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Comprehensive Description
Dogtooth tuna (Gymnosarda unicolor) are found in the Indo West Pacific, from Australia (the Great Barrier Reef) to East Africa and the Red Sea, and in the waters off of the coast of Japan and the Philippines, New Guinea, Marquesas, Tahiti, Tuamotus, Pitcairn, and Oeno Islands.
Biogeographic Regions: oceanic islands (Native ); indian ocean (Native ); pacific ocean (Native )
- Fishbase, 2007. "Fishbase" (On-line). Accessed Sept. 21, 2000 at http://www.fishbase.org/Summary/SpeciesSummary.cfm?genusname=Gymnosarda&speciesname=unicolor.
Range Description
Physical Description
Dogtooth tuna are members of the mackerel family. Distinguishing features include a streamlined body with a large head and a mouth that contains twenty sharp dog-like teeth per jaw. They have two dorsal fins; the first is spiny and large, and the second, right behind it is soft-rayed. The ventral fin is similar in size and shaped like the second dorsal. Nine spiny finlets stretch down the upper and lower tail section toward its crescent shaped tailfin. This species exhibits counter shading and has no scales. The dorsal surface is blue green, the sides are silver, and the belly is white. They swim constantly with their mouth open to force water through the gills because of a high oxygen requirement and great muscular activity. An unusual vessel system in the liver and tail provides counter-current temperature exchange, raising the body temperature 6°C to 12°C higher than the water temperature. They can reach speeds of up to 80 kph (50 mph). Dogtooth tuna's average weight is 15 to 20 kg (33 to 44 lbs). The spear fishing record is 55 kg and the all-tackle record is 131kg (288 lbs).
Range mass: 131 (high) kg.
Average mass: 15-20 kg.
Other Physical Features: endothermic ; ectothermic ; homoiothermic; bilateral symmetry
Sexual Dimorphism: sexes alike
- Grizimek, B., W. Ladiges. 1974. Fishes 2. New York, New York: Van Nostrand Reinhold LTD.
- Maas, T. 1997. "BlueWater Hunting and Freediving" (On-line). Accessed Sept 16, 2000 at http://www.freedive.net/chapters/dogtooth8.html.
Max. size
Diagnostic Description
Dogtooth tuna are typically pelagic, but are known to come inshore and are found around coral reefs and atolls at depths from 15m (50ft) to 45m (150ft). They prefer water temperatures between 21°C (70°F) and 26°C (80°F). They are migratory; their movements are linked to water temperatures and the fish they feed upon.
Habitat Regions: tropical ; saltwater or marine
Aquatic Biomes: pelagic ; reef ; coastal
Habitat and Ecology
Size at first maturity is 65 cm fork length (FL) (Lewis et al. 1983). Maximum Size is 247 cm FL. The all-tackle angling record is of a 104.32 kg fish caught off LeMorne, Mauritius in 1993 (IGFA 2011).
- Marine
Recorded at 100 meters.
Habitat: reef-associated.
Water temperature and chemistry ranges based on 30 samples.
Environmental ranges
Depth range (m): 2.44 - 95
Temperature range (°C): 24.485 - 28.954
Nitrate (umol/L): 0.099 - 5.255
Salinity (PPS): 34.131 - 34.761
Oxygen (ml/l): 3.591 - 4.544
Phosphate (umol/l): 0.113 - 0.567
Silicate (umol/l): 1.579 - 10.063
Graphical representation
Depth range (m): 2.44 - 95
Temperature range (°C): 24.485 - 28.954
Nitrate (umol/L): 0.099 - 5.255
Salinity (PPS): 34.131 - 34.761
Oxygen (ml/l): 3.591 - 4.544
Phosphate (umol/l): 0.113 - 0.567
Silicate (umol/l): 1.579 - 10.063
Note: this information has not been validated. Check this *note*. Your feedback is most welcome.
Trophic Strategy
Dogtooth tuna feed upon shoaling fishes like herring (Clupea), sprats (Sprattus), mackerel (Scomber), whiting (Merlucciidae), cuttlefish (Sepia) and sometimes squid (Loligo).
Animal Foods: fish
Primary Diet: carnivore (Piscivore )
Life History and Behavior
Perception Channels: tactile ; chemical
Spawning takes place around December, January, and February. Dogtooth Tuna are non-guarders and are classified as open water substratum egg scatterers. The eggs are small and float near the surface, hatching within two days. Larvae are .635cm (0.25 inch) long and grow very quickly.
