Affiliation of Author(s):

(1) Wuhan University, Computer School, Wuhan 430070, China

Journal:

Energy Education Science and Technology Part A: Energy Science and Research

Abstract:

With the wide application of bigdata, various datacenters are deployed on a global scale wide, the problems of tremendous power consumption, high operational cost and serious environmental pollution have become increasingly prominent. In this paper, we developed an Adaptive Energy-Economize Scheduling (AEES) to reduce power cost and carbon footprint, an increasing number of bigdata service providers attempt to power their datacenters. The AEES strategy aims at adaptively adjusting voltages according to the system workload, thereby making trade-offs between energy conservation and user expectation. When the system is under heavy workload, to meet user expectations, AEES not only considers the voltage for a new task, but also takes the voltages to run tasks waiting in local queues into account; in contrast, AEES degrades voltage levels to reduce energy consumption while holding higher user satisfaction rate in terms of user expected finish time. The research results show that AEES is able to effectively enhance the system adaptivity, reduce the computation and communication energy cost efficiently and get a good energy saving effect. © Sila Science.

Co-author:

Shijun(1), Li, Gan,Yu, Liu, Qin, Lin(1), Jin(1), Feng(1), Wang, Yanxia(1), Jun(1), Wei(1)

Translation or Not:

no

Date of Publication:

2013-01-01