Predictive Probabilistic Resource Availability based Cloud Workflow Scheduling (PPRA)

Abstract

Cloud Computing provides access to a shared pool of computing resources such as servers, storage, computer networks and services, which can be rapidly provisioned and released, for the execution of various scientific and business applications. Scheduling scientific workflows modeled by Directed Acyclic Graphs is an NP complete problem. In cloud environment, there are fluctuations in resource availability due to shared resources and vastly varying workloads. The performance variations in virtual machines, have an impact on task execution times and data transfer times. This has a great impact on the performance of scheduling algorithms. Schedulers should map tasks to cloud resources considering the effective utilization of underlying resources. We propose a new static workflow scheduling algorithm called Predictive Probabilistic Resource Availability based Cloud Workflow Scheduling (PPRA) with the objective of minimizing makespan considering the probability of resource availability of cloud resources. This algorithm is compared with existing algorithms which assume full resource availability while making scheduling decisions. The proposed algorithm is found to be more reliable and performing better than existing HEFT and ECTS algorithms, in terms of minimized makespan.