A hyper-heuristic selector algorithm for cloud computing scheduling based on workflow features

Abstract

This study focuses on the presentation of a new algorithm for scheduling workflows on heterogeneous distributed systems such as cloud computing. Since heterogeneous distributed systems deal with different types of resources, scheduling of applications on cloud resources plays an important role in the computing environment. Due to being heterogeneous and dynamic properties of resources as well as large numbers of tasks with different characteristics and dependencies among tasks, scheduling tasks on cloud computing is referred to as an NP-hard problem. Heuristic methods are one of the common approaches to solve this problem. Heuristic algorithms according to the specifications of resources and workflow structure could be superior to the rule-based methods. However, it is difficult to define which heuristic algorithm is performed better than the rest. Therefore, the choice of appropriate heuristic algorithms based on the circumstances can be effective. Moreover, the hyper-heuristic algorithm obtains higher performance. In this study, a new method is presented to improve the Hyper-Heuristic Scheduling Algorithm for the cloud using the decision tree method to select a convenient heuristic algorithm based on the characteristics of resources and workflows by considering evaluation criteria such as cost and Makespan. Finally, the presented algorithm is evaluated by Workflowsim and using RapidMiner. The simulation results demonstrate that our proposed algorithm outperforms existing approaches in terms of Makespan and Accuracy.