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. Existing researches in this direction mainly consider time-invariant performance of cloud infrastructures and scheduling decision-making as a static optimization problem.In this work, instead, 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.
Authors: Yi Pan, Xiaoning Sun and Yunni Xia (Chongqing University, China); Chen Peng (Xihua University, China); Shanchen Pang (China University of Petroleum, China); Xiaobo Li (Chongqing Animal Husbandry Techniques Extension Center, China); Yong Ma (Jiangxi Normal University, China)
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