• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.8
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• pp.1825-1842
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• 2013

Rapid growth of the IT industry has led to significant energy consumption in the last decade. Data centers swallow an enormous amount of electrical energy and have high operating costs and carbon dioxide excretions. In response to this, the dynamic consolidation of virtual machines (VMs) allows for efficient resource management and reduces power consumption through the live migration of VMs in the hosts. Moreover, each client typically has a service level agreement (SLA), this leads to stipulations in dealing with energy-performance trade-offs, as aggressive consolidation may lead to performance degradation beyond the negotiation. In this paper we propose a heuristic based resource allocation of VM selection and a VM allocation approach that aims to minimize the total energy consumption and operating costs while meeting the client-level SLA. Our experiment results demonstrate significant enhancements in cloud providers' profit and energy savings while improving the SLA at a certain level.

• Journal of Communications and Networks
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• v.12 no.1
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• pp.30-42
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• 2010

In this work we consider the problem of downlink resource allocation for proportional fairness of long term received rates of data users and quality of service for real time sessions in an OFDMA-based wireless system. The base station allocates available power and subchannels to individual users based on long term average received rates, quality of service (QoS) based rate constraints and channel conditions. We formulate and solve a joint bandwidth and power optimization problem, solving which provides a performance improvement with respect to existing resource allocation algorithms. We propose schemes for flat as well as frequency selective fading cases. Numerical evaluation results show that the proposed method provides better QoS to voice and video sessions while providing more and fair rates to data users in comparison with existing schemes.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.3
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• pp.46-58
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• 1998

Multi-disk data allocation problem examined in this paper is to find a method to distribute a Binary Cartesian Product File on multiple disks to maximize parallel disk I/O accesses for partial match retrieval. This problem is known to be NP-hard, and heuristkc approaches have been applied to obtain sub-optimal solutions. Recently, efficient methods have been proposed with a restriction that the number of disks in which files are stored should be power of 2. In this paper, we propose a new disk Allocation method based on Genetic Algorithm(GA) to remove the restriction on the number of disks to be applied. Using the schema theory, we prove that our method can find a near-optimal solutionwith high probability. We compare the quality of solution derived by our method with General Disk Modulo, Binary Disk Modulo, and Error Correcting Code methods through the simulation. The simulation results show that proposed GA is superior to GDM method in all cases and provides comparable performance to the BDM method which has a restriction on the number of disks.

• Journal of the Korean Magnetics Society
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• v.22 no.2
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• pp.33-36
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• 2012

We propose a simple prescription for resolving the general-$N$ problem existing in the old-fashioned mixed-radix fast Fourier transformation FORTRAN subroutine by Singleton in 1968. After a brief investigation on the problem, we discuss our prescription with the worst case analysis within the dynamical allocation. The analysis reveals that our implementation is superior, at least for multi-variate data set, than previously proposed data copying methods.

• The Journal of Information Technology and Database
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• v.5 no.1
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• pp.1-12
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• 1998

분산 환경에서 데이터의 할당(allocation)는 중요한 설계 이슈이다. 데이터의 할당은 분산 데이터에 대한 비용(cost) 감소, 성능(performance) 및 가용성(availability) 향상 등의 이점을 극대화할 수 있도록 최적화되어야 한다. 기존 연구들의 대부분은 트랜잭션의 수행 비용을 최소화하는 방향으로만 최적화된 데이터 할당 결과를 제시하고 있다. 즉, 비용, 성능 및 가용성을 모두 함께 고려하는 연구는 아직까지 제시된 결과가 없으며 이는 복잡한 모델에 대한 적절한 최적화 기법이 없기 때문이다. 본 연구에서는 분산 데이터의 이점들인 비용, 성능 및 가용성 등의 다중측면을 동시에 고려함으로써 데이터 할당에 대한 파레토 최적해를 제공하는 DAMMA (Data Allocation Methodology considering Multiple Aspects) 방법론을 제안하였다. DAMMA 방법론은 데이터 분할 과정을 통하여 생성된 최적의 단편들을 분산 시스템의 운용 비용, 수행 성능, 가용성 등의 요소를 고려하여 각 물리적 사이트에 중복 할당하는 파레토 최적해들을 생성해낼 수 있는 설계 방법론이다.

