{"id":{"repo_id":"athabasca","oai_identifier":"oai:dt.athabascau.ca:10791/269"},"canonical_url":"https://search.dev.ndltd.org/etd/athabasca/oai:dt.athabascau.ca:10791/269","repository":{"repo_id":"athabasca","name":"Athabasca University","base_url":"https://dt.athabascau.ca/oai/request"},"display":{"title":"A Predictive Workload Balancing Algorithm in Cloud Services","abstract":"2018-August","abstract_html":"2018-August","abstract_has_math":false,"creators":["Jodayree, Mahdee"],"institution":"Athabasca University","degree_name":"Master of Science, Information Systems (MScIS)","degree_level":"master's","degree_discipline":"Faculty of Science and Technology","degree_department":null,"school":null,"contributors":["Dr. Mahmoud Abaza, Faculty of Science and Technology, Athabasca University (Supervisor)","Dr. Ching Tan, Faculty of Science and Technology, Athabasca University (Internal Committee Member)","Dr. Ebrahim Bagheri, Electrical and Computer Engineering, Ryerson University (External Examiner)"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-09-07","date_published":"2018-09-07","updated_at":"2026-08-21T16:41:56Z","subjects":["Predictive Workload Balancing Algorithm in Cloud Services","Predictive Workload Balancing","CloudSim"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["TC-AEAU-269"],"render_values":[{"text":"TC-AEAU-269","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10791/269","outbound_label":"Handle","outbound_source":"dc:identifier"},"source_record":{"url":"https://dt.athabascau.ca/oai/request?verb=GetRecord&metadataPrefix=oai_etdms&identifier=oai%3Adt.athabascau.ca%3A10791%2F269","prefix":"oai_etdms"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Dr. Mahmoud Abaza, Faculty of Science and Technology, Athabasca University (Supervisor)","Dr. Ching Tan, Faculty of Science and Technology, Athabasca University (Internal Committee Member)","Dr. Ebrahim Bagheri, Electrical and Computer Engineering, Ryerson University (External Examiner)"]},{"key":"dc:creator","label":"Author","values":["Jodayree, Mahdee"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-09-07"]},{"key":"dc:publisher","label":"Institution","values":["Athabasca University"]},{"key":"dc:type","label":"Dc Type","values":["Electronic Thesis or Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Faculty of Science and Technology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["master's"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science, Information Systems (MScIS)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Athabasca University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Predictive Workload Balancing Algorithm in Cloud Services","Predictive Workload Balancing","CloudSim"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/10791/269","https://dt.athabascau.ca/jspui/bitstream/10791/269/5/Thesis-Final-Submission-2018.pdf","TC-AEAU-269"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["2018-August","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."]},{"key":"dc:title","label":"Title","values":["A Predictive Workload Balancing Algorithm in Cloud Services"]}]}],"canonical_facts":{"dc:contributor":["Dr. Mahmoud Abaza, Faculty of Science and Technology, Athabasca University (Supervisor)","Dr. Ching Tan, Faculty of Science and Technology, Athabasca University (Internal Committee Member)","Dr. Ebrahim Bagheri, Electrical and Computer Engineering, Ryerson University (External Examiner)"],"dc:creator":["Jodayree, Mahdee"],"dc:date":["2018-09-07"],"dc:description":["2018-August","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."],"dc:identifier":["http://hdl.handle.net/10791/269","https://dt.athabascau.ca/jspui/bitstream/10791/269/5/Thesis-Final-Submission-2018.pdf","TC-AEAU-269"],"dc:publisher":["Athabasca University"],"dc:subject":["Predictive Workload Balancing Algorithm in Cloud Services","Predictive Workload Balancing","CloudSim"],"dc:title":["A Predictive Workload Balancing Algorithm in Cloud Services"],"dc:type":["Electronic Thesis or Dissertation"],"thesis:degree_discipline":["Faculty of Science and Technology"],"thesis:degree_level":["master's"],"thesis:degree_name":["Master of Science, Information Systems (MScIS)"],"thesis:institution_name":["Athabasca University"]},"updated_at":"2026-08-21T16:41:56Z"}