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A Clustering Approach to Scientific Workflow Scheduling on the Cloud with Deadline and Cost Constraints | ||
| AUT Journal of Modeling and Simulation | ||
| مقاله 3، دوره 46، شماره 1، شهریور 2014، صفحه 19-29 اصل مقاله (703.69 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/miscj.2014.532 | ||
| نویسندگان | ||
| Arash Deldari؛ Mahmoud Naghibzadeh* ؛ Saeid Abrishami؛ Amin Rezaeian | ||
| Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, IRAN | ||
| چکیده | ||
| 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. | ||
| کلیدواژه‌ها | ||
| High Throughput Computing؛ Cloud computing؛ Workflow scheduling؛ clustering؛ Time Overlap | ||
| عنوان مقاله [English] | ||
| A Clustering Approach to Scientific Workflow Scheduling on the Cloud with Deadline and Cost Constraints | ||
| نویسندگان [English] | ||
| Arash Deldari؛ Mahmoud Naghibzadeh؛ Saeid Abrishami؛ Amin Rezaeian | ||
| Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, IRAN | ||
| چکیده [English] | ||
| 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. | ||
| کلیدواژه‌ها [English] | ||
| High Throughput Computing, Cloud computing, Workflow scheduling, clustering, Time Overlap | ||
| مراجع | ||
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