A novel hybrid heuristic-based list scheduling algorithm in heterogeneous cloud computing environment for makespan optimization

An efficient workflow scheduling can potentially exploit heterogeneity of resources in heterogeneous cloud computing (HCC) platform commensurate with variable requirement of dependent tasks in a given workflow. Minimizing the total scheduling length, makespan, is essential for application performance in heterogeneous computing systems especially in cloud computing environment. The problem of scheduling a set of different dependent tasks onto a set of heterogeneous computational resources is a well-known NP-Hard problem. Therefore, no polynomial scheduling algorithm for computing the optimal solution exists. For approximating a solution to this problem many algorithms have been proposed, but majority of them have low efficiency. In this paper, a novel hybrid heuristic-based list scheduling (HH-LiSch) algorithm is presented for solving the dependent task scheduling in HCC systems in a bounded number of the fully connected virtual machines (VMs). The novelty of the current paper is to present the new task priority strategy, find appropriate VM's slot time, and utilize task duplication technique. Two novel task priority strategies are applied to prioritize tasks in an efficient ordered list. Then, during the scheduling process an insertion-based procedure is called to find an appropriate potential slot time for performing task duplication technique. If it works, the task duplication is added to rudimentary scheduling scheme. In this way, the final scheduling is gradually generated. To validate the work, the experiments are based on six real-world scientific workflows and a random task graph (RTG); then, the performance is evaluated in terms of makespan, Schedule Length Ratio (SLR), speedup and efficiency. The simulation results prove a significant improvement against other counterparts in literature.