• Structural Engineering and Mechanics
• /
• v.15 no.4
• /
• pp.451-462
• /
• 2003

In this paper, a new block iterative algorithm is presented by using the special feature of the continuous Riccati equation in the optimal shape control. Because the real-time control require that the CPU time should be as short as possible, an appropriate modal control algorithm is sought. The computing cost is less than the one of the all state feedback control. A numerical example is given to illustrate the algorithm.

• Journal of Broadcast Engineering
• /
• v.19 no.1
• /
• pp.24-30
• /
• 2014

This study investigates computing offloading effect of cloud in real-time video personal broadcast service, whose server is mobile device. Mobile device does not have enough computing resource for encoding video. The computing burden is offloaded to cloud, which has abundant resources in terms of computing, power, and storage compared to mobile device. By reducing computing burden, computation energy can be saved while transmission data amount increases because of decreasing compression efficiency. This study shows that the optimal operation point can be found adaptively to time-varying LTE communication condition result of tradeoff analysis between offloaded computation burden and increase in amount of transmitted data.

• Electronics and Telecommunications Trends
• /
• v.33 no.5
• /
• pp.111-120
• /
• 2018

Agent-based modeling and simulation (ABMS) is a computational method for analyzing research targets through observations of agent-to-agent interactions, and can be applied to multidimensional policy experiments in various fields of social sciences to support policy and decision making. Recently, according to increasing complexity of society and the rapid growth of collected data, the need for high-speed processing is considered to be more important in this field. For this reason, in the ABMS research field, a scalable and large-scale computing infrastructure is becoming an essential element, and cloud computing has been considered a promising infrastructure of ABMS. This paper surveys the technology trends of ABMS tools, cloud computing-based modeling, and simulation studies, and forecasts the use of cloud-computing infrastructure for future modeling and simulation tools. Although fundamental studies are underway to apply and operate cloud computing in the areas of modeling and simulation, new and additional studies are required to devise an optimal cloud computing infrastructure to satisfy the needs of large-scale ABMS.

• Journal of Korea Society of Industrial Information Systems
• /
• v.26 no.3
• /
• pp.1-7
• /
• 2021

Appropriate containers are deployed to cope with new request arrivals at Fog Computing (FC) hosts. In the case, we can consider two scenarios: (1) the requests may be queued until sufficient resources are prepared for the container deployments; (2) FC hosts may transfer arrived service requests to nearby FC hosts when they cannot accommodate new container deployments due to their limited or insufficient resources. Herein, for more employed neighboring FC hosts, arrived service requests may experience shorter waiting time in container deployment queue of each FC host. In contrast, they may take longer transfer time to pass through increased number of FC hosts. For this reason, there exists a trade-off relationship in the container deployment time depending on the number of employed FC hosts accommodating service request arrivals. Consequently, we numerically analyze the trade-off relationship to employ optimal number of neighboring FC hosts.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.6
• /
• pp.2060-2073
• /
• 2022

Cloud Computing permits users to access vast amounts of services of computing power in a virtualized environment. Providing secure services is essential. There are several problems to real-world optimization that are dynamic which means they tend to change over time. For these types of issues, the goal is not always to identify one optimum but to keep continuously adapting to the solution according to the change in the environment. The problem of scheduling in Cloud where new tasks keep coming over time is unique in terms of dynamic optimization problems. Until now, there has been a large majority of research made on the application of various Evolutionary Algorithms (EAs) to address the issues of dynamic optimization, with the focus on the maintenance of population diversity to ensure the flexibility for adapting to the changes in the environment. Generally, trust refers to the confidence or assurance in a set of entities that assure the security of data. In this work, a dynamic optimization technique is proposed to find an optimal trust weights in cloud during scheduling.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.19 no.40
• /
• pp.169-178
• /
• 1996

