Sung Choul Cho , San Ha Kang , Hung Sik Moon , Hu Keun Kwak , Kyu Sik Chung

KIPS Transactions on Computer and Communication Systems, Vol. 4, No. 2, pp. 47-56, Feb. 2015
10.3745/KTCCS.2015.4.2.47,   PDF Download:

Abstract

In an energy saving server cluster environment, the power modes of servers are controlled according to load situation, that is, by making ON only minimum number of servers needed to handle current load while making the other servers OFF. This algorithm works well under normal circumstances, but does not guarantee QoS under abnormal circumstances such as sharply rising or falling loads. This is because the number of ON servers cannot be increased immediately due to the time delay for servers to turn ON from OFF. In this paper, we propose a new prediction algorithm of the power consumption for improving QoS under not only normal but also abnormal circumstances. The proposed prediction algorithm consists of two parts: prediction based on the conventional time series analysis and prediction adjustment based on trend analysis. We performed experiments using 15 PCs and compared performance for 4 types of conventional time series based prediction methods and their modified methods with our prediction algorithm. Experimental results show that Exponential Smoothing with Trend Adjusted (ESTA) and its modified ESTA (MESTA) proposed in this paper are outperforming among 4 types of prediction methods in terms of normalized QoS and number of good reponses per power consumed, and QoS of MESTA proposed in this paper is 7.5% and 3.3% better than that of conventional ESTA for artificial load pattern and real load pattern, respectively.

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