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Please use this identifier to cite or link to this item: http://hdl.handle.net/10791/269

Title: A Predictive Workload Balancing Algorithm in Cloud Services
Authors: Jodayree, Mahdee
Supervisor(s): Dr. Mahmoud Abaza, Faculty of Science and Technology, Athabasca University (Supervisor)
Examining Committee: Dr. Ching Tan, Faculty of Science and Technology, Athabasca University (Internal Committee Member)
Dr. Ebrahim Bagheri, Electrical and Computer Engineering, Ryerson University (External Examiner)
Degree: Master of Science, Information Systems (MScIS)
Department: Faculty of Science and Technology
Keywords: Predictive Workload Balancing Algorithm in Cloud Services
Predictive Workload Balancing Algorithm in Cloud Services
Predictive Workload Balancing
Issue Date: 7-Sep-2018
Abstract: In today’s business world, many companies and government agencies depend on the infrastructures of cloud services to host and process their information. Load processing of many cloud services is distributed in a static manner which can overload the largest available systems. This paper is an exploratory study on the predictive approach for dynamic resource distribution of cloud services. Today, many cloud service providers are exploring the benefit of dynamic workload-balancing for their resource management. Rather than issuing fixed resources to each customer, a dynamic hosting alternative offers a way to allocate resources dynamically and more efficiently to save computational power. Efficient cloud resource management can be achieved by simulating cloud services based on the predictions of incoming workloads, which can be more efficient than static allocation methods (Wolke, Bichler, and Setzer, 2015). Previous researchers in this area have focused on dynamic load balancing algorithms that are based on a current workload demanded by a client. These approaches require high computational power and additional time to meet the demands of dynamic cloud services. This paper introduces a rule-based workload-balancing algorithm based on the predictions of an end-to-end system called Cicada. A simulation of cloud services can be achieved by a cloud service simulator called CloudSim and it will be used to achieve an algorithm with lower computational demand and a faster workload balancing. The final result will demonstrate the effectiveness of a predictive workload balancing approach that can achieve faster workload balancing with a lower computational power usage.
Graduation Date: 2018
URI: http://hdl.handle.net/10791/269
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