• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.7A
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• pp.693-701
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• 2008

In OFDMA (Orthogonal Frequency Division Multiple Access) system that frequency reuse factor is 1, as the same channels in the neighborhood cells creates inter-cell co-channel interference which provides a resource underutilization problem, channel allocation schemes to minimize inter-cell interference have been studied. This paper proposes a new CNIR (Carrier to Noise and Interference Ratio)-based distributed Inter-cell DCA (Dynamic Channel Allocation) algorithm in the OFDMA environment with frequency reuse factor of 1. When a channel allocation is requested, if there is not a free channel in home cell or the available free channels in home cell do not satisfy a required threshold value, the proposed Inter-cell DCA algorithm finds CNIR values of available free channels in the neighborhood cells and then allocates a free channel with maximum CNIR value. Through the simulation results, we find that the proposed scheme decreases both new call block rate and forced termination rate due to new call generation at the same time because it increases channel allocation probability.

• Journal of Communications and Networks
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• v.18 no.3
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• pp.387-396
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• 2016

Multiuser communication has been an important research area of underwater acoustic communications and networking. This paper studies the use of adaptive orthogonal frequency-division multiple access (OFDMA) in a downlink scenario, where a central node sends data to multiple distributed nodes simultaneously. In practical implementations, the instantaneous channel state information (CSI) cannot be perfectly known by the central node in time-varying underwater acoustic (UWA) channels, due to the long propagation delays resulting from the low sound speed. In this paper, we explore the CSI feedback for resource allocation. An adaptive power-bit loading algorithm is presented, which assigns subcarriers to different users and allocates power and bits to each subcarrier, aiming to minimize the bit error rate (BER) under power and throughput constraints. Simulation results show considerable performance gains due to adaptive subcarrier allocation and further improvement through power and bit loading, as compared to the non-adaptive interleave subcarrier allocation scheme. In a lake experiment, channel feedback reduction is implemented through subcarrier clustering and uniform quantization. Although the performance gains are not as large as expected, experiment results confirm that adaptive subcarrier allocation schemes based on delayed channel feedback or long term statistics outperform the interleave subcarrier allocation scheme.

• Journal of the Korean Operations Research and Management Science Society
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• v.26 no.4
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• pp.23-37
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• 2001

This paper is concerned with the critical issue of allocating limited corporate resources among multiple products and between production and marketing functions of a functionally decentralized firm where the two geographically remote functions independently make decisions pertaining to their own decision-making domain. We attempt to demonstrate how IT can contribute to enhancing the quality of coordinating production and marketing functions from the perspective of resource allocation. To this end, we propose a prototype named ITBCS (IT-Based Coordination System) that works under LAN supported computing environments. We develop a comprehensive coordination scheme that can handle various cost functions for the resource constrained, multiple product case that huts been tittle discussed in literature. A preliminary version of ITBCS has been implemented for a hypothetical situation where LAN electronically wires distributed marketing and production computing nodes. Managerial implications are also discussed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.6
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• pp.2035-2049
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• 2015

Applications deployed in cloud receive a huge volume of requests from geographically distributed users whose satisfaction with the cloud service is directly proportional to the efficiency with which the requests are handled. Assignment of user requests based on appropriate load balancing principles significantly improves the performance of such cloud-based applications. To study the behavior of such systems, there is a need for simulation tools that will help the designer to set a test bed and evaluate the performance of the system by experimenting with different load balancing principles. In this paper, a novel architecture for cloud called Request Assignment Simulator (RAS) is proposed. It is a customizable, visual tool that simulates the request assignment process based on load balancing principles with a set of parameters that impact resource utilization. This simulator will help to ascertain the best possible resource allocation technique by facilitating the designer to apply and test different load balancing principles for a given scenario.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2319-2337
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• 2019

In this paper, we study the distributed cooperative caching for Internet content providers in a small cell of heterogeneous network (HetNet). A general framework based on bankruptcy game model is put forth for finding the optimal caching policy. In this framework, the small cell and different content providers are modeled as bankrupt company and players, respectively. By introducing strategic decisions into the bankruptcy game, we propose a caching value assessment algorithm based on analytic hierarchy process in the framework of bankruptcy game theory to optimize the caching strategy and increase cache hit ratio. Our analysis shows that resource utilization can be improved through cooperative sharing while considering content providers' satisfaction. When the cache value is measured by multiple factors, not just popularity, the cache hit rate for user access is also increased. Simulation results show that our approach can improve the cache hit rate while ensuring the fairness of the distribution.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.911-931
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• 2021

