Cloud resource allocation from the user perspective: A bare-bones reinforcement learning approach

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.