Cost Effective Expa-Max-Min Scientific Workflow Allocation and Load Balancing Strategy in Cloud Computing

Abstract

The rise in demand for cloud resources (network, hardware and software) requires cost effective scientific workflow scheduling algorithm to reduce cost and balance load of all jobs evenly for a better system throughput. Getting multiple scientific workflows scheduled with a reduced makespan and cost in a dynamic cloud computing environment is an attractive research area which needs more attention. Scheduling multiple workflows with the standard Max-Min algorithm is a challenge because of the high priority given to task with maximum execution time first. To overcome this challenge, we proposed a new mechanism call Expanded Max-Min (Expa-Max-Min) algorithm to effectively give equal opportunity to both cloudlets with maximum and minimum execution time to be scheduled for a reduce cost and time. Expa-Max-Min algorithm first calculates the completion time of all the cloudlets in the cloudletList to find cloudlets with minimum and maximum execution time, then it sorts and queue the cloudlets in two queues based on their execution times. The algorithm first select a cloudlet from the cloudletList in the maximum execution time queue and assign it to a resource that produces minimum completion time, while executing cloudlets in the minimum execution time queue concurrently. The experimented results demonstrats that our proposed algorithm, Expa-Max-Min algorithm, is able to produce good quality solutions in terms of minimising average cost and makespan and able to balance loads than Max-Min and Min-Min algorithms.