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An adaptive and deadline-constrained workflow scheduling algorithm in infrastructure as a service clouds

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Abstract

Cloud computing technology, which is a model of service provisioning in distributed systems, has been introduced as a way to execute workflow applications. One of the most challenging problems for executing workflow applications in cloud environment is developing workflow scheduling algorithms that consider different QoS requirements such as time and cost. We have previously presented a workflow scheduling algorithm called the CTDC (Constrained Time and Decreased Cost), which decreases the execution cost of the workflow while meeting user-defined deadline. However, the CTDC algorithm is a static scheduling algorithm and does not take into account the virtual machine performance variability. In CTDC algorithm, if the performance of virtual machines is reduced during the execution of the workflow due to the virtualization of resources and shared nature of infrastructure, the virtual machines cannot complete the tasks within the estimated time, and thus, the defined deadline will be missed. Therefore, in this paper, we complete the CTDC algorithm and propose a scheduling algorithm named AWS-CTDC (Adaptive Workflow Scheduling-CTDC) which considers the performance variation of virtual machines in the cloud environment and tries to complete the execution of the workflow within the user-defined deadline. The AWS-CTDC algorithm involves initial static scheduling, monitoring the finish time of completed tasks and rescheduling unexecuted tasks, if needed. The proposed algorithm is evaluated by a simulation process using WorkflowSim which is based on CloudSim. To evaluate the proposed algorithm, its results are compared with the results of CTDC, JIT-C (Just-In-Time), RTC (Robustness-Time-Cost), RCT (Robustness-Cost-Time), and CEGA (Cost-Effective Genetic Algorithm) algorithms. The results show that the proposed algorithm performs better than the above-mentioned algorithms.

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Authors and Affiliations

  1. Department of Computer Engineering, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran

    Robabeh Ghafouri

  2. Department of Computer Engineering, Sharif University of Technology, Tehran, Iran

    Ali Movaghar

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  1. Robabeh Ghafouri
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  2. Ali Movaghar
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Correspondence to Robabeh Ghafouri.

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Ghafouri, R., Movaghar, A. An adaptive and deadline-constrained workflow scheduling algorithm in infrastructure as a service clouds. Iran J Comput Sci 5, 17–39 (2022). https://doi.org/10.1007/s42044-021-00082-6

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  • DOI: https://doi.org/10.1007/s42044-021-00082-6

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