A CLUSTERING APPROACH TO SCIENTIFIC WORKFLOW SCHEDULING ON THE CLOUD WITH DEADLINE AND COST CONSTRAINTS

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Deldari Arash; Naghibzadeh Mahmoud; ABRISHAMI SAEID; Rezaeian Amin

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Journal Paper

Paper Information

Journal: AMIRKABIR INTERNATIONAL JOURNAL OF MODELING, IDENTIFICATION, SIMULATION AND CONTROL Year:2014 | Volume:46 | Issue:1 Page(s): 19-29

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Information Journal Paper

Title

A CLUSTERING APPROACH TO SCIENTIFIC WORKFLOW SCHEDULING ON THE CLOUD WITH DEADLINE AND COST CONSTRAINTS

Author(s)

Deldari Arash | Naghibzadeh Mahmoud | ABRISHAMI SAEID | Rezaeian Amin | Issue Writer Certificate

Keywords

HIGH THROUGHPUT COMPUTING

CLOUD COMPUTING

WORKFLOW SCHEDULING

CLUSTERING

TIME OVERLAP

Abstract

One of the main features of HIGH THROUGHPUT COMPUTING systems is the availability of high power processing resources. CLOUD COMPUTING systems can offer these features through concepts like Pay-Per-Use and Quality of Service (QoS) over the Internet. Many applications in CLOUD COMPUTING are represented by workflows. Quality of Service is one of the most important challenges in the context of scheduling scientific workflows. On the other hand, the remarkable growth of the multicore processor technology has led to the use of these processors by service providers as building blocks of their infrastructure. Therefore, scheduling scientific workflows on the Cloud requires especial attention to multicore processor infrastructure which adds more challenges to the problem. On the other hand, in addition to these challenges users’ QoS constraints like execution time and cost should be regarded. The main objective of this research is scheduling workflows on the Cloud, considering a multicore based infrastructure. A new algorithm is proposed which finds clusters of the workflow that can be executed in parallel while having large data communications. These kinds of clusters could be appropriate candidates to be executed on a multicore processor. In contrast, there are other clusters which should be executed in serial. This algorithm investigates whether serial execution of these clusters is possible or not. The experimental results show that the algorithm has a positive effect on execution time and cost of the workflow execution.

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APA: Copy

Deldari, Arash, Naghibzadeh, Mahmoud, ABRISHAMI, SAEID, & Rezaeian, Amin. (2014). A CLUSTERING APPROACH TO SCIENTIFIC WORKFLOW SCHEDULING ON THE CLOUD WITH DEADLINE AND COST CONSTRAINTS. AMIRKABIR INTERNATIONAL JOURNAL OF MODELING, IDENTIFICATION, SIMULATION AND CONTROL, 46(1), 19-29. SID. https://sid.ir/paper/320036/en

Vancouver: Copy

Deldari Arash, Naghibzadeh Mahmoud, ABRISHAMI SAEID, Rezaeian Amin. A CLUSTERING APPROACH TO SCIENTIFIC WORKFLOW SCHEDULING ON THE CLOUD WITH DEADLINE AND COST CONSTRAINTS. AMIRKABIR INTERNATIONAL JOURNAL OF MODELING, IDENTIFICATION, SIMULATION AND CONTROL[Internet]. 2014;46(1):19-29. Available from: https://sid.ir/paper/320036/en

IEEE: Copy

Arash Deldari, Mahmoud Naghibzadeh, SAEID ABRISHAMI, and Amin Rezaeian, “A CLUSTERING APPROACH TO SCIENTIFIC WORKFLOW SCHEDULING ON THE CLOUD WITH DEADLINE AND COST CONSTRAINTS,” AMIRKABIR INTERNATIONAL JOURNAL OF MODELING, IDENTIFICATION, SIMULATION AND CONTROL, vol. 46, no. 1, pp. 19–29, 2014, [Online]. Available: https://sid.ir/paper/320036/en

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