Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/576615
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dc.date.accessioned2024-07-12T08:43:52Z-
dc.date.available2024-07-12T08:43:52Z-
dc.identifier.urihttp://hdl.handle.net/10603/576615-
dc.description.abstractNavigating the intricate landscape of contemporary cloud data centers brings forth a newlinecomplex and pivotal challenge: the optimization of hardware maintenance, load newlinebalancing, and the live storage migration of Virtual Machines (VMs). Adaptive MultiAgent Deep-Deterministic Policy Gradient (AMS-DDPG) is a technique introduced in newlinethis study that is part of a comprehensive examination of state-of-the-art approaches. newlineAimed at transcending conventional boundaries, this innovative approach redefines newlinethe paradigms of task allocation and VM migration within the dynamic fabric of cloud newlineenvironments. As we delve into the heart of this exploration, it becomes evident that newlinethe complexities inherent in cloud computing ecosystems demand innovative solutions newlinethat not only address existing challenges but also pave the way for future newlineadvancements. Traditional VM migration methodologies confront formidable newlinechallenges in dynamic cloud settings, particularly when dealing with heavily loaded newlinesource servers during maintenance or upgrades. These methods inadvertently newlineintroduce unwarranted storage and network traffic, triggering a cascade effect that newlinereverberates through source server performance. The discernible degradation in VM newlineefficiency not only impacts the individual VMs but also exerts a significant influence newlineon the overall performance of associated applications. Recognizing these challenges, newlinethis study goes beyond mere acknowledgment to emphasize the pressing need for newlineinnovative solutions that enhance the adaptability and efficiency of VM migration newlinestrategies. This need is underscored by the escalating demands placed on newlinecontemporary cloud computing environments. Effective cloud infrastructure newlinemanagement necessitates holistic solutions that transcend isolated challenges. newlineAddressing issues related to VM failures, power management, maintenance, and load newlinebalancing requires a comprehensive and integrated approach. The migration of VMs newlineacross data centers assumes a heightened significance, especially in federated syst newline
dc.format.extentviii, 126p.
dc.languageEnglish
dc.relation
dc.rightsuniversity
dc.titleMigration Techniques for Virtual Machine Distribution using Machine Learning
dc.title.alternative
dc.creator.researcherPanesar, Gurpreet Singh
dc.subject.keywordComputer Science
dc.subject.keywordComputer Science Artificial Intelligence
dc.subject.keywordEngineering and Technology
dc.description.note
dc.contributor.guideChadha, Raman
dc.publisher.placeMohali
dc.publisher.universityChandigarh University
dc.publisher.institutionDepartment of Computer Science Engineering
dc.date.registered2021
dc.date.completed2023
dc.date.awarded2023
dc.format.dimensions24cm.
dc.format.accompanyingmaterialNone
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Department of Computer Science Engineering

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01_title.pdfAttached File65.3 kBAdobe PDFView/Open
02_prelim pages.pdf1.82 MBAdobe PDFView/Open
03_content.pdf45.7 kBAdobe PDFView/Open
04_abstract.pdf82.25 kBAdobe PDFView/Open
05_chapter 1.pdf327.91 kBAdobe PDFView/Open
06_chapter 2.pdf266.9 kBAdobe PDFView/Open
07_chapter 3.pdf381.22 kBAdobe PDFView/Open
08_chapter 4.pdf477.9 kBAdobe PDFView/Open
09_chapter 5.pdf1.11 MBAdobe PDFView/Open
10_chapter 6.pdf28.8 kBAdobe PDFView/Open
11_annexures.pdf139.57 kBAdobe PDFView/Open
80_recommendation.pdf92.84 kBAdobe PDFView/Open


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