PROJECT TITLE :
Optimal Resource Provisioning and the Impact of Energy-Aware Load Aggregation for Dynamic Temporal Workloads in Data Centers
An important goal of data center providers is to minimize their operational cost, which reflected through the wear-and-tear cost and the energy consumption cost. In this paper, we present optimization formulations to minimize the cost of ownership in terms of server energy consumption and serverwear-and-tear cost under three different data center server setups (homogeneous, heterogeneous, and hybrid hetero-homogeneous clusters) for dynamic temporal workloads. Our studies show that the homogeneous model takes significantly less computational time than the heterogeneous model (by an order of magnitude). To compute optimal configurations in near real time for large-scale data centers, we propose two modes for using our models: aggregation by maximum (preserves workload deadline) and aggregation by mean (relaxes workload deadline). In addition, we propose two aggregation methods for use in each of the two modes: static (periodic) aggregation and dynamic (aperiodic)aggregation. We found that in the aggregation by maximum mode, dynamic aggregation resulted in cost savings of up to approximately 18% over the static aggregation. In the aggregation by mean mode, dynamic aggregation saved up to approximately a 50% workload rearrangement compared with the static aggregationby mean mode.
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