Please use this identifier to cite or link to this item:
Title: Machine Learning Approaches for Resource Allocation in the Cloud: Critical Reflections
Authors: Murali, A.
Das, N.N.
Sukumaran, S.S.
Chandrasekaran, K.
Joseph, C.
Martin, J.P.
Issue Date: 2018
Citation: 2018 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2018, 2018, Vol., , pp.2073-2079
Abstract: Resource Allocation is the effective and efficient use of a Cloud's resources and is a very challenging problem in cloud environments. Many attempts have been made to make Resource Allocation automated and optimal in terms of profit. The best of these methods used Machine Learning, but this comes with an overhead for computation. A lot of research has been done in this domain to find more efficient methods. Distributed Neural Networks (DNN) is the future of computation and will soon be used to make the computation of large-scale data faster and easier. DNN is currently the most researched area. This paper will summarize the major research works in these fields. A new taxonomy is proposed and can be used as a reference for all future research in this domain. The paper also proposes some areas that need more research in the foreseeable future. � 2018 IEEE.
Appears in Collections:2. Conference Papers

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.