Recently, most cloud services use Docker container environment to provide their services. However, there are no researches to evaluate the performance of communication libraries for multi-GPU based distributed deep learning in a Docker container environment. In this paper, we propose an efficient communication architecture for multi-GPU based deep learning in a Docker container environment by evaluating the performances of various communication libraries. We compare the performances of the parameter server architecture and the All-reduce architecture, which are typical distributed deep learning architectures. Further, we analyze the performances of two separate multi-GPU resource allocation policies - allocating a single GPU to each Docker container and allocating multiple GPUs to each Docker container. We also experiment with the scalability of collective communication by increasing the number of GPUs from one to four. Through experiments, we compare OpenMPI and MPICH, which are representative open source MPI libraries, and NCCL, which is NVIDIA's collective communication library for the multi-GPU setting. In the parameter server architecture, we show that using CUDA-aware OpenMPI with multi-GPU per Docker container environment reduces communication latency by up to 75%. Also, we show that using NCCL in All-reduce architecture reduces communication latency by up to 93% compared to other libraries.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.615-629
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• 2021

With the development of mobile edge computing, how to utilize the computing power of edge computing to effectively and efficiently offload data and to compute offloading is of great research value. This paper studies the computation offloading problem of multi-user and multi-server in mobile edge computing. Firstly, in order to minimize system energy consumption, the problem is modeled by considering the joint optimization of the offloading strategy and the wireless and computing resource allocation in a multi-user and multi-server scenario. Additionally, this paper explores the computation offloading scheme to optimize the overall cost. As the centralized optimization method is an NP problem, the game method is used to achieve effective computation offloading in a distributed manner. The decision problem of distributed computation offloading between the mobile equipment is modeled as a multi-user computation offloading game. There is a Nash equilibrium in this game, and it can be achieved by a limited number of iterations. Then, we propose a distributed computation offloading algorithm, which first calculates offloading weights, and then distributedly iterates by the time slot to update the computation offloading decision. Finally, the algorithm is verified by simulation experiments. Simulation results show that our proposed algorithm can achieve the balance by a limited number of iterations. At the same time, the algorithm outperforms several other advanced computation offloading algorithms in terms of the number of users and overall overheads for beneficial decision-making.

• Journal of IKEEE
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• v.22 no.2
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• pp.281-288
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• 2018

In this paper, we propose an intelligent face recognition and tracking system that distributes GPU resources using CUDA. The proposed system consists of five steps such as GPU allocation algorithm that distributes GPU resources in optimal state, face area detection and face recognition using deep learning, real time face tracking, and PTZ camera control. The GPU allocation algorithm that distributes multi-GPU resources optimally distributes the GPU resources flexibly according to the activation level of the GPU, unlike the method of allocating the GPU to the thread fixedly. Thus, there is a feature that enables stable and efficient use of multiple GPUs. In order to evaluate the performance of the proposed system, we compared the proposed system with the non - distributed system. As a result, the system which did not allocate the resource showed unstable operation, but the proposed system showed stable resource utilization because it was operated stably. Thus, the utility of the proposed system has been demonstrated.

• The KIPS Transactions:PartC
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• v.16C no.4
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• pp.511-520
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• 2009

Grid technology enables high performance computation and mass data processing by connecting geographically distributed computing resources to high-speed network. To carry on grid tasks requiring guaranteed quality of service, we need to build converged infrastructure which controls and manages network resources as well as computing resources. In this paper, we defined grid network service interface and information model to support grid network path, resource creation and reservation. In addition to resource creation and reservation messages of the existing GLIF, grid network path and notification messages were defined in the grid network service interface. We also defined related statuses and messages for two-phase resource reservation mechanism. We designed and implemented network resource management system based on the grid network information model and service interface. We experimented in resource reservation, allocation and release of network resources in grid environment with GMPLS control and transport network.

• Journal of the Korea Society of Computer and Information
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• v.17 no.12
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• pp.131-136
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• 2012

In these days, with the diffusion of mobile equipments, the number of WiFi Access Point (AP) is increasing, and the growth of WiFi AP causes the throughput degradation due to interferences between APs. This recent phenomenon demands the method able to be utilized with current WiFi network to improve the throughput of Wireless LANs. This paper studied the channel assignment method and several throughput enhancement methods to optimize Radio Resource Management (RRM) for distributed infrastructure WLANs. As a result, it was able to put AP independently, improve older allocation error, and improve execution speed.