Correlating Discriminative Quality Factors (CDQF) for Optimal Resource Scheduling in Cloud Networks

Abstract

The Correlating Discriminative Quality Factors (CDQF) for Optimal Resource Scheduling in cloud networks has been addressed in this manuscript. It is since the resources under the cloud platform are loosely coupled according to the SLA between the cloud platform and the resource partakers. This enables the possibility of multiple resources from diversified partakers, those intended to accomplish similar services. The resource scheduling intends to select one resource among available resources to accomplish the scheduled task(s). The contemporary contributions related to resource scheduling are specific to traditional QoS factors, including cost, deadline constraints, and power consumption. However, the quality of service is often influenced by the contextual factors of the IAAS. Hence, this manuscript portrayed a novel resource scheduling strategy that orders the resources under the degree of optimality proposed in this manuscript. Unlike traditional resource scheduling methods, this manuscript portrayed a set of context-related factors that are further used to define the heuristic measure called “Degree of Optimality.” The experimental study on the simulated environment elevates the proposal performance advantage as opposed to other existing methods.