SOCIAL MEDIA CONTENT - digital ...
Page 1 of 4 ... on social media. Page 2 of 4 ... 3 of 4. HOW CREATE EFFECTIVE SOCIAL MEDIA CONTENT - digital marketing course in hyd - Digitalnest.pdf.
signed value of one is denoted by XE = {Xj1 , ..., XjE } and is called the ... XE is in practice much smaller than. XH. .... Fourteen trademark examiners in the Intel-.
Content Fingerprinting Using Wavelets - Research at Google
Abstract. In this paper, we introduce Waveprint, a novel method for ..... The simplest way to combine evidence is a simple voting scheme that .... (from this point on, we shall call the system with these ..... Conference on Very Large Data Bases,.
Network and Content Adaptive Streaming of ... - Semantic Scholar
[PDF BOOK] Fire TV | Streaming Media Player - Full ...
Media Streaming Observations: Trends in UDP to TCP ...
[15] [21] find that the majority of traffic and infrastructures have migrated to a small number of very large providers, such as those supporting cloud computing.
Report Distributing Streaming Media Content Using ...
Your name
-Select Reason-
Other Terms Of Service Violation
File a copyright complaint
Save changes
Sign In
Remember Password
Forgot Password?
Sign In