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2016 | OriginalPaper | Buchkapitel

Cloud Resource Allocation from the User Perspective: A Bare-Bones Reinforcement Learning Approach

verfasst von : Alexandros Kontarinis, Verena Kantere, Nectarios Koziris

Erschienen in: Web Information Systems Engineering – WISE 2016

Verlag: Springer International Publishing

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Abstract

Cloud computing enables effortless access to a seemingly infinite shared pool of resources, on a pay-per-use basis. As a result, a new challenge has emerged: designing control mechanisms to precisely meet the actual workload requirements of cloud applications in an online manner. To this end, a variety of complex resource management issues have to be addressed, because workloads in the cloud are of a dynamic and heterogeneous nature, and traditional algorithms do not cope well within this context. In this work, we adopt the point of view of the user of a cloud infrastructure and focus on the task of controlling leased resources. We formulate this task as a Reinforcement Learning problem and we simulate the decision-making process of a controller implementing the Q-learning algorithm. We conduct an experimental study, the outcomes of which offer valuable insight into the advantages and shortcomings of using Reinforcement Learning to implement such adaptive cloud resource controllers.

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Vorheriges Kapitel A Scalable Parallel Semantic Reasoning Algorithm-Based on RDFS Rules on Hadoop

Nächstes Kapitel Event Phase Extraction and Summarization

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Optimality arguments explain empirical regularities through objective maximization.

\(-2\) for the two unavailable actions.

A Broker allocates resources from multiple cloud providers.

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Titel: Cloud Resource Allocation from the User Perspective: A Bare-Bones Reinforcement Learning Approach
verfasst von: Alexandros Kontarinis
Verena Kantere
Nectarios Koziris
Verlag: Springer International Publishing
Buch: Web Information Systems Engineering – WISE 2016
Print ISBN: 978-3-319-48739-7

Electronic ISBN: 978-3-319-48740-3

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-48740-3_34

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