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Energy-Efficient and Cost-Effective Resource Management for Geo Distributed Data Centers

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dc.contributor.author Than, Moh Moh
dc.date.accessioned 2022-04-07T05:32:23Z
dc.date.available 2022-04-07T05:32:23Z
dc.date.issued 2021-02-01
dc.identifier.uri https://onlineresource.ucsy.edu.mm/handle/123456789/2595
dc.description.abstract Cloud computing allows services available to customers on a pay-as-you-go model through geographically distributed data centers (GDCs) located around the world. Almost all Internet services are built on GDCs in order to improve performance and reliability. As cloud computing grows in popularity, so does energy consumption and the cost of powering servers in GDCs. Energy and cost reduction have emerged as major challenges for GDCs. In this thesis, energy-efficient and cost-effective resource management framework is proposed for minimizing the operational cost and energy consumption of servers, and ensuring the service level objective (SLO). The framework accomplishes electricity price prediction, resource demand prediction, ensuring SLO as well as energy-efficient and cost-effective resource allocation. Electricity prices of GDCs in multi-regional electricity markets are predicted. Resource demand prediction is performed by using ML techniques for handling dynamic workload nature of the resource amount needed in data centers. Ensuring SLO is conducted to the resource demand prediction system, to ensure the cloud providers have enough resources to meet the resource demand. Energy efficiency factors: allocation policies, power management techniques as well as power models are considered to propose energy-saving resource allocation algorithms for energy-efficient allocation of resources. Energy costs are minimized by delivering the requests first to the data center with cheaper electricity price. Extensive evaluations are carried out using real-world electricity price data of GDC locations and real-world workload traces. According to the evaluation results, the electricity price prediction model has a promising accuracy. The resource demand prediction model predicts the accurate amount of dynamic resource demand while meeting SLO. CloudSim is used to evaluate the performance of the proposed resource allocation algorithms. This work contributes to reducing energy consumption and it also offers cost-saving. en_US
dc.description.sponsorship University of Computer Studies, Yangon en_US
dc.language.iso en en_US
dc.publisher University of Computer Studies, Yangon en_US
dc.subject Energy-Efficient and Cost-Effective Resource Management en_US
dc.subject Geo-Distributed Data Centers en_US
dc.title Energy-Efficient and Cost-Effective Resource Management for Geo Distributed Data Centers en_US
dc.type Thesis en_US

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