Internet of Things (IoT) is a technology paradigm where millions of sensors monitor, and help inform or manage, physical, environmental and human systems in real-time. The inherent closed-loop responsiveness and decision making of IoT applications makes them ideal candidates for using low latency and scalable stream processing platforms. Distributed Stream Processing Systems (DSPS) are becoming essential components of any IoT stack, but the efficacy and performance of contemporary DSPS have not been rigorously studied for IoT data streams and applications. Here, we develop a benchmark suite and performance metrics to evaluate DSPS for streaming IoT applications. The benchmark includes 13 common IoT tasks classified across functional categories and forming micro-benchmarks, and two IoT applications for statistical summarization and predictive analytics that leverage various dataflow patterns of DSPS. These are coupled with stream workloads from real IoT observations on smart cities. We validate the benchmark for the popular Apache Storm DSPS, and present the results.
@cite_5: This paper presents our joint research efforts on big data benchmarking with several industrial partners. Considering the complexity, diversity, workload churns, and rapid evolution of big data systems, we take an incremental approach in big data benchmarking. For the first step, we pay attention to search engines, which are the most important domain in Internet services in terms of the number of page views and daily visitors. However, search engine service providers treat data, applications, and web access logs as business confidentiality, which prevents us from building benchmarks. To overcome those difficulties, with several industry partners, we widely investigated the open source solutions in search engines, and obtained the permission of using anonymous Web access logs. Moreover, with two years' great efforts, we created a sematic search engine named ProfSearch (available from this http URL). These efforts pave the path for our big data benchmark suite from search engines---BigDataBench, which is released on the web page (this http URL). We report our detailed analysis of search engine workloads, and present our benchmarking methodology. An innovative data generation methodology and tool are proposed to generate scalable volumes of big data from a small seed of real data, preserving semantics and locality of data. Also, we preliminarily report two case studies using BigDataBench for both system and architecture researches.
@cite_21: There is a tremendous interest in big data by academia, industry and a large user base. Several commercial and open source providers unleashed a variety of products to support big data storage and processing. As these products mature, there is a need to evaluate and compare the performance of these systems. In this paper, we present BigBench, an end-to-end big data benchmark proposal. The underlying business model of BigBench is a product retailer. The proposal covers a data model and synthetic data generator that addresses the variety, velocity and volume aspects of big data systems containing structured, semi-structured and unstructured data. The structured part of the BigBench data model is adopted from the TPC-DS benchmark, which is enriched with semi-structured and unstructured data components. The semi-structured part captures registered and guest user clicks on the retailer's website. The unstructured data captures product reviews submitted online. The data generator designed for BigBench provides scalable volumes of raw data based on a scale factor. The BigBench workload is designed around a set of queries against the data model. From a business prospective, the queries cover the different categories of big data analytics proposed by McKinsey. From a technical prospective, the queries are designed to span three different dimensions based on data sources, query processing types and analytic techniques. We illustrate the feasibility of BigBench by implementing it on the Teradata Aster Database. The test includes generating and loading a 200 Gigabyte BigBench data set and testing the workload by executing the BigBench queries (written using Teradata Aster SQL-MR) and reporting their response times.
@cite_3: The MapReduce model is becoming prominent for the large-scale data analysis in the cloud. In this paper, we present the benchmarking, evaluation and characterization of Hadoop, an open-source implementation of MapReduce. We first introduce HiBench, a new benchmark suite for Hadoop. It consists of a set of Hadoop programs, including both synthetic micro-benchmarks and real-world Hadoop applications. We then evaluate and characterize the Hadoop framework using HiBench, in terms of speed (i.e., job running time), throughput (i.e., the number of tasks completed per minute), HDFS bandwidth, system resource (e.g., CPU, memory and I O) utilizations, and data access patterns.
@cite_16: For the last decade, the research community and the industry have used TPC-D and its successor TPC-H to evaluate performance of decision support technology. Recognizing a paradigm shift in the industry the Transaction Processing Performance Council has developed a new Decision Support benchmark, TPC-DS, expected to be released this year. From an ease of benchmarking perspective it is similar to past benchmarks. However, it adjusts for new technology and new approaches the industry has embarked on in recent years. This paper describes the main characteristics of TPC-DS, explains why some of the key decisions were made and which performance aspects of decision support system it measures.
There has been a slew of Big Data benchmarks that have been developed recently in the context of processing high volume (i.e., MapReduce-style) and enterprise web data that complement our work. @cite_3 is a workload suite for evaluating Hadoop with popular micro-benchmarks like Sort, WordCount and TeraSort, MapReduce applications like Nutch Indexing and PageRank, and machine learning algorithms like K-means Clustering. @cite_5 analyzes workloads from social network and search engines, and analytics algorithms like Support Vector Machine (SVM) over structured, semi-structured and unstructured web data. Both these benchmarks are general purpose workloads that do not target any specific domain, but MapReduce platforms at large. @cite_21 uses a synthetic data generator to simulate enterprise data found in online retail businesses. It combines structured data generation from the TPC-DS benchmark @cite_16 , semi-structured data on user clicks, and unstructured data from online product reviews. Queries cover data by processing periodic refreshes that feed into the data store, by including free-text user reviews, and by querying over a large web log of clicks. We take a similar approach for benchmarking fast data platforms, targeting the IoT domain specifically and using real public data streams.