Multi-level Power Consumption and Computation Models and Energy-aware Server Selection Algorithms in a Server Cluster

It is critical to reduce the electric energy consumed in server cluster in order to realize eco society. In our previous studies, a server is selected to perform a process by estimating the termination time of every current process and the electric energy consumption of servers. However, it is not easy and takes time to collect the state of each process and to estimate the termination time of each process. In this paper, we first propose a power consumption model, MLPCM (multi-level power consumption with multipleCPUs) model which shows how much electric power a server consumes to perform processes. We also propose a computation model MLCM (multi-level computation with multiple CPUs) model whichshows the expected execution time of a process performed on a server. We newly propose SLEA (simple locally energy-aware) algorithm to select a server for each process in a cluster where only the numberof processes on each server is used. In the evaluation, we show the electric energy consumption and active time of the servers and average execution time of processes can be reduced in the SLEA algorithm.Key Words : MLPCM(Multi-Level Power Consumption with Multiple CPUs) model, SLEA(Simple Locally Energy-aware) server selection algorith

Power Management Techniques for Data Centers: A Survey

With growing use of internet and exponential growth in amount of data to be stored and processed (known as 'big data'), the size of data centers has greatly increased. This, however, has resulted in significant increase in the power consumption of the data centers. For this reason, managing power consumption of data centers has become essential. In this paper, we highlight the need of achieving energy efficiency in data centers and survey several recent architectural techniques designed for power management of data centers. We also present a classification of these techniques based on their characteristics. This paper aims to provide insights into the techniques for improving energy efficiency of data centers and encourage the designers to invent novel solutions for managing the large power dissipation of data centers.Comment: Keywords: Data Centers, Power Management, Low-power Design, Energy Efficiency, Green Computing, DVFS, Server Consolidatio