Evolutionary Multi-Objective Workflow Scheduling for Volatile Resources in the Cloud

Abstract

The cloud has been widely used as a distributed computing platform for running scientific workflow applications. Most of the cloud providers encourage the use of their underutilized resources as spot instances for much cheaper prices compared with common resources as on-demand instances, however, the promise of lower costs for resources results in the volatility such that spot instances can be interrupted at any time by cloud providers. Many workflow scheduling algorithms have been proposed to deal with volatile resources. In this article, we consider the two most important features of the volatile resources namely fulfillment and interruption rates to fully model the instability of the cloud infrastructure. Subsequently, we propose a novel evolutionary multi-objective workflow scheduling approach to generate a set of trade-off solutions that outperform state-of-the-art algorithms in both makespan and economic costs. In addition, we explore the fluctuation of makespan and costs for our obtained schedules under different levels of fulfillment and interruption rates. Experimental results with the five well-known real-world workflows demonstrate that our evolutionary multi-objective workflow scheduling algorithm is competitive in terms of makespan and cost compared with state-of-the-art on-demand scheduling techniques.