Deadline-constrained algorithms for scheduling of bag-of-tasks and workflows in cloud computing environments

Abstract

Cloud computing is an emerging distributed computing paradigm that solves immense scientific applications through distributing computing resources over the Internet. These applications may have a huge number of tasks that may increase their execution costs, if not scheduled appropriately. Thus, scheduling of tasks is one of the key challenges in cloud computing environments. The scheduling problem for Bag-of-tasks (BoT) and workflow applications has been broadly studied, and there exist many algorithms for this in cloud computing. In this paper, we evaluate and compare the performance of four deadline-constrained scheduling algorithms for cloud computing environments in which two are heuristic algorithms, and two are meta-heuristic algorithms. The heuristic algorithms used in this work are IC-PCP, and SCS and meta-heuristic algorithms utilized here are PSO and CSO. The algorithms aim to minimize the makespan and execution cost of BoT and workflow applications while achieving deadline constraints. For performance estimation and comparison of algorithms, we used three categories of BoT as small, medium and, large and two real-world applications for workflows for instance Montage and CyberShake. The results illustrate that CSO algorithm performs better than other algorithms for both BoT and workflow applications. © 2018 Association for Computing Machinery.