Breeding season: Spawning takes place from December to February.
Key Reproductive Features: iteroparous ; seasonal breeding ; gonochoric/gonochoristic/dioecious (sexes separate); sexual ; fertilization (External ); oviparous
- Fishbase, 2007. "Fishbase" (On-line). Accessed Sept. 21, 2000 at http://www.fishbase.org/Summary/SpeciesSummary.cfm?genusname=Gymnosarda&speciesname=unicolor.
- Grizimek, B., W. Ladiges. 1974. Fishes 2. New York, New York: Van Nostrand Reinhold LTD.
Molecular Biology and Genetics
Molecular Biology
Barcode data: Gymnosarda unicolor
The following is a representative barcode sequence, the centroid of all available sequences for this species.
There are 2 barcode sequences available from BOLD and GenBank.
Below is a sequence of the barcode region Cytochrome oxidase subunit 1 (COI or COX1) from a member of the species.
See the BOLD taxonomy browser for more complete information about this specimen and other sequences.
-- end --
Download FASTA File
Statistics of barcoding coverage: Gymnosarda unicolor
Public Records: 8
Specimens with Barcodes: 12
Species With Barcodes: 1
Conservation Status
Dogtooth tuna are susceptible to overfishing, and commercial net fishing is their biggest threat. Coupled with worldwide concern over dolphin free tuna, there is hope that this species can be protected with proper management. As yet, they have not made the ICUN red list and are not considered a threatened species.
US Federal List: no special status
CITES: no special status
IUCN Red List of Threatened Species: no special status
IUCN Red List Assessment
Red List Category
Red List Criteria
Year Assessed
FAO worldwide reported landings fluctuate, but in general show a gradual increase in reported catch from 94 tonnes in 1971 to a high of 808 tonnes in 2003 (FAO 2009).
Population Trend
Conservation Actions
Relevance to Humans and Ecosystems
Ciguatera Fish Poisoning in humans is caused by the consumption of subtropical and tropical finfish. A naturally occurring toxin found in an algae (dinoflagellate) species common in the lower latitudes is the suspected cause. The occurrence of toxic fish is sporadic, and not all fish of a given species or locality will be toxic. Ciguatera poisoning is self-limiting: symptoms usually subside after a few days, are dismissed as seasickness or a hangover, and are therefore under reported.
Dogtooth tuna are a popular game fish. Many charter-fishing boats operate out of Australia and other parts of the south pacific providing a very lucrative business for their owners. They are also marketed commercially either canned or frozen.
Positive Impacts: food ; ecotourism
Dogtooth tuna
|This article needs additional citations for verification. (January 2010)
The dogtooth tuna is a large fast-swimming fish in the family Scombridae. It is placed in its own genus, Gymnosarda. It has the large teeth and straight edged first dorsal fin characteristic of the smaller bonitos (genus Sarda) and although it far exceeds them in size, reaching weights of over 150 kilograms, is considered more closely related to them than to the true tunas. The fish is known in Samoa and some other Pacific nations as tagi. In Mauritius it is referred to as the thon blanc, or white tuna. Its range extends from the Marshall Islands to the western Indian Ocean and as far north as southern Japan and South Korea, where the long-standing world record of 288 lbs was caught.
Dogtooth tuna frequent reef environments, with smaller fish being more commonly found near shallow reef areas and larger ones haunting deep reef drop off areas, seamounts and steep underwater walls. The dogtooth tuna is one of the apex non-pelagic predators in its environment, sharing that position with Giant Trevally, Napoleon Wrasse, and large groupers, as well as reef, bull and tiger sharks. An aggressive predator, the dogtooth tuna is an opportunistic feeder that is capable of taking a wide variety of prey items. In most areas the mainstay of its diet probably consists of pelagic schooling fish found near reef habitat, including fusiliers, carangids such as rainbow runners and mackerel scad, and scombrids.
