Budget-deadline constrained approach for scientific workflows scheduling in a cloud environment
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Budget-deadline constrained approach for scientific workflows scheduling in a cloud environment Naqin Zhou1 • Weiwei Lin2
•
Wei Feng3 • Fang Shi2 • Xiongwen Pang4
Received: 18 April 2020 / Revised: 9 July 2020 / Accepted: 21 August 2020 Ó Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract In cloud computing environments, it is a great challenge to schedule a workflow application because it is an NP-complete problem. Particularly, scheduling workflows with different Quality of Service (QoS) constraints makes the problem more complex. Several approaches have been proposed for QoS workflow scheduling, but most of them are focused on a single QoS constraint. Therefore, this paper presents a new algorithm for multi-QoS constrained workflow scheduling, cost, and time, named Budget-Deadline Constrained Workflow Scheduling (BDCWS). The algorithm builds the task optimistic available budget based on the execution cost of the task on the slowest virtual machine and the optimistic spare budget, and then builds the set of affordable virtual machines according to the task optimistic available budget to control the range of virtual machine selection, and thus effectively controls the task execution cost. Finally, a new balance factor and selection strategy are given according to the optimistic spare deadline and the optimistic spare budget, so that the execution cost and time consumption of the control task are more effective. To evaluate the proposed algorithm, we experimentally evaluated our algorithm using real-world workflow applications. The experimental results show that compared with DBWS (Deadline-Budget Workflow Scheduling) and BDAS (Budget-Deadline Aware Scheduling), the proposed algorithm has a 26.3–79.7% higher success rate. Especially when the deadline and budget are tight, the improvement is more obvious. In addition, the best cost frequency of our algorithm achieves a 98%, which is more cost-competitive than DBWS. Keywords Cloud computing Scientific workflow QoS scheduling Budget Virtual machine
1 Introduction The development of cloud computing technology provides a good platform for parallel applications, especially scientific workflows, such as Epigenomics in bioinformatics,
& Weiwei Lin [email protected] & Xiongwen Pang [email protected] Naqin Zhou [email protected]
Montage from astronomy and LIGO from gravitational physics. These platforms offer numbers of networked, flexible and scalable resources and services, and users pay only for what they use. However, the inherent flexibility of cloud computing platforms, while powerful, may also lead
2
School of Computer Science and Engineering, South China University of Technology, Guangzhou, China
3
Guangzhou Branch of Shanghai Yizhong Enterprise Management Consulting Co., Ltd, Shanghai, China
4
School of Computer, South China Normal University, Guangzhou, China
Wei Feng [email protected] Fang Shi [email protected] 1
Cyberspace Institute of Advanced Technology, Guan
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