Cloud Resource Allocation from the User Perspective: A Bare-Bones Reinforcement Learning Approach
| Autor: | Verena Kantere, Alexandros Kontarinis, Nectarios Koziris |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Computer science
Process (engineering) business.industry Distributed computing Q-learning 020206 networking & telecommunications Cloud computing 02 engineering and technology Task (project management) Resource (project management) 0202 electrical engineering electronic engineering information engineering Reinforcement learning Resource allocation 020201 artificial intelligence & image processing Resource management business |
| Zdroj: | Web Information Systems Engineering – WISE 2016 ISBN: 9783319487397 WISE (1) |
| DOI: | 10.1007/978-3-319-48740-3_34 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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