• Journal of Advanced Navigation Technology
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• v.15 no.6
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• pp.1034-1041
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• 2011

Generally, In distributed online game server system, game maps are processed separately by means of dividing into several unit blocks. But the keeping normal distribution of user in game map is very difficult because preferences of game users are not same according to individual user's private character. For this reason, if the huge number of users concentrate on particular region of same game map at once, the game server exceed their threshold so that the system can be getting down. Conversely, the efficiency of system goes down considerably because the game server must perform map processing continuously even under user_empty situation. To solve this problem, in this paper, we propose a Map management method to control relatively normal distribution of users in game maps using Map Balance Server. In suggested model, we can reduce the load of game servers by means of turn off the game map processing temporary when a server is under user_empty situation. we also can maximize server performance by means of redistribution of map processing load among servers.

• Journal of KIISE
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• v.44 no.10
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• pp.1071-1080
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• 2017

With today's complex networks, asset identification of network devices is becoming an important issue in management and security. Because these assets are connected to the network, it is also important to identify the network structure and to verify the location and connection status of each asset. This can be used to identify vulnerabilities in the network architecture and find solutions to minimize these vulnerabilities. However, in an IoT(Internet of Things) network with a small amount of resources, the Traceroute packets sent by the monitors may overload the IoT devices to determine the network structure. In this paper, we describe how we improved the existing the well-known double-tree algorithm to effectively reduce the load on the network of IoT devices. To balance the load, this paper proposes a new destination-matching algorithm and attempts to search for the path that does not overlap the current search path statistically. This balances the load on the network and additionally balances the monitor's resource usage.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.8
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• pp.535-544
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• 2018

As the spread of mobile devices equipped with various sensors and high-quality wireless network communications functionsexpands, the amount of spatio-temporal data generated from mobile devices in various service fields is rapidly increasing. In conventional research into processing a large amount of real-time spatio-temporal streams, it is very difficult to apply a Hadoop-based spatial big data system, designed to be a batch processing platform, to a real-time service for spatio-temporal data streams. This paper extends the MapReduce online framework to support real-time query processing for continuous-input, spatio-temporal data streams, and proposes a load management method to distribute overloads for efficient query processing. The proposed scheme shows a dynamic load balancing method for the nodes based on the inflow rate and the load factor of the input data based on the space partition. Experiments show that it is possible to support efficient query processing by distributing the spatial data stream in the corresponding area to the shared resources when load management in a specific area is required.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.10
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• pp.2288-2293
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• 2013

An RFID middleware system that can process a large volume of RFID data became necessary with the advent of various kinds of ubiquitous services. In this paper, we propose a method to process massive RFID data and provide it to client applications based on ALE-compliant RFID middleware systems. To distribute the requests to collect and process the RFID data from clients, the proposed system gathers the resource information for RFID middleware systems and processes the requests from clients according to the information.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.4
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• pp.447-451
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• 2010

Monitoring and analyzing the status of smart objects, the ubiquitous smart monitoring system provides several information such as user-state index, service states and system operation among the services in real time. It also provides self-optimization and self-management for enhancing the performance of services. In order to expand the application scope of this real-time monitoring system, it is indispensible to process huge amount of stream data. In this paper, we propose a load balancing scheme to solve the overload of the monitoring agents. Our proposed scheme reduces deadline miss ratio of entire data by more than 80%.

• Journal of Digital Convergence
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• v.15 no.12
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• pp.323-331
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• 2017

In this paper, four types of deposition control processes such as temperature, pressure, input/output(I/O), and gas were replaced by the Internet of Things(IoT) to analyze the data load and sequence procedure before and after the application of it. Through this analysis, we designed the load balancing in the sensing area of the deposition process by creating the sequence diagram of the deposition process. In order to do this, we were modeling of the sensor I/O according to the arrival process and derived the result of measuring the load of CPU and memory. As a result, it was confirmed that the reliability on the deposition processes were improved through performing some functions of the equipment controllers by the IoT. As confirmed through this paper, by applying the IoT to the deposition process, it is expected that the stability of the equipment will be improved by minimizing the load on the equipment controller even when the equipment is expanded.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.4
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• pp.2058-2074
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• 2017

