• KIPS Transactions on Computer and Communication Systems
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• v.2 no.7
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• pp.283-292
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• 2013

In order to ensure high performance, all the servers in an existing server cluster are always On regardless of number of real-time requests. They ensure QoS, but waste server power if some of them are idle. To save energy consumed by servers, the server power mode control was developed by shutdowning a server when a server is not needed. There are two types of server power mode control depending on when a server is actually turned off if the server is selected to be off: static or dynamic. In a static mode, the server power is actually turned off after a fixed time delay from the time of the server selection. In a dynamic mode, server power is actually turned off if all the services served in the server are done. This corresponds to a turn off after a variable time delay. The static mdoe has disadvantages. It takes much time to find an optimal shutdown time manually through repeated experiments. In this paper, we propose a dynamic shutdown method to overcome the disadvantages of static shutdown. The proposed method allows to guarantee user QoS with good power-saving because it automatically approaches an optimal shutdown time. We performed experiments using 30 PCs cluster. Experimental results show that the proposed dynamic shutdown method is almost same as the best static shutdown in terms of power saving, but better than the best static shutdown in terms of QoS.

• The KIPS Transactions:PartC
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• v.19C no.2
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• pp.135-144
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• 2012

All the servers in a traditional server cluster environment are kept On. If the request load reaches to the maximum, we exploit its maximum possible performance, otherwise, we exploit only some portion of maximum possible performance so that the efficiency of server power consumption becomes low. We can improve the efficiency of power consumption by controlling power mode of servers according to load situation, that is, by making On only minimum number of servers needed to handle current load while making Off the remaining servers. In the existing power mode control method, they used a static policy to decide server power mode at a fixed time interval so that it cannot adapt well to the dynamically changing load situation. In order to improve the existing method, we propose a dynamic server power control algorithm. In the proposed method, we keep the history of server power consumption and, based on it, predict whether power consumption increases in the near future. Based on this prediction, we dynamically change the time interval to decide server power mode. We performed experiments with a cluster of 30 PCs. Experimental results show that our proposed method keeps the same performance while reducing 29% of power consumption compared to the existing method. In addition, our proposed method allows to increase the average CPU utilization by 66%.

• Journal of Power Electronics
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• v.14 no.5
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• pp.1069-1078
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• 2014

This study presents the design and implementation of a web-based remote laboratory for a multi-mode single-leg power converter, which is a topic in advanced power electronics course. The proposed laboratory includes an experimental test rig with a multi-mode single-leg power converter and its driver circuits, a measurement board, a control platform, and a LabVIEW-based user interface program that is operated in the server computer. Given that the proposed web-based remote laboratory is based on client/server architecture, the experimental test rig can be controlled by a client computer with Internet connection and a standard web browser. Although the multi-mode single-leg power converter can work at four different modes (main boost, buck-boost, boost-boost, and battery boost modes), only the buck-boost mode is used in the experiment because of page limit. Users can choose the control structure, control parameters, and reference values, as well as obtain graphical results from the user interface software. Consequently, the feedbacks received from students who conducted remote laboratory studies indicate that the proposed laboratory is a useful tool for both remote and traditional education.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.9
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• pp.279-292
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• 2014

