TPC-W e-commerce benchmark evaluation

García, Daniel; García, Javier Avilés

doi:10.1109/mc.2003.1178045

Cited by 84 publications

(50 citation statements)

References 6 publications

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“…Another workload model for e-commerce is implied by the TPC-W benchmark [277]. This has a client-server structure, with clients being browser emulators that create the workload on the server, which is the system being evaluated.…”

Section: E-commercementioning

confidence: 99%

See 1 more Smart Citation

Workload Modeling for Computer Systems Performance Evaluation

Feitelson

2015

196

131

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Reliable performance evaluations require the use of representative workloads. This is no easy task since modern computer systems and their workloads are complex, with many interrelated attributes and complicated structures. Experts often use sophisticated mathematics to analyze and describe workload models, making these models difficult for practitioners to grasp. This book aims to close this gap by emphasizing the intuition and the reasoning behind the definitions and derivations related to the workload models. It provides numerous examples from real production systems, with hundreds of graphs. Using this book, readers will be able to analyze collected workload data and clean it if necessary, derive statistical models that include skewed marginal distributions and correlations, and consider the need for generative models and feedback from the system. The descriptive statistics techniques covered are also useful for other domains.

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Section: E-commercementioning

confidence: 99%

“…a web e-commerce benchmark, which measures performance by completed web interactions per second, subject to a response time requirement for each interaction [277]. The scenario is an online book store, with the system comprising web servers connected to a back-end database, as well as image servers and caches.…”

Section: Tpc-wmentioning

confidence: 99%

Workload Modeling for Computer Systems Performance Evaluation

Feitelson

2015

196

131

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“…Several studies on analysis and modeling of web workloads have been done [9][10][11]. Since obtaining real traces from complex multi-tier systems is difficult, a number of benchmarks have been developed to model the real systems [12][13][14]. Figure 1 shows a workflow used by traditional performance evaluation approaches, which require a real user to interact with the application to record scripts that are used by load generators.…”

Section: Related Workmentioning

confidence: 99%

Performance Evaluation Approach for Multi-Tier Cloud Applications

Bahga

Madisetti

2013

JSEA

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Complex multi-tier applications deployed in cloud computing environments can experience rapid changes in their workloads. To ensure market readiness of such applications, adequate resources need to be provisioned so that the applications can meet the demands of specified workload levels and at the same time ensure that service level agreements are met. Multi-tier cloud applications can have complex deployment configurations with load balancers, web servers, application servers and database servers. Complex dependencies may exist between servers in various tiers. To support provisioning and capacity planning decisions, performance testing approaches with synthetic workloads are used. Accuracy of a performance testing approach is determined by how closely the generated synthetic workloads mimic the realistic workloads. Since multi-tier applications can have varied deployment configurations and characteristic workloads, there is a need for a generic performance testing methodology that allows accurately modeling the performance of applications. We propose a methodology for performance testing of complex multi-tier applications. The workloads of multi-tier cloud applications are captured in two different models-benchmark application and workload models. An architecture model captures the deployment configurations of multi-tier applications. We propose a rapid deployment prototyping methodology that can help in choosing the best and most cost effective deployments for multi-tier applications that meet the specified performance requirements. We also describe a system bottleneck detection approach based on experimental evaluation of multi-tier applications.

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“…TPC-W is a widely used e-commerce benchmark that simulates the operation of an online bookstore [7]. Typically, this multi-tier application uses a three-tier architecture paradigm, which consists of a web server, an application server, and a back-end database.…”

Section: Experimental Environmentmentioning

confidence: 99%

How to parameterize models with bursty workloads

Casale

Cherkasova

et al. 2008

SIGMETRICS Perform. Eval. Rev.

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Although recent advances in theory indicate that burstiness in the service time process can be handled effectively by queueing models (e.g., MAP queueing networks [2]), there is a lack of understanding and of practical results on how to perform model parameterization, especially when this model parameterization must be derived from limited coarse measurements as is often encountered in practice. We propose a new modeling methodology based on the index of dispersion of the service process at a server, which is inferred by observing the number of completions within the concatenated busy periods of that server. The index of dispersion together with other measurements that reflect the "estimated" mean and the 95th percentile of service times are used to derive a MAP process that captures well burstiness and variability of the true service process, despite inevitable inaccuracies that result from inexact measurements. Detailed experimentation on a TPC-W testbed where all measurements are obtained via a commercially available tool, the HP (Mercury) Diagnostics, shows that the proposed technique offers a simple yet powerful solution to the difficult problem of inferring accurate descriptors of the service time process from coarse measurements. Experimental and model prediction results are in excellent agreement and argue strongly for the effectiveness of the proposed methodology under bursty or simply variable workloads.

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TPC-W e-commerce benchmark evaluation

Cited by 84 publications

References 6 publications

Workload Modeling for Computer Systems Performance Evaluation

Workload Modeling for Computer Systems Performance Evaluation

Performance Evaluation Approach for Multi-Tier Cloud Applications

How to parameterize models with bursty workloads

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