• The Journal of the Korea Contents Association
• /
• v.15 no.4
• /
• pp.9-16
• /
• 2015

On modern operating systems such as Linux, virtual memory is a general way to provide a large address space to applications by using main memory and storage devices. Recently, storage devices have been improved in terms of latency and bandwidth, and it is expected that applications with large memory show high-performance if next-generation storage devices are considered. However, due to the overhead of virtual memory subsystem, the paging system can not exploit the performance of next-generation storage devices. In this study, we propose several optimization techniques to extend memory with next-generation storage devices. The techniques are to allocate block addresses of storage devices for write-back operations as well as to configure the system parameters, and we implement the techniques on Linux 3.14.3. Our evaluation through using multiple benchmarks shows that our system has 3 times (/24%) better performance on average than the baseline system in the micro(/macro)-benchmark.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.33 no.2
• /
• pp.48-55
• /
• 2010

Reliability has been considered as a one of the major design measures in various industrial and military systems. The main objective is to suggest a mathematical programming model and a hybrid parallel genetic algorithm(HPGA) for the problem that determines the optimal component reliability to maximize the system reliability under cost constraint in this study. Reliability optimization problem has been known as a NP-hard problem and normally formulated as a mixed binary integer programming model. Component structure, reliability, and cost were computed by using HPGA and compared with the results of existing meta-heuristic such as Ant Colony Optimization(ACO), Simulated Annealing(SA), Tabu Search(TS) and Reoptimization Procedure. The global optimal solutions of each problem are obtained by using CPLEX 11.1. The results of suggested algorithm give the same or better solutions than existing algorithms, because the suggested algorithm could paratactically evolved by operating several sub-populations and improving solution through swap and 2-opt processes.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.23 no.5
• /
• pp.151-158
• /
• 2023

The researchers entirely focused on meta-heuristic method for generalized assignment problem(GAP) that is known as NP-hard problem because of the optimal solution within polynomial time algorithm is unknown yet. On the other hand, this paper proposes a heuristic greedy algorithm with rules for finding solutions. Firstly, this paper reduces the weight matrix of original data to w_ij ≤ b_i/l in order to n jobs(items) pack m machines(bins) with l = n/m. The maximum profit of each job was assigned to the machine for the reduced data. Secondly, the allocation was adjusted so that the sum of the weights assigned to each machine did not exceed the machine capacity. Finally, the k-opt swap optimization was performed to maximize the profit. The proposed algorithm is applied to 50 benchmarking data, and the best known solution for about 1/3 data is to solve the problem. The remaining 2/3 data showed comparable results to metaheuristic techniques. Therefore, the proposed algorithm shows the possibility that rules for finding solutions in polynomial time exist for GAP. Experiments demonstrate that it can be a P-problem from an NP-hard.

• IE interfaces
• /
• v.23 no.2
• /
• pp.147-155
• /
• 2010

Reliability allocation is defined as a problem of determination of the reliability for subsystems and components to achieve target system reliability. The determination of both optimal component reliability and the number of component redundancy allowing mixed components to maximize the system reliability under resource constraints is called reliability-redundancy allocation problem(RAP). The main objective of this study is to suggest a mathematical programming model and a hybrid parallel genetic algorithm(HPGA) for reliability-redundancy allocation problem that decides both optimal component reliability and the number of component redundancy to maximize the system reliability under cost and weight constraints. The global optimal solutions of each example are obtained by using CPLEX 11.1. The component structure, reliability, cost, and weight were computed by using HPGA and compared the results of existing metaheuristic such as Genetic Algoritm(GA), Tabu Search(TS), Ant Colony Optimization(ACO), Immune Algorithm(IA) and also evaluated performance of HPGA. The result of suggested algorithm gives the same or better solutions when compared with existing algorithms, because the suggested algorithm could paratactically evolved by operating several sub-populations and improve solution through swap, 2-opt, and interchange processes. In order to calculate the improvement of reliability for existing studies and suggested algorithm, a maximum possible improvement(MPI) was applied in this study.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.16 no.1
• /
• pp.253-262
• /
• 2016

This paper proposes a balance-swap method for the dynamic economic load dispatch problem. Based on the premise that all generators shall be operated at valve-points, the proposed algorithm initially sets the maximum generation power at $P_i{\leftarrow}P_i^{max}$. As for generator i with $_{max}c_i$, which is the maximum operating cost $c_i=\frac{F(P_i)-F(P_{iv_k})}{(P_i-P_{iv_k})}$ produced when the generation power of each generator is reduced to the valve-point $v_k$, the algorithm reduces i's generation power down to $P_{iv_k}$, the valve-point operating cost. When ${\Sigma}P_i-P_d$ > 0, it reduces the generation power of a generator with $_{max}c_i$ of $c_i=F(P_i)-F(P_i-1)$ to $P_i{\leftarrow}P_i-1$ so as to restore the equilibrium ${\Sigma}P_i=P_d$. The algorithm subsequently optimizes by employing an adult-step method in which power in the range of $_{min}\{_{max}(P_i-P_i^{min}),\;_{max}(P_i^{max}-P_i)\}$>${\alpha}{\geq}10$ is reduced by 10; a baby step method in which power in the range of 10>${\alpha}{\geq}1$ is reduced by 1; and a swap method for $_{max}[F(P_i)-F(P_i-{\alpha})]$>$_{min}[F(P_j+{\alpha})-F(P_j)]$, $i{\neq}j$ of $P_i=P_i{\pm}{\alpha}$, in which power is swapped to $P_i=P_i-{\alpha}$, $P_j=P_j+{\alpha}$. It finally executes minute swap process for ${\alpha}=\text{0.1, 0.01, 0.001, 0.0001}$. When applied to various experimental cases of the dynamic economic load dispatch problems, the proposed algorithm has proved to maximize economic benefits by significantly reducing the optimal operating cost of the extant Heuristic algorithm.

