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dc.contributor.advisorKhademi, Naeem
dc.contributor.authorSabir, Asim
dc.contributor.authorShahid, Ahtsham
dc.date.accessioned2023-08-29T15:51:26Z
dc.date.available2023-08-29T15:51:26Z
dc.date.issued2023
dc.identifierno.uis:inspera:129818259:81197928
dc.identifier.urihttps://hdl.handle.net/11250/3086255
dc.description.abstractAs organizations increasingly adopt hybrid cloud architectures to meet their diverse computing needs, managing workloads across on-premises and on multiple cloud environments has become a critical challenge. This thesis explores the concept of hybrid workload management through the implementation of Azure Arc, a cutting-edge solution offered by Microsoft Azure. The primary objective of this study is to investigate how Azure Arc enables efficient resource utilization and scalability for hybrid workloads. The research methodology involves a comprehensive analysis of the key features and functionalities of Azure Arc, coupled with practical experimentation in a simulated hybrid environment. The thesis begins by examining the fundamental principles of hybrid cloud computing and the associated workload management challenges. It then introduces Azure Arc as a novel approach that extends Azure control to on-premises and multi-cloud systems. The architecture, components, and integration mechanisms of Azure Arc are presented in detail, highlighting its ability to centralize management, enforce governance policies, and streamline operational tasks. This thesis contributes to the understanding of hybrid workload management by exploring the capabilities of Azure Arc. It provides valuable insights into the benefits of adopting this technology for organizations seeking to optimize resource utilization, streamline operations, and scale their workloads efficiently across on-premises and multi-cloud environments. The research findings serve as a foundation for further advancements in hybrid cloud computing and workload management strategies.
dc.description.abstract
dc.languageeng
dc.publisheruis
dc.titleEffective Management of Hybrid Workloads in Public and Private Cloud Platforms
dc.typeMaster thesis


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