Value and energy aware adaptive resource allocation of soft real-time jobs on many-core HPC data centers

Modern high performance computing (HPC) data centers consume huge energy to operate them. Therefore, appropriate measures are required to reduce their energy consumption. Existing efforts for such measures focus on consolidation and dynamic voltage and frequency scaling (DVFS). However, most of them do not perform adaptive resource allocation for the executing dependent tasks (or jobs) in order to optimize both value and energy. The value is achieved by completing the execution of a job and it depends on the completion time. A high value is achieved if the job is completed before its deadline, otherwise a lower value. In this paper, we propose an adaptive resource allocation approach that uses design-time profiling results of jobs for efficient allocation and adaptation in order to optimize both value and energy while executing dependent tasks. The profiling results for each job are obtained by exploiting efficient allocation combined with identification of voltage/frequency levels of used system cores and used in adapting to different number of cores based on the monitored execution progress of the job and available cores. Experiments show that the proposed approach enhances the overall value by about 10% when compare to existing approaches while showing reduction in energy consumption and percentage of rejected jobs leading to zero value.