• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.6 no.3
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• pp.1-11
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• 2007

Seoul's transportation system was changed drastically starting the first of June in two thousand. This policy includes integrated distance-based fare system and public transportation card system called smart card. Especially, as public transportation card data contains individual travel, transfer and using modes information it is possible to catch the characteristics of path-based individuals and mass transit. Thus, public transportation card data can contribute to evaluate the mass transit service in integrated public transportation networks. In addition, public transportation card data are able to help to convert previous researches and analyses with link-based trip assignment models to path-based mass transit service analysis. In this study, an algorithm being suitable for path-based trip assignment models is suggested and proposed algorithm can also contribute to make full use of public transportation card data. For this, column generation algorithm hewn to draw the stable solution is adopted. This paper uses the methodology that is to take local approximate equilibrium from partial network and expand local approximate equilibrium to global equilibrium.

• 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.