Workflow Scheduling in Multi-Tenant Cloud Computing Environments
Multi-tenancy is one of the key features of cloud computing, which provides scalability and economic benefits to the end-users and service providers by sharing the same cloud platform and its underlying infrastructure with the isolation of shared network and compute resources. However, resource mana...
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| Published in | IEEE transactions on parallel and distributed systems Vol. 28; no. 1; pp. 290 - 304 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
New York
IEEE
01.01.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| Online Access | Get full text |
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| Abstract | Multi-tenancy is one of the key features of cloud computing, which provides scalability and economic benefits to the end-users and service providers by sharing the same cloud platform and its underlying infrastructure with the isolation of shared network and compute resources. However, resource management in the context of multi-tenant cloud computing is becoming one of the most complex task due to the inherent heterogeneity and resource isolation. This paper proposes a novel cloud-based workflow scheduling (CWSA) policy for compute-intensive workflow applications in multi-tenant cloud computing environments, which helps minimize the overall workflow completion time, tardiness, cost of execution of the workflows, and utilize idle resources of cloud effectively. The proposed algorithm is compared with the state-of-the-art algorithms, i.e., First Come First Served (FCFS), EASY Backfilling, and Minimum Completion Time (MCT) scheduling policies to evaluate the performance. Further, a proof-of-concept experiment of real-world scientific workflow applications is performed to demonstrate the scalability of the CWSA, which verifies the effectiveness of the proposed solution. The simulation results show that the proposed scheduling policy improves the workflow performance and outperforms the aforementioned alternative scheduling policies under typical deployment scenarios. |
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| AbstractList | Multi-tenancy is one of the key features of cloud computing, which provides scalability and economic benefits to the end-users and service providers by sharing the same cloud platform and its underlying infrastructure with the isolation of shared network and compute resources. However, resource management in the context of multi-tenant cloud computing is becoming one of the most complex task due to the inherent heterogeneity and resource isolation. This paper proposes a novel cloud-based workflow scheduling (CWSA) policy for compute-intensive workflow applications in multi-tenant cloud computing environments, which helps minimize the overall workflow completion time, tardiness, cost of execution of the workflows, and utilize idle resources of cloud effectively. The proposed algorithm is compared with the state-of-the-art algorithms, i.e., First Come First Served (FCFS), EASY Backfilling, and Minimum Completion Time (MCT) scheduling policies to evaluate the performance. Further, a proof-of-concept experiment of real-world scientific workflow applications is performed to demonstrate the scalability of the CWSA, which verifies the effectiveness of the proposed solution. The simulation results show that the proposed scheduling policy improves the workflow performance and outperforms the aforementioned alternative scheduling policies under typical deployment scenarios. |
| Author | Maier, Martin Rimal, Bhaskar Prasad |
| Author_xml | – sequence: 1 givenname: Bhaskar Prasad surname: Rimal fullname: Rimal, Bhaskar Prasad email: bhaskar.rimal@emt.inrs.ca organization: Opt. Zeitgeist Lab., Inst. Nat. de la Rech. Sci., Montreal, QC, Canada – sequence: 2 givenname: Martin surname: Maier fullname: Maier, Martin email: maier@emt.inrs.ca organization: Opt. Zeitgeist Lab., Inst. Nat. de la Rech. Sci., Montreal, QC, Canada |
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| SubjectTerms | Algorithms Cloud computing Completion time Computer architecture direct acyclic graph Dynamic scheduling multi-tenancy Processor scheduling Resource management Resource scheduling Scalability scientific workflow applications Workflow |
| Title | Workflow Scheduling in Multi-Tenant Cloud Computing Environments |
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