The dogtooth tuna is appreciated in most of its range as a fine food fish and also as a game fish sought by both rod and reel anglers and spearfishermen. Dogtooth tuna used to be mostly taken as an incidental catch by anglers trolling for other gamefish - with natural baits for black marlin, for instance, or with lures for wahoo and Spanish (narrowbarred) mackerel. In the last 10 to 15 years there has been more dedicated effort directed at this species because of its rarity and sporting qualities. Dogtooth tuna are now a highly coveted prize by many European and Asian sports anglers. Large specimens are seldom found where there is significant fishing pressure and can be one of the most difficult gamefish to capture. Their habit of making high-speed downward runs when hooked, even on heavy tackle, often sees the line being cut as it contacts deep bottom structure. Sharks frequently mutilate both hooked and speared fish during the later stages of the fight, adding to the difficulty in landing them. The majority of dogtooth tuna captures have tended to be made by trolling with dead and live baits or with lures, particularly deep-swimming plugs. These techniques are still often used, with one niche specialty being the use of live bait such as rainbow runners to tease dogtooth tuna within range of light tackle and fly-casting anglers. High speed jigging with a variety of metal lures has increased tremendously in popularity in the last several years as advancements in tackle technology have resulted in lightweight rods and reels that are capable of handling heavy spectra-type braided lines. Some of the more popular destinations for anglers seeking this species include Okinawa and other islands of southern Japan, Rodrigues and other Indian Ocean islands such as the Maldives, Andaman & Nicobar Islands, Bali and elsewhere in Nusa Tenggara in Indonesia, the Great Barrier Reef and its outlying atolls, and many Western Pacific islands such as Vanuatu, Fiji, Tonga and Samoa.
Here is a picture of a smaller one [1]
|Wikispecies has information related to: Dogtooth tuna
|Wikimedia Commons has media related to: Gymnosarda unicolor
- "Gymnosarda unicolor". Integrated Taxonomic Information System. http://www.itis.gov/servlet/SingleRpt/SingleRpt?search_topic=TSN&search_value=172469. Retrieved 18 April 2006.
- Froese, Rainer, and Daniel Pauly, eds. (2006). "Gymnosarda unicolor" in FishBase. January 2006 version.
- http://www.howtocatchanyfish.com/dogtooth-tuna.html


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I am a husband, father, son, brother, uncle and friend. I believe in sharing my talents and experiences by giving back to the community by giving my time to coaching, church and especially to the disability community. I truly believe that all men and women are created equally.
Make Your Voice Heard on the New Family Care/IRIS System
From the Wisconsin Long-Term Care Coalition.
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All around us, All Abilities Playgrounds are popping up. It is time for one to come to the South Shore. Here is the story of a passionate group of parents who are working to make that happen.
Make Your Voice Heard Come Election Day
Now that the dust has settled on a State budget that leaves a lot to be desired, it is time to turn our attention to what can we do next. Besides remaining in contact with your State legislator to continue to remind them of your concerns surrounding the proposals to long-term care, we can gear up for the next election. If nothing else, the disability community had a strong voice in this past State budget process. I was told by a number of people in the Capitol that the disability community had such a strong voice, they are a group that needs to be recognized come election time.
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We are heading into the home stretch with the State Budget. Joint Finance is expected to release their proposed budget this week to allow debates to take place in both the State Senate and State House. One thing is clear to me, two of the biggest issues in the Budget are not friendly to individuals with disabilities.


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Page 1
Dynamic task scheduling using genetic algorithms for heterogeneous distributed
Andrew J. Page and Thomas J. Naughton
Department of Computer Science,
National University of Ireland, Maynooth,
County Kildare, Ireland.
firstname.lastname@example.com, email@example.com
An algorithm has been developed to dynamically sched-
ule heterogeneous tasks on heterogeneous processors in a
distributed system. The scheduler operates in an environ-
ment with dynamically changing resources and adapts to
variable system resources. It operates in a batch fashion
and utilises a genetic algorithm to minimise the total exe-
cution time. We have compared our scheduler to six other
schedulers, three batch-mode and three immediate-mode
schedulers. We have performed simulations with randomly
tributions, whilst varying the communication overheads be-
tween the clients and scheduler. We have achieved more effi-
cient results than all other schedulers across a range of dif-
ferent scenarios while scheduling 10,000 tasks on up to 50
heterogeneous processors.
1. Introduction
Distributed computing is a promising approach to meet
the increasing computational requirements of scientific re-
search. However, a number of issues arise which are not en-
countered in sequential processing which, if not properly
handled, can nullify the benefits of parallelization. We be-
lieve that task scheduling is the most important of these is-
sues because inappropriate scheduling of tasks can fail to
exploit the true potential of a distributed system and can off-
set the gains from parallelization due to excessive commu-
nication overhead or under-utilisation of resources. Thus it
falls to one's scheduling strategy to produce schedules that
efficiently utilise the resources of the distributed system and
minimise the total execution time. The problem of schedul-
ing heterogeneous tasks onto heterogeneous resources, oth-
erwise known as the task allocation problem, is an NP-hard
problem for the general case [5].
