An Autonomic Capacity Management Approach with Cloud Insight towards Cost-Efficient Throughput Optimization for High Performance Computing

High Performance Computing (HPC) leverages cluster combined with a set of computing nodes exploiting computational capacities to handle varying job submission for scientific computing work. Inappropriate capacity planning and related management mechanism applied, will lead HPC cluster into a rather large number of pending jobs which is considered as a critical factor to affect System's throughput against the goal of HPC cluster. Moreover it will result in inefficiency and wasted capacities cost. Thus an autonomic capacity management approach is therefore proposed in this paper, in order to overcome such issues as regards. Firstly we survey recent researches related in deep, and find that they all lack of consideration on computing node's personality which is crucial to solve job submission and is probable to lead submitted jobs into pending in case of there are insufficient computing nodes associated to this personality. Afterward we present our measures focused on autonomic capacity management by taking advantage of Cloud insight to provision capacities dynamically on demand. Such measures are capable of selfadaptively adjusting cluster capacities to form different personalities for varying job submission, by the repurposeability to coordinate capacities from the idle personality with lower running jobs to the other with higher demand on pending jobs. Finally we verify that, proposals in this paper are significantly in achieving optimized throughput by reducing the number of pending jobs in cost-efficiency, with a couple of simulations.