Dense Heterogeneous Cellular Networks(HCNs) offer a promising approach to meet the target of 1000x increase in aggregate data rates in 5G wireless communication systems. However how to best utilize the available radio resources at densely deployed small cells remains an open problem as those small cells are typically unplanned. In this paper we focus on balancing loads across macro cells and small cells by offloading users to small cells, as well as dynamically switching off underutilized small cells. We propose a joint user association and base station(BS) sleep mangement(UA-BSM) scheme that proactively offloads users to a fraction of the densely deployed small cells. We propose a heuristic algorithm that iteratively solves the user association problem and puts BSs with low loads into sleep. An interference relation matrix(IRM) is constructed to help us identify the candidate BSs that can be put into sleep. User associations are then aggregated to selected small cells that remain active. Simulation results show that our proposed approach achieves load balancing across macro and small cells and reduces the number of active BSs. Numerical results show user signal to interference ratio(SINR) can be improved by small cell sleep control.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.12
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• pp.2294-2314
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• 2011

The key issue in providing fast and reliable access on cloud services is the effective management of resources in a cloud system. However, the high variation in cloud service access rates affects the system performance considerably when there are no default routines to handle this type of occurrence. Adaptive techniques are used in resource management to support robust systems and maintain well-balanced loads within the servers. This paper presents an adaptive resource management for cloud systems which supports the integration of intelligent methods to promote quality of service (QoS) in provisioning of cloud services. A technique of dynamically assigning cloud services to a group of cloud servers is proposed for the adaptive resource management. Initially, cloud services are collected based on the excess cloud services load and then these are deployed to the assigned cloud servers. The assignment function uses the proposed proportional ordering which efficiently assigns cloud services based on its resource consumption. The difference in resource consumption rate in all nodes is analyzed periodically which decides the execution of service assignment. Performance evaluation showed that the proposed dynamic service assignment (DSA) performed best in throughput performance compared to other resource allocation algorithms.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2001.10a
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• pp.286-289
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• 2001

This paper deals with the procedures of effective mid-term Operation Planning establishment for painting shop in shipbuilding. and develop prototype system. In general, the block painting process consists of two stages such as blasting operation for surface preparation and painting operation for paint application for blocks. Weather condition is a potent Influence on those operations. The procedures consists of four steps, Load analysis, Generate alternative simulation plan. Implementation of Allocation automation module and Compare result of each simulation plan. Explain of each step. as follows, 1.step, Load analysis measure amount of assigned workload and manhour. 2.step, simulation scheme include alterable control variable such as overtime, weather. Auto allocating module carry out feasibility of simulation plan. 3.step, Allocation automation module are composed of three algorithms, as followings: - the block allocation algorithm that determines the number of blocks to be processed each day, - the team allocation algorithm that allocates blocks to worker groups. - the block arrangement algorithm that arrange blocks in blasting and painting cells. Since the block arrangement algorithm is conducted simultaneously with the team allocation algorithm, the total structure of the operating algorithms is considered to have two phases: first, daily load balancing with capacity limit and second, team allocation considering arrangement each day 4 step, Comparing result of each simulation plan. and select best simulation plan.

• Journal of KIISE:Computer Systems and Theory
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• v.35 no.12
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• pp.568-575
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• 2008

Server consolidation by sever virtualization can make one physical machine(PM) to run several virtual machines simultaneously. Although It is attractive in cost, it has complex workload behaviors. For that reason, efficient resource management method is required. Dynamic relocation of virtual machine(VM)[3,4] by live migration[1,2] is one of resource management methods. We proposed SCOA(Server Consolidation Optimizing Algorithm) : a fine-grained load balancing mechanism worked on this dynamic relocation mechanism. We could obtain accurate resource distribution information through pointed physical machines on multi dimensional resource usage coordination, so we could maintain more balanced resource state. In this paper, we show the effectiveness of our algorithm by comparison of experimental results between SCOA and sandpiper[3] by software simulation.