Many heuristic algorithms exist for specific in-
stances of the task scheduling problem, but are inefficient
for a more general case [9]. The use of Holland's genetic al-
gorithms [7] (GAs) in scheduling, which apply evolution-
ary strategies to allow for the fast exploration of the search
space of schedules, allows good solutions to be found
quickly and for the scheduler to be applied to more gen-
eral problems. Many researchers have investigated the use
of GAs to schedule tasks in homogeneous [8, 19] and het-
erogeneous [1, 11, 15, 18] multi-processor systems with
notable success.
Unfortunately, assumptions are often made which reduce
the generality of these solutions, such that scheduling can
be calculated off-line in advance and cannot change [1,
8, 15, 18], all communications times are known in ad-
vance [1, 8, 15, 18], networks provide instantaneous mes-
sage passing [19], that all processors have equal capabili-
ties and are dedicated to processing tasks from the sched-
uler [1, 8, 9, 14, 15, 17, 18, 19, 20]. These assumptions
limit the generality of these scheduling strategies in real-
world distributed systems. It would be more preferable to
make no assumptions about the homogeneity of the proces-
sors, or about the availability of system resources.
In this paper a scheduling strategy is presented which
uses a GA to schedule heterogeneous tasks on to heteroge-
neous processors to minimise the total execution time. It op-
erates dynamically, allowing for tasks to arrive for process-
ing continuously, and considers variable system resources,
which has not been considered by other dynamic GA sched-
ulers. This paper is an updated version of [13] in which
we present a revised algorithm, more comprehensive exper-
iments, and significant testing and verification.
In Sect. 2 we review related work and give an overview
of how a GA operates. In Sect. 3 we describe our schedul-
ing algorithm. In Sect. 4 we present the results of our per-
formance experiments. In Sect. 5 we give our conclusions
and suggest future directions for our work in Sect. 6.
Page 2
initialise population
random mutation
}while(stopping conditions not met)
return best individual
Figure 1. Pseudo code for genetic algorithm
2. Genetic Algorithms and scheduling
There are many examples in the literature of artificial in-
telligence techniques being applied to task scheduling [1, 8,
11, 14, 15, 17, 18, 19, 20]. Meta-heuristic search techniques
such as GAs [7], tabu [6], and ant colony search [3] are most
applicable to the task scheduling problem because we wish
to quickly search for a near optimal schedule out of all pos-
sible schedules. Good results have resulted from the use of
GAs in task scheduling algorithms [1, 8, 11, 14, 15, 17, 19,
A GA is a meta-heuristic search technique which allows
for large solution spaces to be partially searched in poly-
nomial time, by applying evolutionary techniques from na-
ture [7]. GAs use historical information to exploit the best
solutions from previous searches, known as generations,
along with random mutations to explore new regions of
the solution space. In general a GA repeats three steps (se-
lection, crossover, and random mutations) as shown by the
pseudo code in Fig. 1. Selection according to fitness (effi-
ciency in our case) is a source of exploitation, and crossover
and random mutations promote exploration.
A generation of a GA contains a population of individ-
uals, each of which correspond to a possible solution from
the search space. Each individual in the population is eval-
uated with a fitness function to produce a value which in-
dicates the goodness of a solution. Selection takes a certain
number of individuals in the population and brings them
forward to the next generation. Crossover takes pairs of in-
dividuals and uses parts of each to produce new individu-
als. Random mutations swaps parts of an individual to pre-
vent the GA from getting caught in a local minimum.
Much work has been done on using GAs for static
scheduling [1, 8, 15, 18], where schedules are created be-
fore runtime. However, the state of all tasks and system re-
sources must be known a priori and cannot change. This
limits these schedulers to specific problems and systems.
Dynamic GA schedulers [11, 19, 20] create schedules at
runtime, with knowledge about the properties of the system
and tasks possibly not known in advance, allowing for vari-
able system and task properties to be considered. Dynamic
GA schedulers are thus more practical than static sched-
ulers for real-world distributed systems. Current dynamic
GA schedulers have been shown to produce near optimal
schedules in simulations [19, 20], although assumptions
that have been made limit their generality. For example, in-
stantaneous message passing [19], homogeneous process-
ing resources [19, 20], variable communications costs and
variable processing resources are not considered [19, 20].