• Journal of Korean Institute of Industrial Engineers
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• v.33 no.3
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• pp.322-328
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• 2007

Highly standardized products are suitable for automated purchasing using electronic commerce technology, where the price becomes the most important factor. Suppliers can change the prices dynamically based on the inventory level and market situation in order to maximize the sales and profit. In the virtual marketplace where multiple customers purchase multiple standardized products from multiple suppliers repetitively, customers can purchase the required amount of each item as a dynamic bidding by allocating purchase orders to the suppliers based on the current price. Customers need a method to quickly determine the optimal allocation of orders to the suppliers using the dynamically changing data to minimize the total cost. We present a LP model which minimizes the sum of the total price plus transportation cost for this problem. Simulation results using random data show meaningful reduction of the total cost.

• Journal of Information Processing Systems
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• v.20 no.1
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• pp.131-147
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• 2024

A routing strategy based on traffic prediction and dynamic cache allocation for satellite nodes is proposed to address the issues of high propagation delay and overall delay of inter-satellite and satellite-to-ground links in low Earth orbit (LEO) satellite systems. The spatial and temporal correlations of satellite network traffic were analyzed, and the relevant traffic through the target satellite was extracted as raw input for traffic prediction. An improved gradient boosting regression tree algorithm was used for traffic prediction. Based on the traffic prediction results, a dynamic cache allocation routing strategy is proposed. The satellite nodes periodically monitor the traffic load on inter-satellite links (ISLs) and dynamically allocate cache resources for each ISL with neighboring nodes. Simulation results demonstrate that the proposed routing strategy effectively reduces packet loss rate and average end-to-end delay and improves the distribution of services across the entire network.

• Journal of Korea Multimedia Society
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• v.3 no.6
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• pp.625-632
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• 2000

This paper proposes an adaptive multislot allocation algorithm in order to achieve large system capacity and higher throughput data transmissions. The proposed system is the combination of the slow adaptive modulation system, in which the base station dynamically a signs optimum modulation parameters measuring the CNR(carrier to noise power ratio) of each transmission terminal, and the multislot allocation scheme, in which the base station flexibly allocates an appropriate number of TDMA data slots according to the instantaneous load conditions. Computer simulations confirm that the proposed system can tremendously improve average message delay characteristics in comparison with the conventional fixed slot allocation method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.6
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• pp.1241-1246
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• 2005

This paper proposes an adaptive multislot allocation algorithm in order to achieve larger system capacity and higher throughput data transmissions. The proposed system is the combination of the slow adaptive modulation system, in which the base station dynamically assigns optimum modulation parameters measuring the SNR of each transmission terminal, and the multislot allocation scheme, in which the base station flexibly allocates an appropriate number of TDMA data slots according to the instantaneous load conditions. Computer simulations confirm that the proposed system can tremendously improve average message delay characteristics in comparison with the conventional fixed slot allocation method.

• The Transactions of the Korea Information Processing Society
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• v.7 no.4
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• pp.1082-1091
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• 2000

In Distributed Shared Memory systems, false sharing occurs when two different data items, not shared but accessed by two different processors, are allocated to a single block and is an important factor in degrading system performance. The paper first analyzes shared memory allocation and reference patterns in parallel applications that allocate memory for shared data objects using a dynamic memory allocator. The shared objects are sequentially allocated and generally show different reference patterns. If the objects with the same size are requested successively as many times as the number of processors, each object is referenced by only a particular processor. If the objects with the same size are requested successively much more than the number of processors, two or more successive objects are referenced by only particular processors. On the basis of these analyses, we propose a memory allocation scheme which allocates each object requested by different processors to different pages and evaluate the existing memory allocation techniques for reducing false sharing faults. Our allocation scheme reduces a considerable amount of false sharing faults for some applications with a little additional memory space.