A Self-Adaptive Weighted Sum Technique for the Joint Optimization of Performance and Power Consumption in Data Centers
Source: North Dakota State University
With data centers, end-users can realize the pervasiveness of services that will be one day the cornerstone of the people lives. However, data centers are often classified as computing systems that consume the most amounts of power. To circumvent such a problem, the authors propose a self-adaptive weighted sum methodology that jointly optimizes the performance and power consumption of any given data center. Compared to traditional methodologies for multi-objective optimization problems, the proposed self-adaptive weighted sum technique does not rely on a systematical change of weights during the optimization procedure. The proposed technique is compared with the greedy and LR heuristics for large-scale problems, and the optimal solution for small-scale problems implemented in LINDO.