3. Scheduling Algorithm
The algorithm we have developed is based on the
state-of-the-art homogeneous GA scheduler developed
by Zomaya et al. [19, 20]. We have created an algo-
rithm which can adapt to varying resource environments
and can produce near-optimal schedules. We wish to sched-
ule an unknown total number of tasks for processing on a
distributed system with a minimal total execution time, oth-
erwise known as makespan.
The processors of the distributed system are heteroge-
neous. The available network resources between processors
in the distributed system can vary over time. The availabil-
ity of each processor can vary over time (processors are not
dedicated can may have other tasks that partially use their
resources). Tasks are indivisible, independent of all other
tasks, arrive randomly, and can be processed by any proces-
sor in the distributed system.
When tasks arrive they are placed in a queue of unsched-
uled tasks. Batches of tasks from this queue are scheduled
on processors during each invocation of the scheduler. Each
idle processor in the system requests a task to process from
the scheduler, which it processes and returns. The sched-
uler contains a queue of future tasks for each processor, and
when a request for work is received the task at the head of
the corresponding queue is sent for processing. A proces-
sor does not contain a queue of tasks; because network re-
sources are limited and processing resources are not ded-
icated, we wish to avoid repeatedly issuing the same task
multiple times, e.g., when a machine is switched off.
Each task has a resource requirement which is measured
in millions of floating point operations (MFLOPs). The
available processing resources, or execution rate, of each
processor is measured in MFLOPs per second, which we
write as Mflop/s to avoid confusion. The execution rate is
measured using Dongarra's Linpack benchmark [4]. This is
a recognised standard used to benchmark systems for in-
clusion in the list of Top 500 Supercomputers [16]. Avail-
able processing and network resources vary over time, so
a smoothing function is used to minimise localised fluctua-
tions, thus allowing for a more realistic processing environ-
ment. A single processor is dedicated to scheduling.
The queue of unscheduled tasks could contain a large
number of tasks and if all where to be scheduled at once, the
scheduler could take a long time to find an efficient sched-
Page 3
Figure 2. Encoding of a schedule
ule. To speedup the scheduler, and reduce the chance of pro-
cessors becoming idle, we only consider a subset of the un-
scheduled tasks, which we call a batch. A larger batch will
usually result in a more efficient schedule [19]. We must
thus trade the batch size with running time. To do this we
dynamically set the batch size according to the estimated
amount of time until the first processor becomes idle.
3.1. Encoding
Each individual in the population represents a possible
schedule. Fig. 2 shows the encoding used. Each character
is a mapping between a task and processor. Each character
contains the unique identification number of a task, with −1
being used to delimit different processor queues, where Pi
is processor i. Thus the number of characters is H +M −1,
where H is the number of tasks in the batch, and M is the
number of processors.
3.2. Fitness Function
A fitness function attaches a value to each individual in
the population, which indicates the goodness of the sched-
ule. We use relative error to generate the fitness values. We
wish to calculate the fitness of each individual in the popu-
lation. Previously assigned, but unprocessed, load for each
processor is considered by calculating the finishing time of
a processor j. δj,i = (Lj/Pj), where Ljdenotes the pre-
viously assigned load, measured in MFLOPs, and Pjis the
current processing power in Mflop/s of processor j.
The theoretical optimal processing time can now be
found as,
ing requirement of task i in the batch (in MFLOPs) and N
is the total number of tasks in the batch.
The relative error of individual i is given as
where Γc
ing task y on processor j. The fitness value of individual
i is Fi = 1/Ei, andFi = [0,1]. A larger value indi-
cates a better or fitter schedule.
ψ =
j=1δj, where tiis the process-
?????ψ −
y=1((ty/Pj) + Γc
(y,j)is the communication cost of schedul-
3.3. Selection, Crossover and Mutation
We choose to use the standard weighted roulette wheel
method of selection which is widely used by previous re-
searchers who have applied GAs to task scheduling [8, 14,
19]. Each individual iin the population is assigned a slot be-
tween 0 and 1. The size of slot i is ςi= Fi×
population. After the selection process is complete we use
the cycle crossover method [12] to promote exploration as
used in [19]. We have chosen to use two types of mutation
to promote exploration of the search space. First we ran-
domly swap elements of a randomly chosen individual in
the population. Then we use a re-balancing heuristic to mu-
tate and improve the population. The initial population is
generated using a list scheduling heuristic. A percentage of
tasks are randomly assigned to processors with the remain-
ing tasks being assigned to the processors that will finish
processing them the earliest. This leads to a well balanced
randomised initial population.
i=1ςi= 1 and p is the number of individuals in the
3.4. Stopping Conditions
The GA will evolve the population until one or more
stopping conditions are met. The individual with the lowest
makespan is selected after each generation and if it is less
than a specified minimum, the GA stops evolving. The max-
ity of the schedules returned with more than that number
does not justify the increased computation cost (as in [19]
and demonstrated in Fig. 3). The GA will also stop evolv-
ing if one of the processors becomes idle, in which case it
will return the best schedule found so far.
