• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권6호
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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.

• ETRI Journal
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• 제44권5호
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• pp.733-745
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• 2022

In software-defined wireless networking (SDWN), the optimal routing technique is one of the effective solutions to improve its performance. This routing technique is done by many different methods, with the most common using integer linear programming problem (ILP), building optimal routing metrics. These methods often only focus on one routing objective, such as minimizing the packet blocking probability, minimizing end-to-end delay (EED), and maximizing network throughput. It is difficult to consider multiple objectives concurrently in a routing algorithm. In this paper, we investigate the application of machine learning to control routing in the SDWN. An intelligent routing algorithm is then proposed based on the machine learning to improve the network performance. The proposed algorithm can optimize multiple routing objectives. Our idea is to combine supervised learning (SL) and reinforcement learning (RL) methods to discover new routes. The SL is used to predict the performance metrics of the links, including EED quality of transmission (QoT), and packet blocking probability (PBP). The routing is done by the RL method. We use the Q-value in the fundamental equation of the RL to store the PBP, which is used for the aim of route selection. Concurrently, the learning rate coefficient is flexibly changed to determine the constraints of routing during learning. These constraints include QoT and EED. Our performance evaluations based on OMNeT++ have shown that the proposed algorithm has significantly improved the network performance in terms of the QoT, EED, packet delivery ratio, and network throughput compared with other well-known routing algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권12호
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• pp.4364-4384
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• 2021

The dynamic network state and the mobility of the terminals make the service function chain (SFC) orchestration mechanisms based on static and deterministic assumptions hard to be applied in SDN/NFV mobile edge computing networks. Designing dynamic and online SFC orchestration mechanism can greatly improve the execution efficiency of compute-intensive and resource-hungry applications in mobile edge computing networks. In order to increase the overall profit of service provider and reduce the resource cost, the system running time is divided into a sequence of time slots and a dynamic orchestration scheme based on an improved column generation algorithm is proposed in each slot. Firstly, the SFC dynamic orchestration problem is formulated as an integer linear programming (ILP) model based on layered graph. Then, in order to reduce the computation costs, a column generation model is used to simplify the ILP model. Finally, a two-stage heuristic algorithm based on greedy strategy is proposed. Four metrics are defined and the performance of the proposed algorithm is evaluated based on simulation. The results show that our proposal significantly provides more than 30% reduction of run time and about 12% improvement in service deployment success ratio compared to the Viterbi algorithm based mechanism.

• 한국정보과학회논문지:소프트웨어및응용
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• 제34권5호
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• pp.397-406
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• 2007

두 서열 A와 B간의 최적정렬을 찾는 문제는 동적프로그래밍 알고리즘을 사용하여 효과적으로 해결 될 수 있다. 하지만, 길이가 각각 m, n인 두 서열, $S_1$, $S_2$를 정렬하기 위해서는 O(m*n)의 시간과 공간 복잡도를 갖기 때문에 서열의 길이가 길어질 경우에는 시간과 공간 비용 문제로 인해 적용 할 수 없게 된다. 실제 계산상에 제한요소로 작용하는 공간비용 문제를 해결하기 위해 Hirschberg에 의해 제시된 선형공간 알고리즘은 이 문제를 O(n*m)의 시간복잡도와 O(n+m)의 공간복잡도로서 해결하였다. 컴퓨터 기술의 발전으로 CPU의 처리속도가 향상되고, 사용가능한 주기억장치의 공간이 확대됨에 따라, 기억공간은 더 사용하더라도 처리속도는 높일 수 있는 방법이 필요하다. 이를 위해, 본 논문에서는 공간 분할 방법을 통하여 공간 소모는 선형공간 알고리즘보다 많지만, 처리 속도는 빠른 O(n*m)의 시간과 O(n+m)의 공간비용을 갖는 알고리즘을 제안한다. 또한 분할 시 서열의 길이변화에 따른 분할 수(d) 문제를 일반화하고, 입/출구 노드 개념을 이용하여 불필요한 연산을 제거하였다. 선형공간 알고리즘이 (m+n)의 공간으로 2*m*n에 가까운 속도를 갖는데 비해, 본 알고리즘은 (m+n)*d의 공간으로 m*n에 가까운 결과를 보임을 증명과 실험결과로부터 확인한다.

• 대한교통학회지
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• 제30권1호
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• pp.125-133
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• 2012

본 논문에서는 스마트카드 데이터를 이용하여 서울시의 대중교통 환승통행 현황을 분석하고, 이를 통해 환승체계 효율화의 필요성을 살펴보았다. 특별히 환승효율성 제고를 위한 환승센터 건립 시 그 입지선정을 위한 수리모형을 이중구조모형으로 구축/제시하였다. 상위모형은 입지결정 모형으로 지역별 환승센터 개수 및 총건설비용 예산 제약하에서 총통행비용을 최소화하는 선형 0-1 정수모형이고, 하위 모형은 환승센터 입지 여부에 따른 사용자 평형 경로 선택 모형으로 구성된다. 모형의 해법으로 상위 및 하위 모형의 해를 순차적으로 구하는 방법을 제시하였고, 예제네트워크 적용을 통해 해법의 수렴성과 모형의 적용성을 평가하였다. 그 결과 본 논문에서 구축된 모형과 해법을 이용하여 효과적으로 최적의 환승센터 입지를 결정할 수 있음을 확인하였다. 마지막으로, 실제 네트워크에 적용하기 위한 방법론을 검토한 결과 본 논문의 모형과 해법이 현실에서도 충분히 활용될 수 있을 것으로 기대된다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권8호
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• pp.3474-3497
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• 2016

