Synthesizing client load models for performance engineering via web crawling

Cai, Yuhong; Grundy, John; Hosking, John

doi:10.1145/1321631.1321684

Cited by 15 publications

(10 citation statements)

References 19 publications

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“…JPetStore uses Apache Derby 6 as its back-end database and contains 125 URLs. DellDVDStore is an open-source simulation of an online e-commerce site, which has been used in a number of industrial performance-related studies similarly to JPetStore [46,45,14,18,72]. DellDVDStore uses MySQL 7 as its backend database and contains 117 URLs.…”

Section: Subject Applicationsmentioning

confidence: 99%

Automating performance bottleneck detection using search-based application profiling

Shen

Luo

Poshyvanyk

et al. 2015

Proceedings of the 2015 International Symposium on Software Testing and Analysis

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Application profiling is an important performance analysis technique, when an application under test is analyzed dynamically to determine its space and time complexities and the usage of its instructions. A big and important challenge is to profile nontrivial web applications with large numbers of combinations of their input parameter values. Identifying and understanding particular subsets of inputs leading to performance bottlenecks is mostly manual, intellectually intensive and laborious procedure.We propose a novel approach for automating performance bottleneck detection using search-based input-sensitive application profiling. Our key idea is to use a genetic algorithm as a search heuristic for obtaining combinations of input parameter values that maximizes a fitness function that represents the elapsed execution time of the application. We implemented our approach, coined as Genetic Algorithm-driven Profiler (GA-Prof) that combines a search-based heuristic with contrast data mining of execution traces to accurately determine performance bottlenecks. We evaluated GA-Prof to determine how effectively and efficiently it can detect injected performance bottlenecks into three popular open source web applications. Our results demonstrate that GA-Prof efficiently explores a large space of input value combinations while automatically and accurately detecting performance bottlenecks, thus suggesting that it is effective for automatic profiling.

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Section: Subject Applicationsmentioning

confidence: 99%

Automating performance bottleneck detection using search-based application profiling

Shen

Luo

Poshyvanyk

et al. 2015

Proceedings of the 2015 International Symposium on Software Testing and Analysis

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“…We selected JPetStore as the cloud application to test in our experiment as it has been widely used as a representative Web application that produces a transactional workload. We modelled the workload of JPetStore using StressCloud based on the client load model introduced by Cai [26]. We profiled and analysed the system performance and energy consumption with different client load models and different resource allocation and deployment strategies in test suites I and II presented in Section 5.5.…”

Section: Mixedmentioning

confidence: 99%

“…JPetStore was selected as the cloud application to test in our experiment as it has been widely used as a representative Web application that produces a transactional workload. We modelled the workload of JPetStore using StressCloud based on the client load model introduced by Cai [26] and shown in Section 3.…”

Section: Mixed Computation- Data-and Communication-intensive Workloadsmentioning

confidence: 99%

Automated analysis of performance and energy consumption for cloud applications

Chen

Grundy

Schneider

et al. 2014

Proceedings of the 5th ACM/SPEC International Conference on Performance Engineering

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In cloud environments, IT solutions are delivered to users via shared infrastructure. One consequence of this model is that large cloud data centres consume large amounts of energy and produce significant carbon footprints. A key objective of cloud providers is thus to develop resource provisioning and management solutions at minimum energy consumption while still guaranteeing Service Level Agreements (SLAs). However, a thorough understanding of both system performance and energy consumption patterns in complex cloud systems is imperative to achieve a balance of energy efficiency and acceptable performance. In this paper, we present StressCloud, a performance and energy consumption analysis tool for cloud systems. StressCloud can automatically generate load tests and profile system performance and energy consumption data. Using StressCloud, we have conducted extensive experiments to profile and analyse system performance and energy consumption with different types and mixes of runtime tasks. We collected finegrained energy consumption and performance data with different resource allocation strategies, system configurations and workloads. The experimental results show the correlation coefficients of energy consumption, system resource allocation strategies and workload, as well as the performance of the cloud applications. Our results can be used to guide the design and deployment of cloud applications to balance energy and performance requirements.

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“…Much of the work in load testing has focused on the automatic generation of load test cases [7,8,10,18,48]. A survey of load testing (and load test cases) may be found in [27].…”

Section: Load Test Case Designmentioning

confidence: 99%

Continuous validation of load test suites

Syer

Jiang

Nagappan

et al. 2014

Proceedings of the 5th ACM/SPEC International Conference on Performance Engineering

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Ultra-Large-Scale (ULS) systems face continuously evolving field workloads in terms of activated/disabled feature sets, varying usage patterns and changing deployment configurations. These evolving workloads often have a large impact on the performance of a ULS system. Hence, continuous load testing is critical to ensuring the error-free operation of such systems. A common challenge facing performance analysts is to validate if a load test closely resembles the current field workloads. Such validation may be performed by comparing execution logs from the load test and the field. However, the size and unstructured nature of execution logs makes such a comparison unfeasible without automated support. In this paper, we propose an automated approach to validate whether a load test resembles the field workload and, if not, determines how they differ by compare execution logs from a load test and the field. Performance analysts can then update their load test cases to eliminate such differences, hence creating more realistic load test cases. We perform three case studies on two large systems: one open-source system and one enterprise system. Our approach identifies differences between load tests and the field with a precision of ≥75% compared to only ≥16% for the state-of-the-practice.

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Synthesizing client load models for performance engineering via web crawling

Cited by 15 publications

References 19 publications

Automating performance bottleneck detection using search-based application profiling

Automating performance bottleneck detection using search-based application profiling

Automated analysis of performance and energy consumption for cloud applications

Continuous validation of load test suites

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