A hybrid meta-heuristic algorithm for scientific workflow scheduling in heterogeneous distributed computing systems

Cloud computing has attracted great attentions in research community because of its ubiquitous, unlimited computing resources, low cost, and flexibility owing to virtualization technology. This paper presents a hybrid meta-heuristic algorithm to solve parallelizable scientific workflows on elastic cloud platforms since applying a single approach cannot yield optimal solution in such complicated problems. Scientific workflows are modeled in the form of directed acyclic graph (DAG) in which there exists data dependency between sub-tasks. In the cloud marketplace, each provider delivers variable virtual machine (VM) configurations which lead different performance. Generally, parallelizable task scheduling on parallel computing machines to obtain minimum total execution time, makespan, belongs to NP-Hard problem. To deal with the combinatorial problem, the hybrid discrete particle swarm optimization (HDPSO) algorithm is presented that has three main phases. At the first phase a random algorithm following by novel theorems is applied to produce swarm members; it is as input of presented new discrete particle swarm optimization (DPSO) algorithm in the second phase. To avoid getting stuck in sub-optimal trap and to balance between exploration and exploitation, local search improvement is randomly combined in DPSO by calling Hill Climbing technique at the third phase to enhance overall performance. Second and third phases are iterated till the termination criteria is met. The average results reported from different executions of intensive settings on 12 scientific datasets proved our hybrid meta-heuristic has the amount of 10.67, 14.48, and 3 percentage dominance in terms of SLR, SpeedUp, and efficiency respectively against other existing meta-heuristics.