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• W4220708920 abstract "This extended chapter covers multiple research topics marked as (R). Cloud resource management requires complex policies and decisions for multiobjective optimization. Effective resource management is extremely challenging due to the scale of the cloud infrastructure and to the unpredictable interactions of the system with a large population of users. The scale makes it impossible to have accurate global state information, and the large user population makes it nearly impossible to predict the type and the intensity of the system workload. An overview of policies and mechanisms for cloud resource management in Section 9.1 is followed by a discussion of scheduling algorithms for computer clouds in Section 9.2 and by an analysis of delay scheduling and of data-aware scheduling in Sections 9.3 and 9.4, respectively. The Apache capacity scheduler is presented in Section 9.5, and start-time fair queuing and borrowed virtual time-scheduling algorithms are analyzed in Sections 9.6 and 9.7, respectively. Cloud scheduling subject to deadlines and MapReduce scheduling with deadlines are discussed in Sections 9.8 and 9.9, respectively. Resource bundling and combinatorial auctions are covered in Section 9.10. Cloud energy efficiency and cloud resource utilization and the impact of application scaling on resource management are analyzed in Sections 9.11 and 9.12, respectively. A control theoretic approach to resource allocations is discussed in Sections 9.13, 9.14, and 9.15, followed by a machine-learning algorithm for coordination of specialized autonomic performance managers in Section 9.16. A utility model for resource allocation for a web service is then presented in Section 9.17. The cloud infrastructure will most likely continue to scale up for accommodating the demands of an increasingly larger cloud user community. A fair question is whether current cloud management systems can sustain this expansion. Self-organization and self-management alternatives come to mind, but the very slow progress made by the autonomic computing initiative is likely to dampen the enthusiasm of those believing in self-management. We discuss emergence and self-organization in Section 9.18 and conclude this chapter with an in-depth analysis of cloud interoperability in Section 9.19. Sections 9.20 and 9.21 contain further readings and exercises and problems." @default.
• W4220708920 created "2022-04-03" @default.
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• W4220708920 date "2023-01-01" @default.
• W4220708920 modified "2023-09-27" @default.
• W4220708920 title "Cloud resource management and scheduling" @default.
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• W4220708920 doi "https://doi.org/10.1016/b978-0-32-385277-7.00016-6" @default.
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