• Proceedings of the IEEK Conference
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• 2002.06c
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• pp.177-180
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• 2002

This paper introduces a new distributed QoS (Quality of Service) control system for clusters of web servers. The proposed system can employ not only network bandwidth but also other metrics such as processor load, memory usage, and storage access load that affect the overall system performance. Moreover, it controls over clustered\ulcorner workstations in of-der to utilize idle resources among workstations. This architecture maximizes overall usage of cluster of web servers while it provides predictable and differentiated performance for each contents volume. We implemented a prototype of introduced system, and the test results showed the proposed method can control QoS in a cluster server system.

• The KIPS Transactions:PartA
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• v.13A no.4 s.101
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• pp.323-334
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• 2006

A metadata service is one of the important factors to affect the performance of cluster file systems. We propose a content-based load balancing algorithm that dynamically distributes client requests to appropriate metadata servers based on the types of metadata operations. By replicating metadatas and logging update messages in each server, rather than moving metadatas across servers, we significantly reduced the response time and evenly distributed client's requests among metadata servers.

• Journal of Korea Game Society
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• v.17 no.4
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• pp.137-148
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• 2017

Since the power consumption of data centers is large and computer serves take a large portion of it, there have been much research on the power saving of servers in various ways recently. Among the units of severs CPU is one of major power consuming units. In this paper, a method of client-server assignment for minimizing the CPU power consumption of servers in a game server cluster is proposed. We model the client-server assignment problem as an optimization problem, and find a solution to the problem using a simulated annealing-based technique. One of major features of our method is to select a proper operating frequency according to the amount of load on a server. The selection of a lower frequency in case of low load will result in reducing power consumption. To our survey, little research on client-server assignment in consideration of power consumption has been carried out.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2004.07a
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• pp.89-89
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• 2004

• The KIPS Transactions:PartA
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• v.14A no.7
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• pp.443-450
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• 2007

Performance scalability and storage scalability become important in a large scale cluster of wireless internet proxy cache servers. Performance scalability means that the whole performance of the cluster increases linearly according as servers are added. Storage scalability means that the total size of cache storage in the cluster is constant, regardless of the number of cache servers used, if the whole cache data are partitioned and each partition is stored in each server, respectively. The Round-Robin based load balancing method generally used in a large scale server cluster shows the performance scalability but no storage scalability because all the requested URL data need to be stored in each server. The hashing based load balancing method shows storage scalability because all the requested URL data are partitioned and each partition is stored in each server, respectively. but, it shows no performance scalability in case of uneven pattern of client requests or Hot-Spot. In this paper, we propose a novel dynamic hashing method with performance and storage scalability. In a time interval, the proposed scheme keeps to find some of requested URLs allocated to overloaded servers and dynamically reallocate them to other less-loaded servers. We performed experiments using 16 PCs and experimental results show that the proposed method has the performance and storage scalability as different from the existing hashing method.

• 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 KIISE:Information Networking
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• v.31 no.6
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• pp.582-594
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• 2004

In school PC labs or other educational purpose PC labs with a few dozens of PCs, computers are configured in a distributed architecture so that they are set up, maintained and upgraded separately. As an alternative to the distributed architecture, we can consider a thin-client computing environment. In a thin-client computing environment, client side devices provide mainly I/O functions with user friendly GUI and multimedia processing support whereas remote servers called terminal server provide computing power. In order to support many clients in the environment, a cluster of terminal servers can be configured. In this architecture, it is difficult due to the characteristics of terminal session persistence and different pattern of computing usage of users so that the utilization of terminal server resources becomes low. To overcome this disadvantage, we propose an adaptive terminal cluster where terminal servers ,ire partitioned into groups and a terminal server in a light-loaded group can be dynamically reassigned to a heavy-loaded group at run time. The proposed adaptive scheme is compared with a generic terminal service cluster and a group based non-adaptive terminal server cluster. Experimental results show the effectiveness of the proposed scheme.

• The KIPS Transactions:PartA
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• v.13A no.7 s.104
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• pp.615-622
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• 2006

Caching in a Wireless Internet Proxy Server Cluster Environment has an effect that minimizes the time on the request and response of Internet traffic and Web user As a way to increase the hit ratio of cache, we can use a hash function to make the same request URLs to be assigned to the same cache server. The disadvantage of the hashing scheme is that client requests cannot be well-distributed to all cache servers so that the performance of the whole system can depend on only a few busy servers. In this paper, we propose an improved load balancing scheme using hashing and Round Robin scheme that distributes client requests evenly to cache servers. In the existing hashing scheme, if a hashing value for a request URL is calculated, the server number is statically fixed at compile time while in the proposed scheme it is dynamically fixed at run time using round robin method. We implemented the proposed scheme in a Wireless Internet Proxy Server Cluster Environment and performed experiments using 16 PCs. Experimental results show the even distribution of client requests and the 52% to 112% performance improvement compared to the existing hashing method.

• 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 KIISE:Information Networking
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• v.32 no.1
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• pp.60-67
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• 2005

A cluster consists of a collection of interconnected stand-alone computers working together and provides a high-availability solution in application area such as web services or information systems. Content-based load distribution for web server clusters uses the detailed data found in the application layer to intelligently route user requests among web servers. In this paper, we propose a content-based load distribution algorithm that considers cache hit and load information of the web servers under the web server clusters. In addition, we expand this algorithm in order to manage user requests for dynamic file. Specially, our algorithm does not keep track of any frequency of access information or try to model the contents of the caches of the web servers.