Bi-Objective Optimizing for Data-Intensive ‎Scientific Workflow Scheduling in Cloud ‎Computing

Abstract

Cloud Computing is increasingly recognized as a new way to use on-demand, computing, ‎storage and network services in a transparent and efficient way. Cloud Computing environment ‎consists of large customers requesting for cloud resources. Nowadays, task scheduling problem ‎and data placement are the current research topic in cloud computing. In this work, a new ‎technique for workflow scheduling and data placement are proposed based on genetic ‎algorithm to fulfill a final bi-objective goal such as minimizing total workflow response time and ‎cost of their execution. the scheduling of scientific workflows is considered to be an NP-complete ‎problem, i.e. a problem not solvable within polynomial time with current resources. The ‎performance of this proposed algorithm has been evaluated using CloudSim toolkit, Simulation ‎results show the effectiveness of the proposed algorithm in comparison with well-known ‎algorithms such as genetic algorithm with Random data placement. ‎