Skip to main content
placeholder image

A novel independent job rescheduling strategy for cloud resilience in the cloud environment

Journal Article


Abstract


  • Purpose: Although proactive fault handling plans are widely spread, many unexpected data center outages still occurred. To rescue the jobs from faulty data centers, the authors propose a novel independent job rescheduling strategy for cloud resilience to reschedule the task from the faulty data center to other working-proper cloud data centers, by jointly considering job nature, timeline scenario and overall cloud performance. Design/methodology/approach: A job parsing system and a priority assignment system are developed to identify the eligible time slots for the jobs and prioritize the jobs, respectively. A dynamic job rescheduling algorithm is proposed. Findings: The simulation results show that our proposed approach has better cloud resiliency and load balancing performance than the HEFT series approaches. Originality/value: This paper contributes to the cloud resilience by developing a novel job prioritizing, task rescheduling and timeline allocation method when facing faults.

Publication Date


  • 2022

Citation


  • Xie, F., Yan, J., & Shen, J. (2022). A novel independent job rescheduling strategy for cloud resilience in the cloud environment. Applied Computing and Informatics. doi:10.1108/ACI-06-2021-0172

Scopus Eid


  • 2-s2.0-85124349056

Web Of Science Accession Number


Abstract


  • Purpose: Although proactive fault handling plans are widely spread, many unexpected data center outages still occurred. To rescue the jobs from faulty data centers, the authors propose a novel independent job rescheduling strategy for cloud resilience to reschedule the task from the faulty data center to other working-proper cloud data centers, by jointly considering job nature, timeline scenario and overall cloud performance. Design/methodology/approach: A job parsing system and a priority assignment system are developed to identify the eligible time slots for the jobs and prioritize the jobs, respectively. A dynamic job rescheduling algorithm is proposed. Findings: The simulation results show that our proposed approach has better cloud resiliency and load balancing performance than the HEFT series approaches. Originality/value: This paper contributes to the cloud resilience by developing a novel job prioritizing, task rescheduling and timeline allocation method when facing faults.

Publication Date


  • 2022

Citation


  • Xie, F., Yan, J., & Shen, J. (2022). A novel independent job rescheduling strategy for cloud resilience in the cloud environment. Applied Computing and Informatics. doi:10.1108/ACI-06-2021-0172

Scopus Eid


  • 2-s2.0-85124349056

Web Of Science Accession Number