Geographically dispersed computing system is made of computers interconnected by a telecommunications network. To make the system operated efficiently, system designer must determine the allocation of data files to each node. In designing such distributed computing system, the most important issue is the determination of the numbers and the locations where database files are allocated. This is commonly referred to as the file allocation problem (FAP)[3]. The proposed model is a 0/l integer programming problem minimizing the sum of file storage costs and communication(query and update) costs. File allocation problem belongs to the class of NP-Complete problems. Because of the complexity, it is hard to solve. So, this paper presents an efficient heuristic algorithm to solve the file allocation problem using Tabu Search Technique. By comparing the optimal solutions with the heuristic solutions, it is believed that the proposed heuristic algorithm gives good solutions. Through the experimentation of various starting points and tabu restrictions, this paper presents fast and efficient method to solve the file allocation problem in the distributed computing system.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.10
• /
• pp.4025-4041
• /
• 2020

Mobile edge computing (MEC) is capable of providing services to smart devices nearby through radio access networks and thus improving service experience of users. In this paper, an offloading strategy for the joint optimization of computing and communication resources in multi-user and multi-MEC overlapping scene was proposed. In addition, under the condition that wireless transmission resources and MEC computing resources were limited and task completion delay was within the maximum tolerance time, the optimization problem of minimizing energy consumption of all users was created, which was then further divided into two subproblems, i.e. offloading strategy and resource allocation. These two subproblems were then solved by the game theory and Lagrangian function to obtain the optimal task offloading strategy and resource allocation plan, and the Nash equilibrium of user offloading strategy games and convex optimization of resource allocation were proved. The simulation results showed that the proposed algorithm could effectively reduce the energy consumption of users.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2000.10a
• /
• pp.302-305
• /
• 2000

The operation of a networked computing system (NCS), such as Internet, can be viewed as a resource allocation problem, and can be analyzed using the techniques of mathematical modeling. We define a general NCS and translate that setup into a model of an economy. The preferences of users are taken as primitives, and servers in the network are viewed as productive firms with priority input queues. Each sewer charges a rental price for its services by priority class. We characterize optimal system allocation, and derive formulae for supporting rental prices and priority premia such that the aggregated individual user demands do not exceed optimal levels and waiting-time expectations are correct. Our economic approach has the added benefit of providing a sound basis for evaluating NCS investment alternatives, using a process analogous to free entry and exit in free-enterprise economies.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.27 no.3
• /
• pp.145-156
• /
• 2002

Business managers are often required to use LP problems to deal with uncertainty inherent in decision making due to rapid changes in today＇s business environments. Uncertain parameters can be easily formulated in the two-stage stochastic LP problems. However, since solution methods are complex and time-consuming, a common approach has been to use modified formulations to provide upper and lower bounds on the two-stage stochastic LP problem. One approach is to use an expected value problem, which provides upper and lower bounds. Another approach is to use “walt-and-see” problem to provide upper and lower bounds. The objective of this paper is to propose a modified approach of “wait-and-see” problem to provide an upper bound and to compare the relative error of optimal value with various upper and lower bounds. A computing experiment is implemented to show the relative error of optimal value with various upper and lower bounds and computing times.

• Journal of Information Processing Systems
• /
• v.7 no.3
• /
• pp.397-412
• /
• 2011

Granular Computing has emerged as a unified and coherent framework of designing, processing, and interpretation of information granules. Information granules are formalized within various frameworks such as sets (interval mathematics), fuzzy sets, rough sets, shadowed sets, probabilities (probability density functions), to name several the most visible approaches. In spite of the apparent diversity of the existing formalisms, there are some underlying commonalities articulated in terms of the fundamentals, algorithmic developments and ensuing application domains. In this study, we introduce two pivotal concepts: a principle of justifiable granularity and a method of an optimal information allocation where information granularity is regarded as an important design asset. We show that these two concepts are relevant to various formal setups of information granularity and offer constructs supporting the design of information granules and their processing. A suite of applied studies is focused on knowledge management in which case we identify several key categories of schemes present there.