3.5. Rebalancing heuristic
We have introduced a re-balancing heuristic to improve
the quality of results returned. For each individual in the
population, in each generation, we select the most heavily
loaded processor. A task is then selected at random from an-
other processor and if it is smaller than a task in the most
heavily loaded processor, a swap is performed. We only al-
low a maximum of 5 random searches for a smaller task. If
the resulting schedule is fitter, it is kept.
Fig. 3 shows the average percentage decrease in
makespan after each generation of the GA, with points
taken after every generation. Each point on the graph is
an average of 50 runs of the scheduler. The largest re-
ductions in makespan occur in the first 100 generations.
After that the reductions begin to level out, requir-
ing larger numbers of generations, with little improvement.
Page 4
0100 200300400
No. of generations
500 600700 8009001000
% reduction in makespan
Pure GA
1 rebalance
50 rebalances
Figure 3. Average reduction in makespan af-
ter each generation of the GA
No. of rebalances
1012 141618 20
Time (s)
Figure 4. Time taken to schedule 10,000 tasks
with varying numbers of re-balances in every
generation of the GA
The re-balancing heuristic minimises the makespan fur-
ther than a pure GA, with 50 re-balances per individual in
the population per generation, resulting in the makespan be-
ing reduced to only 65% of its original value after 1000
generations (from Fig. 3). A single rebalance reduces
the makespan to approximatly 70%, whilst no rebalanc-
ing (pure GA) reduces to 75%.
These re-balances do have an associated additional cost
in terms of time. Fig. 4 shows the time taken to run a GA
for 1000 generations with varying numbers of re-balances.
It increases the time taken linearly. We have decided to only
perform a single re-balancing at each generation to enable
the algorithm to run quickly.
3.6. Smoothing Function
A smoothing function is defined that finds a single rep-
resentative value for a sequence of values. As each new
value is added to the sequence, this representative value
is updated. For the first i values of a sequence of values
a1,a2,..., this representative value would be denoted Γa
and defined recursively as Γa
where the smoothness of the sequence of representative val-
ues is controlled by ν ∈ [0,1], and where we let Γa
The function allows one to vary the influence of more re-
cent sequence values on the representative value, from no
influence (ν = 0) to complete dominance (ν = 1). The
smoothing function is employed in several instances in our
scheduler. In this paper, we describe the application of the
smoothing function to the first i values of an arbitrary se-
quence x1,x2,... with the notation Γx
i= Γa
i−1+ ν(ai− Γa
0= a1.
3.7. Dynamic Batch Size
We wish to define batch sizes that are large enough so
that the processor hosting the scheduler is utilized fully
(and to achieve low makespans), but not too large that any
processors become idle before the schedule has been fully
computed. The GA takes Θ(H2) time to create a schedule,
where H is the number of tasks in a batch (batch size). After
the pth batch has been scheduled, the first processor will be-
come idle after sp= minM
processing time in MFLOPs of the tasks waiting to be pro-
cessed by processor j, and M is the number of processors.
We choose Hp+1=
tion of the optimal size for batch p+1. Once a schedule has
been assigned the batch size is recalculated.
j=1(δj/Pj), where δjis the total
p+ 1?1/2?
as a simple approxima-
4. Experiments
The scheduling algorithm described in Sect. 3 has been
implemented and and applied to simulated data. A num-
ber of different experiments have been performed to
demonstrate the effectiveness of the scheduling algo-
rithm with varying communicating costs. We compare
our scheduler to six other schedulers, and evaluate the re-
sults using two different but related metrics, makespan and
efficiency. Makespan is the total execution time of a sched-
ule. Efficiency is the percentage of the time that proces-
sors actually spend processing rather than communicating
or idling.
