A multi-stage dynamic game-theoretic approach for multi-workflow scheduling on heterogeneous virtual machines from multiple infrastructure-as-a-service clouds

Abstract

Distributed computing systems such as clouds continue to evolve to support various types of scientific applications, especially scientific workflows, with dependable, consistent, pervasive, and inexpensive access to geographically-distributed computational capabilities. Scheduling multiple workflows on distributed computing systems like Infrastructure-as-a-Service (IaaS) clouds is well recognized as a fundamental NP-complete problem that is critical to meeting various types of Quality-of-Service (QoS) requirements. In this paper, we propose a multi-objective optimization workflow scheduling approach based on dynamic game-theoretic model aiming at reducing workflow make-spans, reducing total cost, and maximizing system fairness in terms of workload distribution among heterogeneous cloud virtual machines (VMs). We conduct extensive case studies as well based on various well-known scientific workflow templates and real-world third-party commercial IaaS clouds. Experimental results clearly suggest that our proposed approach outperform traditional ones by achieving lower workflow make-spans, lower cost, and better system fairness.