Abstract
Abstract Dynamic Voltage and Frequency Scaling (DVFS) has been widely used as a promising power management method to reduce the energy consumption of cloud workflows. However, due to the increasing chip density, lowering CPU voltages improperly in cloud data centers may inevitably increase soft error rate during workflow execution. Consequently, failures of timely completion of workflow applications may often take place, which raises serious concerns during the operation and maintenance of cloud data centers. To address such a problem, this paper proposes a soft error-aware energy-efficient task scheduling approach for workflow applications in DVFS-enabled cloud data centers. Under reliability and completion time constraints requested by tenants, our approach can generate energy-efficient task schedules for workflows by allocating tasks to appropriate virtual machines with specific operating frequencies. Comprehensive experiments on various well-known scientific workflow benchmarks show the effectivenss of our approach. Compared with state-of-the-art methods, our approach can achieve more than 30% energy savings while satisfying both reliability and completion time requirements.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
