Multi-objective optimization-based workflow scheduling for applications with data locality and deadline constraints in geo-distributed clouds
Geo-distributed clouds have emerged as a new generation of cloud computing paradigm, in which each cloud is operated and managed by independent cloud service providers (CSPs). By enhancing cooperation among CSPs, it can offer efficient cross-cloud services. In geo-distributed clouds, the resources o...
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| Veröffentlicht in: | Future generation computer systems Jg. 157; S. 485 - 498 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier B.V
01.08.2024
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| ISSN: | 0167-739X, 1872-7115 |
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| Abstract | Geo-distributed clouds have emerged as a new generation of cloud computing paradigm, in which each cloud is operated and managed by independent cloud service providers (CSPs). By enhancing cooperation among CSPs, it can offer efficient cross-cloud services. In geo-distributed clouds, the resources offered by CSPs are heterogeneous with different billing mechanisms and the data required by workflow applications are geographically distributed with locality characteristics. As such, it is significantly challenging for cloud users to select the appropriate resources to execute their workflow applications. In this paper, we model the constrained multi-objective workflow scheduling problem (CMWSP) in geo-distributed clouds as a constrained multi-objective optimization problem that minimizes both workflow makespan and resource rental costs. To solve the CMWSP, we propose a multi-objective multi-workflow scheduling mechanism (MOMWS), which integrates workflow preprocessing, evolutionary multi-objective optimization and intensification strategy while explicitly considering the data locality characteristics, deadline requirements, and rental period reuse. Specifically, we first design a task preprocessing algorithm for workflow applications to reduce transferred data volume by merging tasks with the same original datasets. Based on this algorithm, we introduce a priority assignment algorithm to decide the scheduling sequence of workflow applications. We next propose a makespan and cost-aware workflow scheduling algorithm to determine a set of high-quality approximations of the Pareto front to the CMWSP. Based on real-world CSPs and workflow applications, extensive experiments are carried out to demonstrate the effectiveness and efficiency of MOMWS.
•We formulate the constrained multi-objective workflow scheduling problem in geo-distributed clouds as a CMOP.•We design a practical scheduling mechanism named MOMWS that jointly minimizes workflow makespan and rental costs across multiple workflows and CSPs.•We conduct extensive experiments based on real-world workflow applications and CSPs to validate the effectiveness and efficiency of MOMWS. |
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| AbstractList | Geo-distributed clouds have emerged as a new generation of cloud computing paradigm, in which each cloud is operated and managed by independent cloud service providers (CSPs). By enhancing cooperation among CSPs, it can offer efficient cross-cloud services. In geo-distributed clouds, the resources offered by CSPs are heterogeneous with different billing mechanisms and the data required by workflow applications are geographically distributed with locality characteristics. As such, it is significantly challenging for cloud users to select the appropriate resources to execute their workflow applications. In this paper, we model the constrained multi-objective workflow scheduling problem (CMWSP) in geo-distributed clouds as a constrained multi-objective optimization problem that minimizes both workflow makespan and resource rental costs. To solve the CMWSP, we propose a multi-objective multi-workflow scheduling mechanism (MOMWS), which integrates workflow preprocessing, evolutionary multi-objective optimization and intensification strategy while explicitly considering the data locality characteristics, deadline requirements, and rental period reuse. Specifically, we first design a task preprocessing algorithm for workflow applications to reduce transferred data volume by merging tasks with the same original datasets. Based on this algorithm, we introduce a priority assignment algorithm to decide the scheduling sequence of workflow applications. We next propose a makespan and cost-aware workflow scheduling algorithm to determine a set of high-quality approximations of the Pareto front to the CMWSP. Based on real-world CSPs and workflow applications, extensive experiments are carried out to demonstrate the effectiveness and efficiency of MOMWS.
•We formulate the constrained multi-objective workflow scheduling problem in geo-distributed clouds as a CMOP.•We design a practical scheduling mechanism named MOMWS that jointly minimizes workflow makespan and rental costs across multiple workflows and CSPs.•We conduct extensive experiments based on real-world workflow applications and CSPs to validate the effectiveness and efficiency of MOMWS. |
| Author | Wang, Xueyi Wang, Xingwei Zeng, Rongfei Yang, Kaiqi Wu, Dongkuo Huang, Min |
| Author_xml | – sequence: 1 givenname: Dongkuo surname: Wu fullname: Wu, Dongkuo email: wdk16604621772@163.com organization: College of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning 110819, China – sequence: 2 givenname: Xingwei surname: Wang fullname: Wang, Xingwei email: wangxw@mail.neu.edu.cn organization: College of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning 110819, China – sequence: 3 givenname: Xueyi surname: Wang fullname: Wang, Xueyi email: xywang@mail.neu.edu.cn organization: College of Software, Northeastern University, Shenyang, Liaoning 110819, China – sequence: 4 givenname: Min surname: Huang fullname: Huang, Min email: mhuang@mail.neu.edu.cn organization: College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110819, China – sequence: 5 givenname: Rongfei surname: Zeng fullname: Zeng, Rongfei email: zengrf@swc.neu.edu.cn organization: College of Software, Northeastern University, Shenyang, Liaoning 110819, China – sequence: 6 givenname: Kaiqi orcidid: 0000-0003-1751-3614 surname: Yang fullname: Yang, Kaiqi email: kaiqiyangkq@163.com organization: College of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning 110819, China |
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| Keywords | Multi-objective optimization Geo-distributed clouds Workflow scheduling Evolutionary algorithm |
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