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A Pretreatment Workflow Scheduling Approach for Big Data Applications in Multi-cloud Environments
College of Mathematics and Computer Sciences, Fuzhou University.
College of Mathematics and Computer Sciences, Fuzhou University.
School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology.
College of Mathematics and Computer Sciences, Fuzhou University.
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Number of Authors: 62016 (English)In: IEEE Transactions on Network and Service Management, ISSN 1932-4537, E-ISSN 1932-4537, Vol. 13, no 3, p. 581-594Article in journal (Refereed) Published
Abstract [en]

The rapid development of the latest distributed computing paradigm, i.e., cloud computing, generates a highly fragmented cloud market composed of numerous cloud providers and offers tremendous parallel computing ability to handle Big Data problems. One of the biggest challenges in Multi-clouds is efficient workflow scheduling. Although the workflow scheduling problem has been studied extensively, there are still very few primal works tailored for Multi-cloud environments. Moreover, the existing research works either fail to satisfy the Quality of Service (QoS) requirements, or do not consider some fundamental features of cloud computing such as heterogeneity and elasticity of computing resources. In this paper, a scheduling algorithm which is called Multi-Clouds Partial Critical Paths with Pretreatment (MCPCPP) for Big Data workflows in Multi-clouds is presented. This algorithm incorporates the concept of Partial Critical Paths, and aims to minimize the execution cost of workflow while satisfying the defined deadline constraint. Our approach takes into considerations the essential characteristics of Multi-clouds such as the charge per time interval, various instance types from different cloud providers as well as homogeneous intra-bandwidth vs. heterogeneous inter-bandwidth. Various types of workflows are used for evaluation purpose and our experimental results show that the MCPCPP is promising.

Place, publisher, year, edition, pages
2016. Vol. 13, no 3, p. 581-594
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
URN: urn:nbn:se:ltu:diva-14884DOI: 10.1109/TNSM.2016.2554143ISI: 000384911900018Scopus ID: 2-s2.0-84991294631Local ID: e50cce91-5cce-4316-9c8c-7ffa9a5d0895OAI: oai:DiVA.org:ltu-14884DiVA, id: diva2:987857
Note

Validerad; 2016; Nivå 2; 2016-11-07 (andbra)

Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved

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Vasilakos, Athanasios

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