Performance Analysis of Cloud Resource Provisioning Algorithms

Kukreja, Shilpa; Dalal, Surjeet

doi:10.1007/978-981-10-6872-0_57

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2023

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Cited by 4 publications

(2 citation statements)

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“…The CPU of each VM was loaded to a specified percentage by using the stress-ng library. 3 With respect to monitoring, the Ceilometer 4 and Gnocchi 4.0 5 plugins were employed. The former tracks VM parameters within user-defined time intervals to measure r t at O time stamps.…”

Section: Methodsmentioning

confidence: 99%

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VM Reservation Plan Adaptation Using Machine Learning in Cloud Computing

et al. 2019

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In this paper we propose a novel reservation plan adaptation system based on machine learning. In the context of cloud auto-scaling, an important issue is the ability to define and use a resource reservation plan, which enables efficient resource scheduling. If necessary, the plan may allocate new resources upon reservation where a sufficient amount of resources is available. Our solution allows the updating of a reservation plan initially prepared by an administra

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Section: Methodsmentioning

confidence: 99%

“…However, such planning is complex and very imprecise because it requires knowledge about future system load and accurate predictions of future demand. Therefore, many researchers [3,4] are working on methods which enable the automatic development of such plans and adapting them to new operating conditions.…”

Section: Introductionmentioning

confidence: 99%