Reactive Prediction Models for Cloud Resource Estimation
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The main objective of this thesis is to propose a new reactive resource estimation model to achieve more reasonable resource allocation and higher server utilization for cloud providers. The model is flexible enough to adapt to different situations given the current server utilization, the customer loyalty, the price of the service, etc. More precisely, four mathematical models are first proposed to deal with different situations. Then, a reactive model combining these four models is introduced. Simulations based on CloudSim are designed and implemented. The simulation results for all models meet the expectations derived from the mathematical analysis. Finally, the resource utilization of the combined model is improved by 25% in a real-time simulation compared to previous work.
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Copyright © 2016 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
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- 2016
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hu-reactivepredictionmodelsforcloudresourceestimation.pdf | 2023-05-04 | Public | Download |