Deadline‐constrained cost‐energy aware workflow scheduling in cloud. (28th December 2021)

• Record Type: Journal Article
• Authors: Bugingo, Emmanuel; Zheng, Wei; Lei, Zhenfeng; Zhang, Defu; Sebakara, Samuel Rene Adolphe; Zhang, Dongzhan
• Is Part Of: Concurrency and computation. Volume 34:Number 6(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12-28
• Subjects: cloud computing -- cost‐efficient workflow scheduling -- deadline constrained workflow scheduling -- energy efficient workflow scheduling -- multicriteria workflow scheduling -- multiobjective workflow scheduling -- weighted sum for workflow scheduling; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.6761
• Languages: English
• ISSNs: 1532-0626

Abstract

Abstract: Nowadays, scientists are dealing with large‐scale scientific workflows that need a high processing capacity platform to facilitate on‐time completion. Cloud computing is the ideal platform to overcome this problem as it has several resources that scientists may choose from depending on the size of their applications. However, using cloud computing requires some monetary charges. Recently cloud computing providers started a new pricing schema that offers to their users a set of resources with specific combinations of CPU frequency configurations settings and price. The selected configurations settings reflect energy consumption. Besides, the configuration selection to meet users' satisfaction (minimum cost) and providers' satisfaction (energy saving) is crucial. Therefore, a multiobjective (cost and energy) efficient mechanism is essential. In this article, we address an important novel problem concerning multiobjective deadline constrained workflow scheduling in the cloud. We first study the relationship between cost minimization and minimization of the energy consumption in a cloud environment, and then discuss, develop, and propose an algorithm with two variants to help the system satisfy both sides (users and providers) at the same time during the selection of the configuration. The proposed heuristic is evaluated using specified real‐world applications. The observed results indicate that our heuristic can reduce significantly the energy consumption and the cost at the same time.