• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.154-158
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• 1997

This paper proposes a new resource allocation system where overall performance can be improved using production smoothing method. In economic point of view, market price is determined by the market mechanism that is subject to the law of demand and supply. Similarly, agents determine whether to allocate tasks to machines by profit and loss or not. In existing resource allocation system, tasks are exclusively allocated to agents with better manufacturing conditions, because they are evaluated by the only currency. But in the proposed resource allocation system, agents are evaluated by not only a currency but also machine specifications. Hereby, the production smoothing is achieved and we expect to improve system performance In this study, we propose a resource allocation system with consideration of Production Smoothing.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.167-170
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• 2004

This paper proposes resource allocation method for achieving investment goals in manufacturing system. In order to align resource allocation and manufacturing system design, the system design decomposition (SDD) approach is used. In this paper, a mathematical formulation for resource allocation based on SDD approach is analyzed and a genetic algorithm application is discussed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2282-2303
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• 2021

To solve the problems of heavy computing load and system transmission pressure in energy internet (EI), we establish a three-tier cloud-edge integrated EI network based on a cloud-edge collaborative computing to achieve the tradeoff between energy consumption and the system delay. A joint optimization problem for resource allocation and task offloading in the threetier cloud-edge integrated EI network is formulated to minimize the total system cost under the constraints of the task scheduling binary variables of each sensor node, the maximum uplink transmit power of each sensor node, the limited computation capability of the sensor node and the maximum computation resource of each edge server, which is a Mixed Integer Non-linear Programming (MINLP) problem. To solve the problem, we propose a joint task offloading and resource allocation algorithm (JTOARA), which is decomposed into three subproblems including the uplink transmission power allocation sub-problem, the computation resource allocation sub-problem, and the offloading scheme selection subproblem. Then, the power allocation of each sensor node is achieved by bisection search algorithm, which has a fast convergence. While the computation resource allocation is derived by line optimization method and convex optimization theory. Finally, to achieve the optimal task offloading, we propose a cloud-edge collaborative computation offloading schemes based on game theory and prove the existence of Nash Equilibrium. The simulation results demonstrate that our proposed algorithm can improve output performance as comparing with the conventional algorithms, and its performance is close to the that of the enumerative algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1258-1275
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• 2023

Cloud computing is a new technology that has adapted to the traditional way of service providing. Service providers are responsible for managing the allocation of resources. Selecting suitable containers and bandwidth for job scheduling has been a challenging task for the service providers. There are several existing systems that have introduced many algorithms for resource allocation. To overcome these challenges, the proposed system introduces an Optimized Task Scheduling Algorithm with Deep Learning (OTS-DL). When a job is assigned to a Cloud Service Provider (CSP), the containers are allocated automatically. The article segregates the containers as' Long-Term Container (LTC)' and 'Short-Term Container (STC)' for resource allocation. The system leverages an 'Optimized Task Scheduling Algorithm' to maximize the resource utilisation that initially inquires for micro-task and macro-task dependencies. The bottleneck task is chosen and acted upon accordingly. Further, the system initializes a 'Deep Learning' (DL) for implementing all the progressive steps of job scheduling in the cloud. Further, to overcome container attacks and errors, the system formulates a Container Convergence (Fault Tolerance) theory with high-level security. The results demonstrate that the used optimization algorithm is more effective for implementing a complete resource allocation and solving the large-scale optimization problem of resource allocation and security issues.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.140.3-140
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• 2001

This paper analyzes a blocking that is due to shared resource in multiprocessor system. A proposed analysis for shared resource suggests a scalable and amendable scheduling method about task allocation. An equation of shared resource blocking is proposed by a throughput at common bus and a ratio of throughput during time period, it is included a parameter of tasks scheduling. Using this equation, a new guideline for task allocation of multiprocessor is presented. Finally, in proposed system a model simulations for the proposed blocking model is given by a deterministic ratio of shared resource.

