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Enhancing Static Auto-scaling Approach to Mitigate Resource Over-Provisioning in Cloud Computing

Patil, Manasi (2019) Enhancing Static Auto-scaling Approach to Mitigate Resource Over-Provisioning in Cloud Computing. Masters thesis, Dublin, National College of Ireland.

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Abstract

The elasticity property in cloud computing is favorable for both cloud providers and consumers because of its automatic adaptation to the dynamic workload that the application might experience. There are various approaches for scaling and auto-scalers choose the one to maximize the efficiency based on their application; keeping a balance between SLA violations and the cost of the resources. In almost all the approaches, the efficiency of the auto-scaler is directly proportional to the performance overhead it incurs. Hence, even the most popular approach, which can foresee and prepare itself to adapt to the dynamic workload beforehand, affects the performance of the auto-scaler due to its complex algorithms. This research, thus, focuses on improving a static auto-scaler by mitigating its drawback of resource over-provisioning. Additionally, the proposed solution incurs a negligible performance overhead. The paper introduces an algorithm which leverages the static auto-scaling to provide a solution to avoid over-provisioning. Consequently, the overall cost of the resources used by the application decreases. The results of the empirical evaluation show that the cost of the resources can be decreased by 20-25% depending on the scale of the application.
Keywords: Auto-Scaling, Resource over-provisioning, CloudSim Plus simulation, Cloud Computing, Elasticity, Static Auto-Scaling, MAPE model, Horizontal Scaling

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science

Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software

T Technology > T Technology (General) > Information Technology > Cloud computing
Divisions: School of Computing > Master of Science in Cloud Computing
Depositing User: Dan English
Date Deposited: 04 Jun 2020 12:15
Last Modified: 04 Jun 2020 12:15
URI: http://trap.ncirl.ie/id/eprint/4244

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