Energy aware server cluster aims to reduce power consumption at maximum while keeping QoS(quality of service) as much as energy non-aware server cluster. In the existing methods of energy aware server cluster, they calculate the minimum number of active servers needed to handle current user requests and control server power mode in a fixed time interval to make only the needed servers ON. When loads change rapidly, QoS of the existing methods become degraded because they cannot increase the number of active servers so quickly. To solve this QoS problem, we classify load change situations into five types of rapid growth, growth, normal, decline, and rapid decline, and apply five different thresholds respectively in calculating the number of active servers. Also, we use a flexible scheme to adjust the above classification criterion for multi threshold, considering not only load change but also the remaining capacity of servers to handle user requests. We performed experiments with a cluster of 15 servers. A special benchmarking tool called SPECweb was used to generate load patterns with rapid change. Experimental results showed that QoS of the proposed method is improved up to the level of energy non-aware server cluster and power consumption is reduced up to about 50 percent, depending on the load pattern.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.11
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• pp.369-382
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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 activate only the minimum number of servers needed to handle current user requests. Previous studies on energy aware server cluster put efforts to reduce power consumption or heat dissipation, but they do not consider energy efficiency well. In this paper, we propose an energy efficient cluster management method to improve not only performance per watt but also QoS of the existing server power mode control method based on autonomous learning. Our proposed method is to adjust server power mode based on a hybrid approach of autonomous learning method with multi level thresholds and power consumption prediction method. Autonomous learning method with multi level thresholds is applied under normal load situation whereas power consumption prediction method is applied under abnormal load situation. The decision on whether current load is normal or abnormal depends on the ratio of the number of current user requests over the average number of user requests during recent past few minutes. Also, a dynamic shutdown method is additionally applied to shorten the time delay to make servers off. We performed experiments with a cluster of 16 servers using three different kinds of load patterns. The multi-threshold based learning method with prediction and dynamic shutdown shows the best result in terms of normalized QoS and performance per watt (valid responses). For banking load pattern, real load pattern, and virtual load pattern, the numbers of good response per watt in the proposed method increase by 1.66%, 2.9% and 3.84%, respectively, whereas QoS in the proposed method increase by 0.45%, 1.33% and 8.82%, respectively, compared to those in the existing autonomous learning method with single level threshold.

• Journal of Wind Energy
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• v.2 no.1
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• pp.90-96
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• 2011

The purpose of this paper is to describe the control system for optimal performance of 2MW gearless PMSG wind turbine system, and to afford some techniques of the algorithm selection and design optimization of the wind turbine control system through analysis of load calculation and control characteristic. Wind turbine control system is composed of the main control system and remote control and monitoring system. The main control system is industrial PC based controller, and the remote control and monitoring system is a server based computer system. The main control system has a supervisory control of the wind turbine with operation procedures and power-speed control through the torque control by pitch angle. There are some applications to optimize the wind turbine system at the starting mode with increasing of rotor speed, and cut-in operating mode to prevent trundling cut-in and cut-out, a gain scheduling of pitch PID controller, torque scheduling and limitation of generation power by temperature limitation or remote command by remote control and monitoring system. Also, the server operation program of the remote control and monitoring system and the design of graphical display are described in this paper.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.8B
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• pp.1122-1128
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• 2010

In this paper, we proposed and designed a novel SNA protocol which operates in the way of a server and a client in the power control system. The proposed SNA protocol includes the information about the mode switching, the saving position of context information, the user trigger, and so forth, which are needed in the power management devices. We consider the application of the SNA protocol to the home network, where message flows between the SNA server and the SNA client. To verify the operation of the SNA protocol, the state transition diagrams of the server in the home gateway and the client in the network device are shown. Hence, we can conclude the SNA can operate without malfuction.

• 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.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.3
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• pp.41-49
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• 2011

In this paper, we propose an open protocol to be employed between a smart meter concentrator and a metering data collection server, and also evaluate its performance. Legacy concentrators performs the connection establishment and data gathering operations with DLMS/COSEM protocol standards. However, we note that there are no standardized protocols between the concentrator and the collection server, which inevitably conduces each commercial smart metering system to have its own proprietary protocol. In order to solve this problem, we propose an open protocol - Smart Meter Concentrator Control Protocol(SMCCP) by extending the existing standard protocol(DLMS/COSEM). The SMCCP can provide the proxy mode to enable efficient transmission between the concentrator and the data collection server. It also can support the relay mode to enable a direct communication between the data collection server and each far end smart meter. We also implement an emulator system and a protocol analyzer to provide its operation. In addition, we evaluate the session holding time and the link usage ratio in both relay and proxy modes with OMNET++ simulator.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.4
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• pp.1-8
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• 2008

Recently, there is developing various ubiquitous application services using sensor networks based on TinyOS represented the operating system of sensor node. These sensor networks perform the collection and the transmission of sensing data from sensor node to get the context information. In this paper, we proposes the sensor node control algorithm which converts a sensor node to sleep, active, power off mode according to monitoring result of the voltage state of sensor node. Also, we designs and implement the sensor control module on server, sink, sensor node of sensor networks using this algorithm. It designs a sensor voltage control module of sensor node, data receive and display module of USN server using a java language and TinyOS. And, it checks the voltage state of sensor node, and it changes one of the sleep or power off modes in case of high voltage loss. Accordingly, we effectively use the power of sensor nodes as changing control modes of sensor nodes.