Design and Analysis of an Adjustable and Configurable Bio-inspired Heuristic Scheduling Technique for Cloud Based Systems

FieldValue
dc.contributor.authorAl Buhussain, Ali
dc.date.accessioned2016-05-26T17:48:26Z
dc.date.available2016-05-26T17:48:26Z
dc.date.issued2016
dc.identifier.urihttp://hdl.handle.net/10393/34794
dc.identifier.urihttp://dx.doi.org/10.20381/ruor-6036
dc.description.abstractCloud computing environments mainly focus on the delivery of resources, platforms, and infrastructure as services to users over the Internet. More specifically, Cloud promises user access to a scalable amount of resources, making use of the elasticity on the provisioning of recourses by scaling them up and down depending on the demand. The cloud technology has gained popularity in recent years as the next big step in the IT industry. The number of users of Cloud services has been increasing steadily, so the need for efficient task scheduling is crucial for improving and maintaining performance. Moreover, those users have different SLAs that imposes different demands on the cloud system. In this particular case, a scheduler is responsible for assigning tasks to virtual machines in an effective and efficient matter to meet with the QoS promised to users. The scheduler needs to adapt to changes in the cloud environment along with defined demand requirements. Hence, an Adjustable and Configurable bio-inspired scheduling heuristic for cloud based systems (ACBH) is suggested. We also present an extensively comparative performance study on bio-inspired scheduling algorithms namely Ant Colony Optimization (ACO) and Honey Bee Optimization (HBO). Furthermore, a networking scheduling algorithm is also evaluated, which comprises Random Biased Sampling (RBS). The study of bio-inspired techniques concluded that all the bio-inspired algorithms follow the same flow that was later used in the development of (ACBH). The experimental results have shown that ACBH has a 90% better execution time that it closest rival which is ACO. ACBH has a better performance in terms of the fairness between execution time differences between tasks. HBO shows better scheduling when the objective consists mainly of costs. However, when there is multiple optimization objectives ACBH performs the best due to its configurability and adaptability.
dc.language.isoen
dc.publisherUniversité d'Ottawa / University of Ottawa
dc.subjectCloud Computing
dc.subjectScheduling
dc.subjectOptimization
dc.subjectBio-Inspired
dc.titleDesign and Analysis of an Adjustable and Configurable Bio-inspired Heuristic Scheduling Technique for Cloud Based Systems
dc.typeThesis
dc.contributor.supervisorBoukerche, Azzedine
thesis.degree.nameMASc
thesis.degree.levelMasters
thesis.degree.disciplineGénie / Engineering
uottawa.departmentScience informatique et génie électrique / Electrical Engineering and Computer Science
CollectionThèses, 2011 - // Theses, 2011 -

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