• Industrial Engineering and Management Systems
• /
• v.7 no.2
• /
• pp.171-181
• /
• 2008

Home care, known also as domiciliary care, is part of the community care service that is a responsibility of the local government authorities in the UK as well as many other countries around the world. The aim is to provide the care and support needed to assist people, particularly older people, people with physical or learning disabilities and people who need assistance due to illness to live as independently as possible in their own homes. It is performed primarily by care workers visiting clients' homes where they provide help with daily activities. This paper is concerned with the dispatching of care workers to clients in an efficient manner. The optimized routine for each care worker determines a schedule to achieve the minimum total cost (in terms of distance traveled) without violating the capacity and time window constraints. A collaborative population-based meta-heuristic called Particle Swarm Optimization (PSO) is applied to solve the problem. A particle is defined as a multi-dimensional point in space which represents the corresponding schedule for care workers and their clients. Each dimension of a particle represents a care activity and the corresponding, allocated care worker. The continuous position value of each dimension determines the care worker to be assigned and also the assignment priority. A heuristic assignment scheme is specially designed to transform the continuous position value to the discrete job schedule. This job schedule represents the potential feasible solution to the problem. The Earliest Start Time Priority with Minimum Distance Assignment (ESTPMDA) technique is developed for generating an initial solution which guides the search direction of the particle. Local improvement procedures (LIP), insertion and swap, are embedded in the PSO algorithm in order to further improve the quality of the solution. The proposed methodology is implemented, tested, and compared with existing solutions for some 'real' problem instances.

• The Journal of the Korea Contents Association
• /
• v.7 no.8
• /
• pp.59-65
• /
• 2007

Overlay Multicast is an effective method for efficient utilization of system resources and network bandwidth without a need for hardware customization. Multicast tree reconstruction is required when a non-leaf node leaves or fails. However frequent multicast tree reconstruction introduces serious degradation in performance. In this paper, we propose a tree performance optimization algorithm to solve this defect by using information(RTCP-probing) that becomes a periodic feedback to a source node from each child node. The proposed model is a mechanism performed when a parent node seems to cause deterioration in the tree performance. We have improved the performance of the whole service tree using the mechanism and hence composing an optimization tree. The simulation results show that our proposal stands to be an effective method that can be applied to not only the proposed model but also to existing techniques.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2003.09a
• /
• pp.187-190
• /
• 2003

Many manufacturing companies consider the integrated and concurrent scheduling because they need the global optimization technology that could manufacture various products more responsive to customer needs. In this paper, we propose an advanced scheduling model to generate the schedules considering resource constraints and precedence constraints in make-to-order (MTO) manufacturing environments. Precedence of work- in-process(WIP) and resources constraints have recently emerged as one of the main constraints in advanced scheduling problems. The advanced scheduling problems is formulated as a multiobjective mathematical model for generating operation schedules which are obeyed resources constraints, alternative workstations of operations and the precedence constraints of WIP in MTO manufacturing. For effectively solving the advanced scheduling problem, the multi-objective hybrid genetic algorithm (m-hGA) is proposed in this paper. The m-hGA is to minimize the makespan, total flow time of order, and maximum tardiness for each order, simultaneously. The m-hGA approach with local search-based mutation through swap mutation is developed to solve the advanced scheduling problem. Numerical example is tested and presented for advanced scheduling problems with various orders to describe the performance of the proposed m-hGA.

• Journal of the Korea Society of Computer and Information
• /
• v.19 no.10
• /
• pp.169-173
• /
• 2014

Nobody has yet been able to determine the optimal solution conclusively whether NP-complete problems are in fact solvable in polynomial time. Gu$\acute{e}$ret et al. tries to obtain the optimal solution using linear programming with $O(m^4)$ time complexity for barge loading problem a kind of bin packing problem that is classified as nondeterministic polynomial time (NP)-complete problem. On the other hand, this paper suggests the loading rule of profit priority rank algorithm with O(m log m) time complexity. This paper decides the profit priority rank firstly. Then, we obtain the initial loading result using the rule of loading the good has profit priority order. Finally, we balance the loading and capability of barge swap the goods of unloading in previously loading in case of under loading. As a result of experiments, this algorithm reduces the $O(m^4)$ of linear programming to O(m log m) time complexity for NP-complete barge loading problem.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.21 no.6
• /
• pp.71-76
• /
• 2021

Recently, as the size of IoT data grows, the memory power consumption of real-time systems increases rapidly. This is because real-time systems always place entire tasks in memory, which increases the demand of DRAM significantly. In this paper, we adopt emerging fast storage media and move a certain portion of real-time tasks from DRAM to storage. The part of tasks in storage are, then, loaded into memory when they are actually used. We incorporate our memory/storage power-saving into the dynamic voltage/frequency scaling of processors, thereby optimizing power consumptions in CPU and memory simultaneously. Specifically, the proposed technique aims at minimizing the CPU idle time and the DRAM memory size by determining appropriate voltage modes of CPU and the swap ratio of memory, without violating the deadlines of all tasks. Through simulation experiments, we show that the proposed technique significantly reduces the power consumption of real-time systems.