Deadline-constrained workflow scheduling algorithms for Infrastructure as a Service Clouds
The advent of Cloud computing as a new model of service provisioning in distributed systems encourages researchers to investigate its benefits and drawbacks on executing scientific applications such as workflows. One of the most challenging problems in Clouds is workflow scheduling, i.e., the proble...
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| Vydáno v: | Future generation computer systems Ročník 29; číslo 1; s. 158 - 169 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier B.V
01.01.2013
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| Témata: | |
| ISSN: | 0167-739X, 1872-7115 |
| On-line přístup: | Získat plný text |
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| Abstract | The advent of Cloud computing as a new model of service provisioning in distributed systems encourages researchers to investigate its benefits and drawbacks on executing scientific applications such as workflows. One of the most challenging problems in Clouds is workflow scheduling, i.e., the problem of satisfying the QoS requirements of the user as well as minimizing the cost of workflow execution. We have previously designed and analyzed a two-phase scheduling algorithm for utility Grids, called Partial Critical Paths (PCP), which aims to minimize the cost of workflow execution while meeting a user-defined deadline. However, we believe Clouds are different from utility Grids in three ways: on-demand resource provisioning, homogeneous networks, and the pay-as-you-go pricing model. In this paper, we adapt the PCP algorithm for the Cloud environment and propose two workflow scheduling algorithms: a one-phase algorithm which is called IaaS Cloud Partial Critical Paths (IC-PCP), and a two-phase algorithm which is called IaaS Cloud Partial Critical Paths with Deadline Distribution (IC-PCPD2). Both algorithms have a polynomial time complexity which make them suitable options for scheduling large workflows. The simulation results show that both algorithms have a promising performance, with IC-PCP performing better than IC-PCPD2 in most cases.
► We propose two workflow scheduling algorithms for IaaS Clouds. ► The algorithms aim to minimize the workflow execution cost while meeting a deadline. ► The pricing model of the Clouds is considered which is based on a time interval. ► The algorithms are compared with a list heuristic through simulation. ► The experiments show the promising performance of both algorithms. |
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| AbstractList | The advent of Cloud computing as a new model of service provisioning in distributed systems encourages researchers to investigate its benefits and drawbacks on executing scientific applications such as workflows. One of the most challenging problems in Clouds is workflow scheduling, i.e., the problem of satisfying the QoS requirements of the user as well as minimizing the cost of workflow execution. We have previously designed and analyzed a two-phase scheduling algorithm for utility Grids, called Partial Critical Paths (PCP), which aims to minimize the cost of workflow execution while meeting a user-defined deadline. However, we believe Clouds are different from utility Grids in three ways: on-demand resource provisioning, homogeneous networks, and the pay-as-you-go pricing model. In this paper, we adapt the PCP algorithm for the Cloud environment and propose two workflow scheduling algorithms: a one-phase algorithm which is called IaaS Cloud Partial Critical Paths (IC-PCP), and a two-phase algorithm which is called IaaS Cloud Partial Critical Paths with Deadline Distribution (IC-PCPD2). Both algorithms have a polynomial time complexity which make them suitable options for scheduling large workflows. The simulation results show that both algorithms have a promising performance, with IC-PCP performing better than IC-PCPD2 in most cases.
► We propose two workflow scheduling algorithms for IaaS Clouds. ► The algorithms aim to minimize the workflow execution cost while meeting a deadline. ► The pricing model of the Clouds is considered which is based on a time interval. ► The algorithms are compared with a list heuristic through simulation. ► The experiments show the promising performance of both algorithms. |
| Author | Abrishami, Saeid Epema, Dick H.J. Naghibzadeh, Mahmoud |
| Author_xml | – sequence: 1 givenname: Saeid surname: Abrishami fullname: Abrishami, Saeid email: s-abrishami@um.ac.ir organization: Department of Computer Engineering, Engineering Faculty, Ferdowsi University of Mashhad, Azadi Square, Mashhad, Iran – sequence: 2 givenname: Mahmoud surname: Naghibzadeh fullname: Naghibzadeh, Mahmoud email: naghibzadeh@um.ac.ir organization: Department of Computer Engineering, Engineering Faculty, Ferdowsi University of Mashhad, Azadi Square, Mashhad, Iran – sequence: 3 givenname: Dick H.J. surname: Epema fullname: Epema, Dick H.J. email: d.h.j.epema@tudelft.nl organization: Parallel and Distributed Systems Group, Faculty EEMCS, Delft University of Technology, Mekelweg 4, 2628 CD, Delft, The Netherlands |
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| ContentType | Journal Article |
| Copyright | 2012 Elsevier B.V. |
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| Keywords | IaaS Clouds Cloud computing Grid computing QoS-based scheduling Workflow scheduling |
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