2 research outputs found
Energy-Aware Aggregation of Dynamic Temporal Workload in Data Centers
Data center providers seek to minimize their total cost of ownership (TCO),
while power consumption has become a social concern. We present formulations to
minimize server energy consumption and server cost under three different data
center server setups (homogeneous, heterogeneous, and hybrid hetero-homogeneous
clusters) with dynamic temporal workload. Our studies show that the homogeneous
model significantly differs from the heterogeneous model in computational time
(by an order of magnitude). To be able to compute optimal configurations in
near real-time for large scale data centers, we propose two modes, aggregation
by maximum and aggregation by mean. In addition, we propose two aggregation
methods, static (periodic) aggregation and dynamic (aperiodic) aggregation. We
found that in the aggregation by maximum mode, the dynamic aggregation resulted
in cost savings of up to approximately 18% over the static aggregation. In the
aggregation by mean mode, the dynamic aggregation by mean could save up to
approximately 50% workload rearrangement compared to the static aggregation by
mean mode. Overall, our methodology helps to understand the trade-off in
energy-aware aggregation
Estimating Optimal Cost of Allocating Virtualized Resources with Dynamic Demand
Abstract—Considering the dynamics of the demand on virtulized resources is indispensable to reduce the operational costs in the context of “pay-as-you-go ” model. In this paper, we formulate the optimization problem of minimizing the operational costs with dynamic demand. Furthermore, a new simple index to estimate the optimum is proposed. I