• KIPS Transactions on Computer and Communication Systems
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• v.4 no.6
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• pp.185-196
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• 2015

Energy aware server clusters aim to reduce power consumption at maximum while keeping QoS(Quality of Service) compared to energy non-aware server clusters. They adjust the power mode of each server in a fixed or variable time interval to let only the minimum number of servers needed to handle current user requests ON. Previous studies on energy aware server cluster put efforts to reduce power consumption further or to keep QoS, but they do not consider energy efficiency well. In this paper, we propose an energy efficient cluster management based on autonomous learning for energy aware server clusters. Using parameters optimized through autonomous learning, our method adjusts server power mode to achieve maximum performance with respect to power consumption. Our method repeats the following procedure for adjusting the power modes of servers. Firstly, according to the current load and traffic pattern, it classifies current workload pattern type in a predetermined way. Secondly, it searches learning table to check whether learning has been performed for the classified workload pattern type in the past. If yes, it uses the already-stored parameters. Otherwise, it performs learning for the classified workload pattern type to find the best parameters in terms of energy efficiency and stores the optimized parameters. Thirdly, it adjusts server power mode with the parameters. We implemented the proposed method and performed experiments with a cluster of 16 servers using three different kinds of load patterns. Experimental results show that the proposed method is better than the existing methods in terms of energy efficiency: the numbers of good response per unit power consumed in the proposed method are 99.8%, 107.5% and 141.8% of those in the existing static method, 102.0%, 107.0% and 106.8% of those in the existing prediction method for banking load pattern, real load pattern, and virtual load pattern, respectively.

• Current Research on Agriculture and Life Sciences
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• v.14
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• pp.49-60
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• 1996

In order to evaluate soil factors affecting the growth of Italian poplar, 23 areas planted with Italian poplar were surveyed. These 23 areas were classified into 3 categories, river-side, fallow-land and hill-side. The growth performance and soil factors for each area were investigated. The growth of Italian poplar at river-side was shown to be superior to that of fallow-land and fill-side. The rates of growth for fallow-land and hill-side are decreased by 8% and 21% compared to those of river-side, respectively. This suggests that plantation of Italian poplar at hill-side would not be profitable. Soil conditions of high productive area appeared liquid phase 20%, porosity 45%, water holding capacity 35 - 40%, soil hardness $1kg/cm^3$. pH 6 and rich in organic matter and total nitrogen. The results of factor analysis for soil factors affecting to Italian poplar growth that showed eigenvalue over 1 and communality value over 70% explained factor 1 : liquid phase, porosity and water holding capacity, factor 2 : pH and calcium, and factor 3 : soil hardness. This suggests that physical characteristics of soil is more important than chemical characteristics for Italian poplar growth. Multiregerssion analysis was conducted between diameter growth and soil hardness, liquid phase and calcium. The t-values for each independent variables showed significance at 1 - 10% level, but water holding capacity and pH are not significant. It is supposed that sites suitable to Italian poplar were alluvial plain of sandy loam or part of banking soil, well-ventilating soil, lower soil hardness, apposite soil moisture absorbing with about 100cm of ground water level, plentiful organic matters and total nitrogen and little acidity soil.