Experimental evaluation of N-tier systems: Observation and analysis of multi-bottlenecks

Malkowski, Simon; Hedwig, Markus; Pu, Calton

doi:10.1109/iiswc.2009.5306791

Cited by 43 publications

(30 citation statements)

References 17 publications

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“…We focus our discussion on statistical and learning techniques due to the volume of literature on them. However, signal processing methods such as Extended Window Averaging, Adaptive Filtering and Fourier Transforms have also been used in [ [30]; [16]]. We describe the commonly used techniques in literatures along with relevant references in sections 3.2.1 and 3.2.2.…”

Section: Detection Methodsmentioning

confidence: 99%

“…An inherent characteristic of the bottleneck component is a near-linear load-dependent growth in resource usage. [16].…”

Section: Bottlenecks Behavioursmentioning

confidence: 99%

“…Two or more system resources or component may saturate simultaneously, or concurrently due to interdependency. Malkowski et al [16] classifies multiple saturation behaviours as simultaneous, oscillatory, and concurrent depending on saturation frequency given the presence of another saturation.…”

Section: Bottlenecks Behavioursmentioning

confidence: 99%

“…This period witnessed the development of analytical approaches and tools such as; transaction mix models [134]; queuing-theoretic models [21]; signature models [ [76]; [31]] and statistical techniques [[43]; [2]]. While e↵orts such as in [[49]; [16]] propose an experimental approach, [ [44]; [50]] demonstrate the potential of analyzing the flow of messages across distributed components as a suitable method for detecting performance abnormalities.…”

Section: Research Trendsmentioning

confidence: 99%

“…Malkowski et al [16] describes a bottleneck component as a potential rootcause of undesirable performance behaviour caused by a limitation (e.g. saturation) of some major system resources associated with the component [17].…”

Section: Performance Bottlenecksmentioning

confidence: 99%

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Performance Anomaly Detection and Bottleneck Identification

2015

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In order to meet stringent performance requirements, system administrators must e↵ectively detect undesirable performance behaviours, identify potential root causes and take adequate corrective measures. The problem of uncovering and understanding performance anomalies and their causes (bottlenecks) in di↵erent system and application domains is well studied. In order to assess progress, research trends and identify open challenges, we have reviewed major contributions in the area and present our findings in this survey. Our approach provides an overview of anomaly detection and bottleneck identification research as it relates to the performance of computing systems. By identifying fundamental elements of the problem, we are able to categorize existing solutions based on multiple factors such as the detection goals, nature of applications and systems, system observability, and detection methods.

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Section: Detection Methodsmentioning

confidence: 99%

“…An inherent characteristic of the bottleneck component is a near-linear load-dependent growth in resource usage. [16].…”

Section: Bottlenecks Behavioursmentioning

confidence: 99%

Section: Bottlenecks Behavioursmentioning

confidence: 99%

Section: Research Trendsmentioning

confidence: 99%

Section: Performance Bottlenecksmentioning

confidence: 99%

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Performance Anomaly Detection and Bottleneck Identification

2015

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Web workload generation challenges – an empirical investigation

2011

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SUMMARY Workload generators are widely used for testing the performance of Web‐based systems. Typically, these tools are also used to collect measurements such as throughput and end‐user response times that are often used to characterize the QoS provided by a system to its users. However, our study finds that Web workload generation is more difficult than it seems. In examining the popular RUBiS client generator, we found that reported response times could be grossly inaccurate, and that the generated workloads were less realistic than expected, causing server scalability to be incorrectly estimated. Using experimentation, we demonstrate how the Java virtual machine and the Java network library are the root causes of these issues. Our work serves as an example of how to verify the behavior of a Web workload generator. Copyright © 2011 John Wiley & Sons, Ltd.

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