Abstract
The cloud computing paradigm is characterized by the ability to provide flexible provisioning patterns for computing resources and on-demand common services. As a result, building business processes and workflow-based applications on cloud computing platforms is becoming increasingly popular. However, since real-world cloud services are often affected by real-time performance changes or fluctuations, it is difficult to guarantee the cost-effectiveness and quality-of-service (Qos) of cloud-based workflows at real time. In this work, we consider that workflows, in terms of Directed Acyclic Graphs (DAGs), to be supported by decentralized cloud infrastructures are with time-varying performance and aim at reducing the monetary cost of workflows with the completion-time constraint to be satisfied. We tackle the performance-fluctuation workflow scheduling problem by incorporating a stochastic-performance-distribution-based framework for estimation and optimization of workflow critical paths. The proposed method dynamically generates the workflow scheduling plan according to the accumulated stochastic distributions of tasks. In order to prove the effectiveness of our proposed method, we conducted a large number of experimental case studies on real third-party commercial clouds and showed that our method was significantly better than the existing method.
Y. Pan and X. Sun—Contribute equally to this article and should be considered co-first authors.
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Acknowledgement
This work is supported in part by Science and Technology Program of Sichuan Province under Grant 2020JDRC0067.
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Pan, Y. et al. (2020). A Stochastic-Performance-Distribution-Based Approach to Cloud Workflow Scheduling with Fluctuating Performance. In: Ku, WS., Kanemasa, Y., Serhani, M.A., Zhang, LJ. (eds) Web Services – ICWS 2020. ICWS 2020. Lecture Notes in Computer Science(), vol 12406. Springer, Cham. https://doi.org/10.1007/978-3-030-59618-7_3
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