A Cost Optimization Strategy for Workflow Scheduling in Cloud

Workflow scheduling in cloud is one of the most challenging issues of recent times. It focuses on executing workflow applications in a manner that maps interdependent tasks to virtual machines under specified quality of service requirements. Cloud service providers offer resources with different performance at different prices. The same workflow with different resources can result in different makespan and cost. One of the main problems of workflow scheduling in cloud is to find a cheaper scheduling method on the premise of meeting the deadline. In this paper, an improved L-ACO algorithm (imL-ACO) is adopted. It optimizes the parameters of L-ACO algorithm and can solve the above problem well for specific workflow. The experiment shows that imL-ACO algorithm can make the scheduling cost lower, allocate resources more effectively, and the effect is better than L-ACO algorithm.