• Proceedings of the IEEK Conference
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• 2003.07a
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• pp.61-64
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• 2003

GPRS is designed for transmitting packet data and supposed to take its radio resource form the pool of channels unused by GSM voice services. In this paper, The GPRS and GSM circuit switched services share the same radio resource. Whenever a channel is not used by circuit switched services, it may be utilized by GPRS. In this paper, the main aim is performance and comparison resource management policies with channel de-allocation in GPRS network. Three resource management policies is voice priority, R-reservation, dynamic reservation.

• Journal of Information Processing Systems
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• v.20 no.2
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• pp.226-238
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• 2024

Recently, multi-access edge computing (MEC) has emerged as a promising technology to alleviate the computing burden of vehicular terminals and efficiently facilitate vehicular applications. The vehicle can improve the quality of experience of applications by offloading their tasks to MEC servers. However, channel conditions are time-varying due to channel interference among vehicles, and path loss is time-varying due to the mobility of vehicles. The task arrival of vehicles is also stochastic. Therefore, it is difficult to determine an optimal offloading with resource allocation decision in the dynamic MEC system because offloading is affected by wireless data transmission. In this paper, we study computation offloading with resource allocation in the dynamic MEC system. The objective is to minimize power consumption and maximize throughput while meeting the delay constraints of tasks. Therefore, it allocates resources for local execution and transmission power for offloading. We define the problem as a Markov decision process, and propose an offloading method using deep reinforcement learning named deep deterministic policy gradient. Simulation shows that, compared with existing methods, the proposed method outperforms in terms of throughput and satisfaction of delay constraints.

• Journal of Korean Society for Quality Management
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• v.33 no.4
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• pp.21-41
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• 2005

This study analyzes the Internet utilization pattern of customer by comprehensively investigating the previous studies on the behavior pattern of customer in terms of Internet business. Based on the analysis, this study develops research framework that supports strategic decision-making for resource allocation in Internet business. Such research framework would be helpful for providing the typology of Internet business model that can be specialized by each industry. As a result of the simulation analysis, it was found that the optimal resource allocation portfolio providing maximum profits to the Internet bookstore involves large-scale investment on delivery service and customer support service which are the key factors for post-purchase customer satisfaction, regardless of the growth pattern or size of Internet bookstore market. Consequently, from the above analysis, the investment ratio of resources for the profit maximization of Internet bookstore was drawn. Conclusively, based on the comprehensive examination of the results, this study provided a framework for dynamic resource allocation decision-making, and proposed a management strategy which allows consumers to shop under more favorable environment, and simultaneously enables the Internet bookstore to accomplish management objectives such as continuous growth and profit maximization.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5357-5381
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• 2018

Cloud computing offers a wide range of on-demand resources over the internet. Utility-based resource allocation in cloud data centers significantly increases the number of cloud users. Heavy usage of cloud data center encounters many problems such as sacrificing system performance, increasing operational cost and high-energy consumption. Therefore, the result of the system damages the environment extremely due to heavy carbon (CO2) emission. However, dynamic allocation of energy-efficient resources in cloud data centers overcomes these problems. In this paper, we have proposed Energy and Service Level Agreement (SLA) Aware Resource Allocation Heuristic Algorithms. These algorithms are essential for reducing power consumption and SLA violation without diminishing the performance and Quality-of-Service (QoS) in cloud data centers. Our proposed model is organized as follows: a) SLA violation detection model is used to prevent Virtual Machines (VMs) from overloaded and underloaded host usage; b) for reducing power consumption of VMs, we have introduced Enhanced minPower and maxUtilization (EMPMU) VM migration policy; and c) efficient utilization of cloud resources and VM placement are achieved using SLA-aware Modified Best Fit Decreasing (MBFD) algorithm. We have validated our test results using CloudSim toolkit 3.0.3. Finally, experimental results have shown better resource utilization, reduced energy consumption and SLA violation in heterogeneous dynamic cloud environment.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.7B
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• pp.422-430
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• 2007

