COMPARATIVE ANALYSIS OF PSO-DERIVED WORKFLOW SCHEDULING ALGORITHMS IN CLOUD COMPUTING BASED ON QOS REQUIREMENTS

Cloud computing is an advanced technique involving networks of servers that run on a huge amount of data. These servers and other sources of the cloud can be located in wide remote areas. Different scientific and web applications are representing by using Workflow models in the cloud. Scheduling different workflows in multi-cloud environment is a major issue of concern since they are quite huge and follow specific scientific standards. The need to meet user's Quality of service (QoS) requirements are the other issues in public cloud computing, such as scalability and reliability and as well maximize the rate of resource utilization to end-users. This paper makes a comparison between three Particle Swarm Optimization (PSO) based algorithms in terms of makespan and cost. These algorithms were tested with the same number of virtual machines (VMs) and workflows. This is intended to help users to decide on which of these three algorithms can provide the required QoS for large scientific workflows in infrastructure as a service (IaaS) cloud platform as well as help them map tasks to resources. These algorithms are simulated on different simulation packages and tested with different scientific workflow datasets such as LIGO, Montage, CyberShake and Epigenome. The algorithms considered in this article can effectively distribute tasks to available resources for efficient optimization of makespan and cost. Simulation experiments reveal that ACO-PSO outperforms the basic PSO, C-PSO and PSO-DS in the same working environment.