A representative set of heterogeneous computing task
benchmarks does not exist as yet, as noted by Theys et
al. [15]. Our task sizes are randomly generated using, uni-
form, normal, and Poisson distributions. By using different
random distributions, we can demonstrate the flexibility of
our scheduling algorithm. This is often overlooked in the
Page 5
literature [1, 8, 9, 11, 14, 15, 17, 19, 20]. For these exper-
iments we will vary the communication costs and the task
4.1. Other schedulers
We have also compared our scheduling algorithm against
a number of well known batch and immediate mode heuris-
tic schedulers. An immediate mode scheduler only consid-
ers a single task for scheduling on a FCFS (first come,
first served) basis while a batch mode scheduler consid-
ers a number of tasks at once for scheduling. We will com-
pare our algorithm to three immediate mode and three batch
mode schedulers [11, 15].
The earliest first (EF) algorithm is an immediate mode
scheduler. When a task is presented for processing, the
scheduler considers the existing load on each processor and
allocates the task to the processor which will finish pro-
cessing it the earliest. The EF algorithm uses the available
information about the task and the processors when mak-
ing a scheduling decision. It has a worst case complex-
ity of Θ(M), where M is the number of processors, when
scheduling a single task.
scheduler which allocates tasks to the processor with the
lowest current load, measured in our case as MFLOPs. It
does not consider the size of a task when scheduling it. It
has a worst case complexity of Θ(M).
The round robin (RR) scheduler is the most basic of
the immediate mode schedulers used in these experiments,
ion. No load or task information is used when making a
scheduling decision. It has a worst case complexity of Θ(1).
The max-min (MX) scheduler is a batch mode heuristic
scheduler. It takes batches of tasks on a FCFS basis. These
tasks are then sorted according to task size in a descend-
ing order. The largest task is then allocated to the processor
that will finish processing it first (same as EF). This is re-
peated until the batch is empty, after which another batch
is considered. The main aim of this scheduler is to have the
largest tasks scheduled as early as possible, with smaller
tasks at the end filling in the gaps. It has a complexity of
Θ(max(M,nlogn)), where n is the size of the batch.
The min-min (MM) scheduler is similar to the MX
ing to size.
The scheduler proposed by Zomaya et al. (ZO) in [19]
has been implemented for this paper. It is the current state
of the art homogeneous GA scheduler and the basis for our
scheduler. The ZO scheduler was easily converted from a
homogeneous scheduler to a heterogeneous scheduler by
using the Mflop/s benchmark for task sizes rather than time.
It is a batch scheduler which uses GAs to create schedules.
0 0.01 0.020.030.040.05 0.06 0.070.08 0.090.1
1/mean communications cost
Figure 5. Efficiency of schedulers with a nor-
mal distribution of task sizes and varying
communication costs.
We have validated our implementation of this scheduler by
reproducing some of the performance results in [19] (not in-
cluded here).
4.2. Setup
We simulated the performance of our scheduler
against the performance of six other schedulers, de-
scribed in Sect. 4.1, for these experiments. All of the
tasks arrived for scheduling at the beginning of the sim-
ulation. Each experiment was repeated 50 times and an
average result was calculated for each point on the result-
ing graphs.
We scheduled up to 10,000 heterogeneous tasks onto 50
heterogeneous processors. For these experiments each pro-
dicting the communication costs in advance will improve
the efficiency, compared to heuristics which adapt to com-
munication costs after they have been incurred. All sched-
ulers were presented with the same set of tasks for schedul-
ing and all schedulers have the same information available
to them.
We have decided to use a population size of 20, which is
known as a micro GA [2] and used in [19, 20], which speeds
up computation time without impacting greatly on the final


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Tatiana Barrera Speaks on "NO MILK, PLEASE"
in Health
Tatiana Barrera, author of Eat Alkaline Foods. I have dedicated myself to the study of ways to improve your health through nutrition, and love to be able to help people do so. This passion for helping improve people's health gave birth to Eat Alkaline Foods. We have found how small changes in our nutrition and lifestyle can have profound changes in our health, and how many of us are unaware of that fact.
Sharing the message of good health and alkaline lifestyle is a passion for us at Eat Alkaline Foods. And we want to be here for you every step of the way to help facilitate your journey towards an alkaline lifestyle.
A.J. Barrera - "Breaking Through"
in Spirituality
Join Suzane for The Suzane Northrop Show, which airs monthly on Blog Talk Radio. Each month Suzane interviews exciting guests in the field of spirituality and metaphysics, and offers live on-air readings for listeners.
This month, Suzane welcomes special guest, A.J. Barrera.
