A Novel Approach to Scheduling Workflows Upon Cloud Resources with Fluctuating Performance

  • Yi Pan
  • Shu Wang
  • Lei WuEmail author
  • Yunni Xia
  • Wanbo Zheng
  • Shanchen Pang
  • Ziyang Zeng
  • Peng Chen
  • Yawen Li


Cloud computing is recently getting increasingly popular for supporting scientific applications and complex business processes. Clouds are highly potent for executing workflow-based tasks due to the fact that they provide elastic resource provisioning styles through which computational-intensive workflows can obtain requested resources according to their elastic demand and establish execution environment over virtual machines (VMs). However, it remains a challenge to guarantee cost-effectiveness and quality of service of workflow deployed upon clouds due to the fact that real-world cloud infrastructures are usually with fluctuating and time-varying performance. Existing researches mainly consider that cloud infrastructures are with fixed, random, or bounded quality of service (QoS). In this work, however, we consider that scientific computing processes to be supported by decentralized cloud infrastructures with fluctuating QoS and aim at managing the monetary cost of workflows with the completion-time constraint to be satisfied. We address the performance-variation-aware workflow scheduling problem by leveraging a time-series-based prediction model and a Critical-Path-Duration-Estimation-based (CPDE for short) VM Selection strategy. The proposed method is capable of exploiting real-time trends of performance changes of cloud infrastructures and generating dynamic workflow scheduling plans. To prove the effectiveness of our proposed method, we perform extensive experimental case analysis over real-world third-party commercial clouds and show that our method clearly beats existing approaches.


Cloud computing Scheduling Workflow Quality-of-service 



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© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  1. 1.School of Mathematical ScienceUniversity of Electronic Science and Technology of ChinaChengduChina
  2. 2.College of InformationLiaoning UniversityShenyangChina
  3. 3.School of ComputersChongqing UniversityChongqingChina
  4. 4.Data Science Research Center, Faculty of ScienceKunming University of Science and TechnologyKunmingChina
  5. 5.School of Computer and Communication EngineeringChina University of PetroleumQingdaoChina
  6. 6.School of Mechanical EngineeringChongqing UniversityChongqingChina
  7. 7.School of Computer and Software EngineeringXihua UniversityChengduChina

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