Characterizing data analysis workloads in data centers

Jia, Zhen; Wang, Lei; Zhan, Jianfeng; Zhang, Lixin; Luo, Chunjie

doi:10.1109/iiswc.2013.6704671

Cited by 103 publications

(50 citation statements)

References 28 publications

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“…For example, DCBench [26] and the AMPLab Big Data Benchmark [1] focus on data analytics applications, while Sirius [24] targets applications for intelligent personal assistants like Apple Siri. Besides being domainspecific, these suites include applications with higher latencies than the interactive services TailBench focuses on.…”

Section: B Tailbench Vs Existing Benchmark Suitesmentioning

confidence: 99%

Tailbench: a benchmark suite and evaluation methodology for latency-critical applications

Kasture

Sánchez

2016

2016 IEEE International Symposium on Workload Characterization (IISWC)

150

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Abstract-Latency-critical applications, common in datacenters, must achieve small and predictable tail (e.g., 95 th or 99 th percentile) latencies. Their strict performance requirements limit utilization and efficiency in current datacenters. These problems have sparked research in hardware and software techniques that target tail latency. However, research in this area is hampered by the lack of a comprehensive suite of latency-critical benchmarks.We present TailBench, a benchmark suite and evaluation methodology that makes latency-critical workloads as easy to run and characterize as conventional, throughput-oriented ones. TailBench includes eight applications that span a wide range of latency requirements and domains, and a harness that implements a robust and statistically sound load-testing methodology. The modular design of the TailBench harness facilitates multiple load-testing scenarios, ranging from multi-node configurations that capture network overheads, to simplified single-node configurations that allow measuring tail latency in simulation. Validation results show that the simplified configurations are accurate for most applications. This flexibility enables rapid prototyping of hardware and software techniques for latency-critical workloads.

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Section: B Tailbench Vs Existing Benchmark Suitesmentioning

confidence: 99%

Tailbench: a benchmark suite and evaluation methodology for latency-critical applications

Kasture

Sánchez

2016

2016 IEEE International Symposium on Workload Characterization (IISWC)

150

View full text Add to dashboard Cite

show abstract

“…[15]), virtualization techniques to obtain energy savings [16], [17] or the use of low-latency power state changes in servers to improve the impact of virtualization [18]. To complement these, there are many contributions that focus on the characterization of workloads in DCs, such as [19].…”

Section: Related Workmentioning

confidence: 99%

On the feasibility of collaborative green data center ecosystems

et al. 2015

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The increasing awareness of the impact of the IT sector on the environment, together with economic factors, have fueled many research efforts to reduce the energy expenditure of data centers. Recent work proposes to achieve additional energy savings by exploiting, in concert with customers, services' workloads and to reduce data centers' carbon footprints by adopting demandresponse mechanisms between data centers and their energy providers. In this paper, we debate about the incentives that customers and data centers should have to adopt such measures and propose a new service type and pricing scheme that is economically attractive and technically realizable. Simulation results based on real measurements confirm that our scheme can achieve additional energy savings while preserving service performance.

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“…Ferdman et al study the microarchitecture-level characteristics of scale-out programs in the cloud [13]; Wang et al propose a big data benchmark suite named BigDataBench for evaluating Internet services [14]; and Jia et al characterize data analysis workloads in data centers [16]. Early Hadoop benchmarks include GridMix [17], Sort [18], and TeraSort [19,20].…”

Section: Related Workmentioning

confidence: 99%

ShenZhen transportation system (SZTS): a novel big data benchmark suite

Xiong

Eeckhout

et al. 2016

J Supercomput

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Data analytics is at the core of the supply chain for both products and services in modern economies and societies. Big data workloads, however, are placing unprecedented demands on computing technologies, calling for a deep understanding and characterization of these emerging workloads. In this paper, we propose ShenZhen Transportation System (SZTS), a novel big data Hadoop benchmark suite comprised of real-life transportation analysis applications with real-life input data sets from Shenzhen in China. SZTS uniquely focuses on a specific and real-life application domain whereas other existing Hadoop benchmark suites, such as HiBench and CloudRank-D, consist of generic algorithms with synthetic inputs. We perform a cross-layer workload characterization at the microarchitecture level, the operating system (OS) level, and the job level, revealing unique characteristics of SZTS compared to existing Hadoop benchmarks as well as general-purpose multi-core PARSEC benchmarks. We also study the sensitivity of workload behavior with respect to input data size, and we propose a methodology for identifying representative input data sets.

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Characterizing data analysis workloads in data centers

Cited by 103 publications

References 28 publications

Tailbench: a benchmark suite and evaluation methodology for latency-critical applications

Tailbench: a benchmark suite and evaluation methodology for latency-critical applications

On the feasibility of collaborative green data center ecosystems

ShenZhen transportation system (SZTS): a novel big data benchmark suite

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