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in Wrestling
http://www.facebook.com/pwroundtable You didn't see them with their hands raised often but without them, the stars of the time would not have been stars. Tonight we talk old school with Nacho Barrera and Sonny Rogers. We will also be joined by the trainers of Ladies Night guest Raze, Santino Brothers. We invite your questions in our chat room or by calling 347-826-9418. Please join us.
tsuNomination with Danielle Paccione and Bill Bachman - September 22,2015
in Paranormal
Tonight your hosts Danielle Paccione and Bill Bachman discuss Isla de las Munecas...The Island of the Dolls!
The best-known chinampa in Xochimilco belonged to a man named Julián Santana Barrera, a native of the La Asunción neighborhood. Santana Barrera was a loner, who was rarely seen in most of Xochimilco. He came to fame because he would collect the old broken bodies of dolls from the canals and rubbish trips, and then hang them from branches and tie them to tree trunks to keep away evil spirits and appease the spirit of a dead girl he had found. He would say that he believed that the dolls were somehow still "alive" but forgotten by their owners and at night, these dolls are said to walk around the island killing animals. Before this, it was thought that no one lived on this chinampa, but Santana Barrera was there, living in a hut with no services and generally did not receive visitors, other than family. The display of dolls and parts attracted attention of the press. Eventually, he stated to them that the dolls were there to keep away evil spirits and to help with the harvests in his gardens. His favorite was called La Moneca and he frequently moved the dolls around among the tree branches.
Be sure to join the interactive chat room on our website and call in if you have a story about The Island of the Dolls (347) 539-5549!
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Ace Micheals interviews broadway performer Tatiana Monteiro
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Paranormal Static,presents Sherrie Schoon, Island Of The Creepy Dolls
in Spirituality
Paranormal Static, presents Island Of The Dolls.Special guest sharrie Schoon of Creepy Dolls.Your host Jan Reynolds,Evan jensen.The Island of the Dolls, or Isla de las Mucenas, is a popular tourist attraction located in the canals of Xochimico near Mexico City. The tragic story of why the dolls exist starts with island's lone inhabitant Don Julian Santana Barrera.
Paranormal Static,presents Sherrie Schoon, Island Of The Creepy Dollsl Static,presents Sherrie Schoon, Island Of The Creepy Dolls
Ask MomRN Show: Back to School Tips, Rev Run and Vanessa Simmons, Baby Care
in Family
Ask MomRN Show is back from summer hiatus and today's show is full of great guests and valuable tips!
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Santa Cruz vs. Mares Preview-Debate-Predictions!Will Leo Sink or Swim vs. Mares?
in Sports
This episode begins with a news segment featuring previews for a few recent fights that were finalized on the fall schedule and Recap from the Cotto vs. Canelo Press Conference. Plus, Preview, Debate, & Predictions for the highly anticipated fight between the unbeaten/untested Leo Santa Cruz and Abner Mares, who looks to regain his stature in the boxing world. The bout will be broadcasted live on the PBC on ESPN from the Staples Center in LA! Other Topics For Discussion-Sullivan Barrera agrees to a Title Elimantor with Artur Beterbiev to become mandotary challenger to Sergey Kovalev! -Terence Crawford faces Dierry Jean on HBO October 24th. -Preview the upcoming fight schedule which will include the Co-Feature to Mares/Santa Cruz fight between Hugo Ruiz and Julio Cesar Ceja! Plus, More Current News Topics! Follow on Twitter @RopeADopeRadio Call 646-381-4990 Listen Live or Join The Roundtable! Your Voice Will Be Heard!
Independent Everything: LIVE with Memphis R&B/Hip-Hop Artist ~ Tatiana Evans
in Music
Tatiana Evans is an R&B/Hip-Hop artist from Memphis, TN. Music has always been her passion. At a young age, she sang in schools and in the church choirs. As Tatiana got older, she started getting in the studio to record her music. At age 25, her cousin-in-law, Hollowpoynt, gave her free studio time and her uncle, Kimbo Brown, gave her free beats. To show the world her dream and how passionate she is about music, her uncle also shot her video.
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in Music
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Teens charged with mischief, trespassSeptember 25,2013Teens accused of
mischief, trespass
HARTLAND Two Windsor teenagers face multiple charges for allegedly causing damage to a Hartland laundry mat.
According to police, Timothy Murphy, 18, and Makenzie Delaney, 17, caused $2,000 worth of damage to two coin-operated machines Thursday at the Itty Bitty Laundry Mat on Route 5 in Hartland.
Murphy and Delaney were located by police and charged with unlawful mischief, unlawful trespass and providing false information to a police officer.
Arraignments for both suspects are scheduled for Nov. 5 in criminal court in White River Junction.MORE IN This Just In
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