Title: Energy-efficient task scheduling and consolidation algorithm for workflow jobs in cloud

Authors: Mustafa Khaleel; Michelle M. Zhu

Addresses: Department of Computer Science, Southern Illinois University, Carbondale, IL, 62901, USA ' Department of Computer Science, Southern Illinois University, Carbondale, IL, 62901, USA

Abstract: The energy consumption of underlying cloud hardware has dramatically increased. The cloud service providers need to adopt some cost-effective and energy-aware job scheduler without compromising the quality of service (QoS) specified in the service level agreement (SLA). Based on a rigorous mathematical model, we formulate an energy efficient problem to improve the resource utilisation for high system throughput. A multiple-procedure heuristic workflow scheduling and consolidation strategy is proposed with objectives to maximise the resource utilisation and minimise the power. Several techniques have been utilised including dynamic voltage and frequency scaling (DVFS) with task module migration for workload balance and task consolidation for virtual machine (VM) overhead reduction. The simulation results illustrate that our approach consistently achieves a lower power consumption and higher resource utilisation rate within the execution time bound compared with other similar scheduling algorithms as well as our previous algorithm without the task migration based on VM threshold.

Keywords: energy efficiency; scientific workflow scheduling; task consolidation; directed acyclic graph; DAG; cloud computing; mathematical modelling; resource utilisation; high throughput; workload balance; virtual machines; VMs; simulation.

DOI: 10.1504/IJCSE.2016.078933

International Journal of Computational Science and Engineering, 2016 Vol.13 No.3, pp.268 - 284

Received: 13 May 2014
Accepted: 13 Jul 2014
Published online: 06 Sep 2016 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article