Workflow scheduling in cloud computing environment using Cat Swarm Optimization

Abstract

Cloud computing is new era of network based computing, where resources are distributed over the network and shared among its users. Any user can use these resources through internet on the basis of Pay-As-Per-Use system. A service used by any user can produce a very large amount of data. So in this case, the data transfer cost between two dependent resources will be very high. In addition, a complex application can have a large number of tasks which may cause an increase in total cost of execution of that application, if not scheduled in an optimized way. So to overcome these problems, the authors present a Cat Swarm Optimization (CSO) - based heuristic scheduling algorithm to schedule the tasks of an application onto available resources. The CSO heuristic algorithm considers both data transmission cost between two dependent resources and execution cost of tasks on different resources. The authors experiment with the proposed CSO algorithm using a hypothetical workflow and compare the workflow scheduling results with the existing Particle Swarm Optimization (PSO) algorithm. The experimental results show - (1) CSO gives an optimal task-to-resource (TOR) scheduling scheme that minimizes the total cost, (2) CSO shows an improvement over existing PSO in terms of number of iterations, and (3) CSO ensures fair load distribution on the available resources.