About: Entropy4Cloud: Using Entropy-Based Complexity To Optimize Cloud Service Resource Management

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type	http://eprints.org/ontology/ArticleEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #63355 Entropy4Cloud: Using Entropy-Based Complexity To Optimize Cloud Service Resource Management
sameAs	Entropy4Cloud: Using Entropy-Based Complexity To Optimize Cloud Service Resource Management
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Title	Entropy4Cloud: Using Entropy-Based Complexity To Optimize Cloud Service Resource Management
described by	https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3 https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
Date	2018-01-23
Creator	Frank Z. Wang
status	peer reviewed published
Publisher	IEEE
abstract	In cloud service resource management system, complexity limits the system’s ability to better satisfy the application’s QoS requirements, e.g. cost budget, average response time and reliability. Numerousness, diversity, variety, uncertainty, etc. are some of the complexity factors which lead to the variation between expected plan and actual running performance of cloud applications. In this paper, after defining the complexity clearly, we identify the origin of complexity in cloud service resource management system through the study of ”Local Activity Principle”. In order to manage complexity, an Entropy-based methodology is presented to use which covers identifying, measuring, analysing and controlling (avoid and reduce) of complexity. Finally, we implement such idea in a popular cloud engine, Apache Spark, for running Analysis as a Service (AaaS). Experiments demonstrate that the new, Entropy-based resource management approach can significantly improve the performance of Spark applications. Compare with the Fair Scheduler in Apache Spark, our proposed Entropy Scheduler is able to reduce overall cost by 23%, improve the average service response time by 15% - 20% and minimized the standard deviation of service response time by 30% - 45%.
Is Part Of	IEEE Transactions on Emerging Topics in Computational Intelligence https://kar.kent.ac.uk/id/repository
Subject	QA 75 Electronic computers. Computer science QC20 Mathematical Physics
list of authors	https://kar.kent.ac.uk/id/eprint/63355#authors
issue	1
volume	2
is topic of	https://www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3 https://raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #63355 Entropy4Cloud: Using Entropy-Based Complexity To Optimize Cloud Service Resource Management

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