Workload Mining in Cloud Computing using Extended Cloud Dempster–Shafer Theory (ECDST)

Abstract

Cloud computing is a growing technology where the resources are provided as a service on demand basis. The services offered are Infrastructure as a Service, Platform as a Service, Software as a Service, Network as a Service etc., Based on the requests or the workloads received from the customer side, the resources are fairly allocated to the cloud customers to complete their jobs in time. As there exists huge volume of resources in cloud computing, plenty of workloads from various users are submitted to the cloud workload analyzer. Identifying and analyzing the huge volume of workloads in the cloud computing environment within a particular time is found to be an important and highly complexity. Hence this paper proposes an Extended Cloud Dempster–Shafer Theory based clustering algorithm for identifying, analyzing, classifying and clustering the workloads efficiently. The experimental result demonstrates that the proposed Extended Cloud Dempster–Shafer Theory based clustering algorithm performs clustering accurately and also reduces the execution time of cloud workloads efficiently by comparing its performance with QoS attribute’s weight based clustering algorithm.