• Journal of Information Processing Systems
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• v.18 no.3
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• pp.389-401
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• 2022

The business of Internet of Vehicles (IoV) is growing rapidly, and the large amount of data exchange has caused problems of large mobile network communication delay and large energy loss. A strategy for resource allocation of IoV communication based on mobile edge computing (MEC) is thus proposed. First, a model of the cloud-side collaborative cache and resource allocation system for the IoV is designed. Vehicles can offload tasks to MEC servers or neighboring vehicles for communication. Then, the communication model and the calculation model of IoV system are comprehensively analyzed. The optimization objective of minimizing delay and energy consumption is constructed. Finally, the on-board computing task is coded, and the optimization problem is transformed into a knapsack problem. The optimal resource allocation strategy is obtained through genetic algorithm. The simulation results based on the MATLAB platform show that: The proposed strategy offloads tasks to the MEC server or neighboring vehicles, making full use of system resources. In different situations, the energy consumption does not exceed 300 J and 180 J, with an average delay of 210 ms, effectively reducing system overhead and improving response speed.

• Journal of Korea Multimedia Society
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• v.16 no.7
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• pp.862-869
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• 2013

In order to support a cell-independent traffic asymmetry, the conventional TDD system cannot avoid crossed time slot (CTS) interference. Moreover, the TDD/FDD hierarchical overlay cellular systems is taken into account as a generally accepted cell model in a heterogeneous radio environment. In this paper, we propose an interference resolving radio resource allocation technique in a TDD-OFDMA cellular system that overlays a FDD-CDMA cell. In our proposed scheme, we exploit under-used FDD-CDMA uplink resource by TDD mobile abiding by a region based time slot(TS) allocation which in turn mitigates CTS interference considerably. It is demonstrated that combined with under-used resource utilization scheme based on mobile's location, the proposed technique can reduce CTS interference considerably and support the asymmetric traffic in TDD system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.10
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• pp.2395-2410
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• 2013

Many telecommunication operators around the world have multiple networks. The networks run by each operator are always of different generations, such as 2G and 3G or even 4G systems. Each system has unique characters and specified requirements for optimal operation. It brings about resource allocation problem among these networks for the operator, because the budget of each operator is limited. However, the evaluation of resource allocation among various networks under each operator is missing for long, not to mention resource allocation optimization. The operators are dying for an algorithm to end their blind resource allocation, and the Resource Allocation Optimization Algorithm for Multi-network Operator (RAOAMO) proposed in this paper is what the operators want. RAOAMO evaluates and optimizes resource allocation in the view of overall cost for each operator. It outputs a resource distribution target and corresponding optimization suggestion. Evaluation results show that RAOAMO helps operator save overall cost in various cases.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.393-402
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• 2017

Just like terrestrial cellular networks, underwater cellular communication networks, which can manage the overall network resource by adaptively allocating backhaul resource for each base station according to its ingress traffic, are necessary. In this paper, a new resource allocation protocol is proposed for the underwater cellular communication network, allocating backhaul resource of a base station proportional to its ingress traffic to the base station. This protocol is classified into two types dependent upon allocation period: the resource allocation protocol with adaptive period and that with fixed period. In order to determine a proper resource allocation protocol, the performance of the two protocols, in terms of reception rate, message overhead, and latency is compared and investigated via simulation. As a result, the resource protocol with adaptive period outperforms that with fixed period; the resource allocation protocol with fixed period results in a maximum of $10^2$ order longer queueing delay as well as $10^2$ order greater message overhead than that with adaptive period.

• The KIPS Transactions:PartA
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• v.19A no.3
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• pp.161-168
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• 2012

The study of Cloud based system is emerging to become the core technology in IT field due to the tremendous growth of Cloud Computing. Researches to deliver applications to Thin-Client based mobile virtual machine and Desktop as a Service(DaaS) using Cloud Computing are conducted actively. In this paper, we propose a Cloud system to run the mobile application in the mobile Thin-Client device and resource allocation mechanism Dynamic Resource Allocation Manager for Mobile Application(DRAMMA). Thus, through performance check, we show DRAMMA has improved the utilization of Cloud system, less migration of virtual machines and decreased the error rate of resource allocation. Also our proposed system delivers service more efficiently than the previous resource allocation algorithm.