Improving Real Time Tuning on YARN

Belmonte, Navarro; Pablo, Víctor

doi:10.22215/etd/2018-12663

Cited by 1 publication

(8 citation statements)

References 40 publications

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“…While Hadoop and Spark have hundreds of parameters, only a few dozen of them play a significant role in determining framework performances [5] [6]. Since each parameter has a number of possible values, there are numerous combinations of these parameter values.…”

Section: Motivation and Problem Statementmentioning

confidence: 99%

“…The related concepts and benchmarks are also introduced in this section. 5. Section 6 analyses the experimental data from quantitative and qualitative views.…”

Section: Thesis Contributionsmentioning

confidence: 99%

“…According to past research [5] [6], the values of parameters determine the performance of analytics frameworks. For Hadoop, it has hundreds of parameters, but only dozens of them significantly affect the performance of Hadoop.…”

Section: Parameters Tuningmentioning

confidence: 99%

“…The framework is shut down and new values are set for the parameters in the configuration files. 5. The framework is started and the application is run.…”

Section: Offline Auto-tuningmentioning

confidence: 99%

“…NM monitors the resource utilization in containers. 5. Each container executes one task based on the User-defined code.…”

Section: Introduction To Hadoop and Yarnmentioning

confidence: 99%

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Improving Automatic Tuning of Hadoop and Spark by Analysing Container Performance Metrics

Zhou¹

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This thesis attempts to provide further research on improving automatic tuning of Hadoop and Spark by analysing container performance metrics. Analytics frameworks, for example, Hadoop and Spark, are powerful tools in the study of big data. The parameter values in these analytics frameworks significantly affect the performance of applications. However, it is difficult to select the optimal framework parameter values for every application. Many research teams have proposed methods for the automatic tuning of Hadoop or Spark parameters from different perspectives. Most of the currently existing automatic tuning systems do not support working with different kinds of frameworks and Resource Managers. In addition, research on selecting the best timing to evoke the tuning method is lacking, which is another gap in the study of big data frameworks. Our research aims to fill these gaps. This research introduces novel container performance metrics and proves that these metrics are beneficial in the development of automatic tuning systems. Hadoop and Spark show different patterns in the static and dynamic values of container creation rate, container completion rate, container average response time and relative standard deviation of response-time(RSD). By applying five kinds of machine learning algorithms, container creation rate was found to be the most sensitive metric to identify and classify the workload type at an average accuracy of 83%. RSD can be used to detect workload transitions with an average accuracy of 74%. Our research results will decrease tuning overhead and promote the development of automatic tuning systems.

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Section: Motivation and Problem Statementmentioning

confidence: 99%

“…The related concepts and benchmarks are also introduced in this section. 5. Section 6 analyses the experimental data from quantitative and qualitative views.…”

Section: Thesis Contributionsmentioning

confidence: 99%

Section: Parameters Tuningmentioning

confidence: 99%

“…The framework is shut down and new values are set for the parameters in the configuration files. 5. The framework is started and the application is run.…”

Section: Offline Auto-tuningmentioning

confidence: 99%

“…NM monitors the resource utilization in containers. 5. Each container executes one task based on the User-defined code.…”

Section: Introduction To Hadoop and Yarnmentioning

confidence: 99%

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