Traffic model and performance evaluation of Web servers

Liu, Zhen; Niclausse, Nicolas; Jalpa-Villanueva, César

doi:10.1016/s0166-5316(01)00046-3

Cited by 91 publications

(48 citation statements)

References 20 publications

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“…In the next subsection, we describe a simple alternative way for estimating I that is also able to overcome the limitations imposed by the measurement granularity wall. We also remark that, if the measurements show long-range dependence, then the index of dispersion value can grow arbitrarily large and other measurements should be used to quantify temporal dependence [11]. In our experiments with TPC-W, we have found that the index of dispersion was very effective to characterize workload burstiness and the value of I was always converging to a finite value.…”

Section: Characterization Of Burstinessmentioning

confidence: 60%

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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Section: Characterization Of Burstinessmentioning

confidence: 60%

How to parameterize models with bursty workloads

Casale

Cherkasova

et al. 2008

SIGMETRICS Perform. Eval. Rev.

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show abstract

“…Web activity may be analyzed in terms of a three-layer model similar to the one proposed earlier: the aggregate traffic, the sequences of sessions from individual clients, and the requests within each session [524]. But it is also possible to dissect the data in more detail, leading to some variant of the following multilevel model [36,335,445,535,575]:…”

Section: World Wide Web Activitymentioning

confidence: 99%

“…A useful generative model can be constructed based on common activity patterns when using different applications. For example, web browsing can be characterized at the following five levels [36,335,445,535,575]:…”

Section: Time Framementioning

confidence: 99%

Workload Modeling for Computer Systems Performance Evaluation

Feitelson

2015

186

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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“…For workload generation, we used the WAGON model that has been validated in extensive studies of production web servers [14]. This model characterizes web workloads in terms of several parameters, including the rate at which new sessions arrive (session arrival rate) and the number of URLs requested when a page is accessed (burst length).…”

Section: Experimental Assessmentmentioning

confidence: 99%

Optimizing Quality of Service Using Fuzzy Control

Diao

Hellerstein

Parekh

2002

Management Technologies for E-Commerce and E-Business Applications

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Abstract. The rapid growth of eCommerce increasingly means business revenues depend on providing good quality of service (QoS) for web site interactions. Traditionally, system administrators have been responsible for optimizing tuning parameters, a process that is time-consuming and skills-intensive, and therefore high cost. This paper describes an approach to automating parameter tuning using a fuzzy controller that employs rules incorporating qualitative knowledge of the effect of tuning parameters. An example of such qualitative knowledge in the Apache web server is "MaxClients has a concave upward effect on response times." Our studies using a real Apache web server suggest that such a scheme can improve performance without human intervention. Further, we show that the controller can automatically adapt to changes in workloads.

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Traffic model and performance evaluation of Web servers

Cited by 91 publications

References 20 publications

How to parameterize models with bursty workloads

How to parameterize models with bursty workloads

Workload Modeling for Computer Systems Performance Evaluation

Optimizing Quality of Service Using Fuzzy Control

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