摘要：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.
