An H-CSO Algorithm for Workflow Scheduling in Heterogeneous Cloud Environment

Abstract

Cloud computing technology is becoming popular in both academia and industries these days because of its dynamic and flexible infrastructure which provides good computing facilities through web networks without a place specification. In order to process the workflows, the cloud technology uses datacenters which consist of hosts having various homogeneous and heterogeneous virtual machines. Virtual machines play the role of real machines with multiple operating systems environments. Due to a wide acceptance of the cloud, the load on the cloud is increasing regularly. So, scheduling plays a key role to manage a machine load by mapping several workflows with available virtual machines. The scheduling can be divided into two types: dynamic and static scheduling. Dynamic scheduling can be done effectively by meta-heuristic computing techniques. In the field of cloud computing, many scientists have developed various algorithms for workflow scheduling in order to schedule the workflows. The Cat Swarm Optimization works fine as compared to the Max-Min algorithm, Particle Swarm, and Ant Colony Optimization algorithms. In this research, a new algorithm is designed named H-CSO by using the concept of Heterogeneous Earliest Finish Time and Cat Swarm Optimization algorithms. After experiments, it is found that the proposed H-CSO algorithm gives efficient makespan at realistic costs as matched to the Cat Swarm Optimization. The newly designed H-CSO algorithm is 2.99%, 2.87%, 3.35%, and 5.77% efficient for CyberShake_1000, Montage_1000, Inspiral_1000, and Sipht_1000 datasets respectively as compared to the standard Cat Swarm Optimization in terms of average makespan reduction. In case of cost reduction, the proposed algorithm is 4.03%, 5.12%, 2.42%, and 3.93 effective as compared to the Cat Swarm Optimization for CyberShake_1000, Montage_1000, Inspiral_1000, and Sipht_1000 datasets respectively.