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Energy-aware workload management for geographically distributed data centers

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dc.contributor.authorHogade, Ninad, author
dc.contributor.authorPasricha, Sudeep, advisor
dc.contributor.authorSiegel, Howard Jay, committee member
dc.contributor.authorMaciejewski, Anthony, committee member
dc.contributor.authorAnderson, Chuck, committee member
dc.date.accessioned2024-01-01T11:25:12Z
dc.date.available2024-12-29
dc.date.issued2023
dc.description.abstractCloud service providers are distributing data centers globally to reduce operating costs while also improving the quality of service by using intelligent cloud management strategies. The development of time-of-use electricity pricing and renewable energy source models has provided the means to reduce high cloud operating costs through intelligent geographical workload distribution. However, neglecting essential considerations such as data center cooling power, interference effects from workload co-location in servers, net-metering, peak demand pricing of electricity, data transfer costs, and data center queueing delay has led to sub-optimal results in prior work because these factors have a significant impact on cloud operating costs, performance, and carbon emissions. This dissertation presents a series of critical research studies addressing the vital issues of energy efficiency, carbon emissions reductions, and operating cost optimization in geographically distributed data centers. It scrutinizes different approaches to workload management, considering the diverse, dynamic, and complex nature of these environments. Starting from an exploration of energy cost minimization through sophisticated workload management techniques, the research extends to integrate network awareness into the problem, acknowledging data transfer costs and queuing delays. These works employ mathematical and game theoretic optimization to find effective solutions. Subsequently, a comprehensive survey of state-of-the-art Machine Learning (ML) techniques utilized in cloud management is discussed. Then, the dissertation traverses into the realm of Deep Reinforcement Learning (DRL) based optimization for efficient management of cloud resources and workloads. Finally, the study culminates in a novel game-theoretic DRL method, incorporating non-cooperative game theory principles to optimize the distribution of AI workloads, considering energy costs, data transfer costs, and carbon footprints. The dissertation holds significant implications for sustainable and cost-effective cloud data center workload management.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.identifierHogade_colostate_0053A_18017.pdf
dc.identifier.urihttps://hdl.handle.net/10217/237404
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2020-
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.rights.accessEmbargo expires: 12/29/2024.
dc.subjectdata centers
dc.subjectgeo-distributed
dc.subjectreinforcement learning
dc.subjectgame theory
dc.subjectcloud
dc.subjectoptimization
dc.titleEnergy-aware workload management for geographically distributed data centers
dc.typeText
dcterms.embargo.expires2024-12-29
dcterms.embargo.terms2024-12-29
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineElectrical and Computer Engineering
thesis.degree.grantorColorado State University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)

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