Network Function Virtualization (NFV) and Software Defined Networking (SDN) are recently considered as very promising drivers of the evolution of existing middlebox services, which play intrinsic and fundamental roles in today's networks. To address the virtual service deployment issues that caused by introducing NFV or SDN to networks, this paper proposes an optimal solution by combining quantum genetic algorithm with cooperative game theory. Specifically, we first state the concrete content of the service deployment problem and describe the system framework based on the architecture of SDN. Second, for the service location placement sub-problem, an integer linear programming model is built, which aims at minimizing the network transport delay by selecting suitable service locations, and then a heuristic solution is designed based on the improved quantum genetic algorithm. Third, for the service amount placement sub-problem, we apply the rigorous cooperative game-theoretic approach to build the mathematical model, and implement a distributed algorithm corresponding to Nash bargaining solution. Finally, experimental results show that our proposed method can calculate automatically the optimized placement locations, which reduces 30% of the average traffic delay compared to that of the random placement scheme. Meanwhile, the service amount placement approach can achieve the performance that the average metric values of satisfaction degree and fairness index reach above 90%. And evaluation results demonstrate that our proposed mechanism has a comprehensive advantage for network application.

• 전기학회논문지
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• 제67권10호
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• pp.1278-1285
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• 2018

For saving electricity bill, energy storage system(ESS) is being installed in factories, public building and commercial building with a Time-of-Use(TOU) tariff which consists of demand charge(KRW/kW) and energy charge(KRW/kWh). However, both of peak reduction and ESS special tariff are not considered in an analysis of initial cost payback period(ICPP) on ESS. Since it is difficult to reflect base rate by an amount of uncertain peak demand reduction during mid-peak and on-peak periods in the future days. Therefore, the ICPP on ESS can be increased. Based on this background, this paper presents the advanced analysis method for the ICPP on ESS. In the proposed algorithm, the representative days of monthly electricity consumption pattern for the amount of peak reduction can be found by the k­means clustering algorithm. Moreover, the total expected energy costs of representative days are minimized by optimal daily ESS operation considering both peak reduction and the special tariff through a mixed-integer linear programming(MILP). And then, the amount of peak reduction becomes a value that the sum of the expected energy costs for 12 months is maximum. The annual benefit cost is decided by the amount of annual peak reduction. Two simulation cases are considered in this study, which one only considers the special tariff and another considers both of the special tariff and amount of peak reduction. The ICPP in the proposed method is shortened by 18 months compared to the conventional method.

• 한국통신학회논문지
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• 제40권6호
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• pp.1032-1039
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• 2015

서비스 기능 체이닝(SFC: Service function chaining)은 다수의 서비스 기능들을 순차적으로 구성하는 기술이다. 서비스 기능 체이닝에서는 확장성과 fault-tolerant를 위해 다수의 서비스 기능 인스턴스가 필요하며, 네트워크에 인입된 플로우는 다수의 서비스 기능 인스턴스로 적절하게 분배되어야 한다. 따라서 본 논문에서는 각 서비스 기능인스턴스들의 자원을 고려하면서 종단 간 지연시간(latency)을 최소화 할 수 있는 플로우 분배 문제를 정의한다. 또한 GT-ITM 토폴로지 생성 툴을 사용하여 보다 현실적인 네트워크 토폴로지 상에서 시뮬레이션을 수행하고, 그 결과 최적의 플로우 분배 기법이 전체 지연시간을 줄일 수 있음을 확인하였다.

• 한국철도학회논문집
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• 제10권6호
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• pp.742-750
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• 2007

본 논문은 장래 도입되는 KTX2 고속차량을 이용한 중련편성 운영계획 최적화를 위한 방법론 제시를 목적으로 한다. 중련편성 운영계획 최적화를 위한 수리적 모형을 제시하고, 제안된 수리적 모형의 적용방안을 제시한다. 특히 확률적 수요를 고려한 모형을 수립하여, 계획수립 단계에서 수요의 확률적 요소를 보다 현실적으로 반영하고, 모형내에서 좌석 서비스 수준을 직접 조정할 수 있다.

• 산업경영시스템학회지
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• 제40권1호
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• pp.11-21
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• 2017

Artillery fire power due to effectiveness which is hard to predict well-planned and surprising attack can give a fear and shock to the personnel and is a very core weapon system and takes a critical role in wartime. Therefore in order to maximize operational effectiveness, Army required protecting artillery and takes a quick attack action through rapid construction of artillery's positions. The artillery use artillery's position to prevent exposure by moving to other position frequently. They have to move and construct at new artillery's positions quickly against exposing existed place by foe's recognition. These positions should be built by not manpower but engineering construction equipment. Because artillery positions have to protect human and artillery equipment well and build quickly. Military engineering battalion have lots of construction equipment which include excavator, loader, dozer, combat multi-purposed excavator, armored combat earthmover dump truck and so on. So they have to decide to optimal number of Team combining these equipments and determine construction sequence of artillery's position in operational plan. In this research, we propose to decide number of Team efficiently and allocate required construction's positions for each Team under constraints of limited equipments and time. To do so, we develop efficient heuristic method which can give near optimal solution and be applied to various situation including commander's intention, artillery position's priority or grouping etc. This heuristic can support quick and flexible construction plan of artillery positions not only for using various composition's equipment to organize Teams but also for changing quantity of positions.