Uncertainty Aware Resource Provisioning Framework for Cloud Using Expected 3-SARSA Learning Agent: NSS and FNSS Based Approach

K. Bhargavi; B. Sathish Babu

doi:10.2478/cait-2019-0028

Abstract

Efficiently provisioning the resources in a large computing domain like cloud is challenging due to uncertainty in resource demands and computation ability of the cloud resources. Inefficient provisioning of the resources leads to several issues in terms of the drop in Quality of Service (QoS), violation of Service Level Agreement (SLA), over-provisioning of resources, under-provisioning of resources and so on. The main objective of the paper is to formulate optimal resource provisioning policies by efficiently handling the uncertainties in the jobs and resources with the application of Neutrosophic Soft-Set (NSS) and Fuzzy Neutrosophic Soft-Set (FNSS). The performance of the proposed work compared to the existing fuzzy auto scaling work achieves the throughput of 80% with the learning rate of 75% on homogeneous and heterogeneous workloads by considering the RUBiS, RUBBoS, and Olio benchmark applications.

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Uncertainty Aware Resource Provisioning Framework for Cloud Using Expected 3-SARSA Learning Agent: NSS and FNSS Based Approach

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