Real-time and dynamic fault-tolerant scheduling for scientific workflows in clouds

Zhongjin Li, Victor Chang*, Haiyang Hu, Hua Hu, Chuanyi Li, Jidong Ge

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Cloud computing has become a popular technology for executing scientific workflows. However, with a large number of hosts and virtual machines (VMs) being deployed, the cloud resource failures, such as the permanent failure of hosts (HPF), the transient failure of hosts (HTF), and the transient failure of VMs (VMTF), bring the service reliability problem. Therefore, fault tolerance for time-consuming scientific workflows is highly essential in the cloud. However, existing fault-tolerant (FT) approaches consider only one or two above failure types and easily neglect the others, especially for the HTF. This paper proposes a Real-time and dynamic Fault-tolerant Scheduling (ReadyFS) algorithm for scientific workflow execution in a cloud, which guarantees deadline constraints and improves resource utilization even in the presence of any resource failure. Specifically, we first introduce two FT mechanisms, i.e., the replication with delay execution (RDE) and the checkpointing with delay execution (CDE), to cope with HPF and VMTF, simultaneously. Additionally, the rescheduling (ReSC) is devised to tackle the HTF that affects the resource availability of the entire cloud datacenter. Then, the resource adjustment (RA) strategy, including the resource scaling-up (RS-Up) and the resource scaling-down (RS-Down), is used to adjust resource demands and improve resource utilization dynamically. Finally, the ReadyFS algorithm is presented to schedule real-time scientific workflows by combining all the above FT mechanisms with RA strategy. We conduct the performance evaluation with real-world scientific workflows and compare ReadyFS with five vertical comparison algorithms and three horizontal comparison algorithms. Simulation results confirm that ReadyFS is indeed able to guarantee the fault tolerance of scientific workflow execution and improve cloud resource utilization.

Original languageEnglish
Pages (from-to)13-39
Number of pages27
JournalInformation Sciences
Early online date9 Mar 2021
Publication statusPublished - 1 Aug 2021

Bibliographical note

© 2021, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International

Funding Information:
This work was supported by the National Natural Science Foundation of China (No. 61802095, 61802167, 61572162), the Zhejiang Provincial Key Science and Technology Project Foundation (No. 2018C01012), the Key Program of Research and Development of China (2016YFC0800803), and the VC Research (No. VCR 0000057).


  • Checkpointing
  • Cloud computing
  • Delay execution
  • Fault-tolerant workflow scheduling
  • Replication
  • Rescheduling


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