WDM-PON has been receiving much attention as a key technology to FTTH. As the cost of WDM-PON heavily depends on the number of transceivers implemented at ONU, one of its shortcomings comes from the fixed provision of wavelength resource, which is tightly coupled with costly transceivers. In this paper, we propose a cost-effective WDM-PON architecture for dynamic resource provision. The proposed architecture is based on hierarchical grouping of multiple ONUs into a logical group and hierarchical sharing the resource. For high utilization of dynamic resource, the dynamic resource allocation algorithm is also proposed. We analyze the system efficiency in term of dynamism of system resource and system cost. We also verify that the proposed resource allocation algorithm achieves a high dynamism on resource allocation, and at the same time maintains the fairness between ONUs.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.8C
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• pp.819-827
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• 2002

Personal mobile communication has been developed up to IMT-2000 which is called the third generation mobile communication. The first generation of personal mobile communication was analog cellular, the second was digital cellular, and the 2.5 generation was PCS. Before the third generation had been developed, the personal mobile communication service was focused mainly on the voice-oriented service. But, we can expect that multimedia service after the third generation system will be the core of the subject. T In this thesis, we propose a dynamic resource allocation algorithm in the UTRA TDD systems which can support the asymmetric traffic propensity and multimedia traffic. The proposed algorithm consists two sub-algorithms. One is the dynamic channel allocation method that determines the amount of bandwidth assigned between uplink and downlink according to resource allocation status list through asymmetric traffic propensity. The other is the channel access mechanism that assigns RU to bearer service generated in a cell. By simulation, the proposed dynamic resource allocation algorithm is proved to support asymmetric propensity of traffic and shows a better throughput for multimedia traffic.

• Journal of the Korean Operations Research and Management Science Society
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• v.32 no.3
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• pp.33-46
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• 2007

This study deals with the adaptive multiuser OFDM (Orthogonal Frequency Division Multiplexing) system which adjusts the resource allocation according to the environmental changes in such as wireless and quality of service required by users. The resource allocation includes subcarrier assignment to users, modulation method and power used for subcarriers. We first develop a general optimization model which maximizes data throughput while satisfying data rates required by users and total power constraints. Based on the property that this problem has the 0 duality gap, we apply the subgradient dual optimization method which obtains the solution of the dual problem by iteration of simple calculations. Extensive experiments with realistic data have shown that the subgradient dual method is applicable to the real world system, and can be used as a dynamic resource allocation mechanism.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.11A
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• pp.1120-1127
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• 2007

In this paper, we propose a generalized processor sharing - dynamic resource allocation (GPS-DRA) scheme which allocates the required amount of resources to each hop dynamically in cellular-based multi-hop systems. In the hybrid-distributed system considered in this paper, a central controller such as a base station (BS) should allocate resources properly to each hop. However, due to changing channel condition with time, it is difficult to allocate as much amount of resources as each hop needs for transmission. GPS-DRA scheme allocates the required amount of resources dynamically to each hop based on the amount of resources used in previous frames by each hop. The amount of control overhead generated by GPS-DRA scheme can be very small because a central controller doesn't need to collect all link information for resource allocation. Our simulation results show that channel utilization increased about 16% and cell capacity increased about 65% compared to those of fixed resource allocation (FRA) scheme.

• IEMEK Journal of Embedded Systems and Applications
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• v.11 no.3
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• pp.153-162
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• 2016

This paper proposes an optimal dynamic resource allocation method in IoT (Internet of Things) parking guidance system using Q-learning resource allocation model. In the proposed method, a resource allocation using a forecasting model based on Q-learning is employed for optimal utilization of parking guidance system. To demonstrate efficiency and availability of the proposed method, it is verified by computer simulation and practical testbed. Through simulation results, this paper proves that the proposed method can enhance total throughput, decrease penalty fee issued by SLA (Service Level Agreement) and reduce response time with the dynamic number of users.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.7
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• pp.512-518
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• 2013

A resource allocation algorithm has a high impact on user satisfaction as well as the ability to accommodate and process services in a distributed cloud computing. In other words, service rejections, which occur when datacenters have no enough resources, degrade the user satisfaction level. Therefore, in this paper, we propose a resource allocation algorithm considering the cloud domain's remaining resources to minimize the number of service rejections. The resource allocation rate based on Q-Learning increases when the remaining resources are sufficient to allocate the maximum allocation rate otherwise and avoids the service rejection. To demonstrate, We compare the proposed algorithm with two previous works and show that the proposed algorithm has the smaller number of the service rejections.