• Journal of Korea Multimedia Society
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• v.12 no.3
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• pp.409-418
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• 2009

Providing Quality-of-Service (QoS) guarantee requires for each router on the path of a traffic flow not to violate the flow's delay budget allocated to itself. Since the amount of load being offered to the router is determined by the budget, some imbalance in load among routers on the path may be alleviated by means of adjusting the budget. The equal allocation applied to the resource reservation protocol (RSVP) is simple to implement, but it has the drawback of a poor resource utilization. A load balancing method in which the delay budget being allocated to a router depends on its load state was developed to improve the drawback, but it's too complex to apply to the RSVP. This paper proposes an intra-path load balancing method not only applicable to the RSVP but also more effective in improving the drawback. The proposed method first partitions the end-to-end delay bound of a flow to routers by the RSVP and then let them adjust their budgets according to both the bottleneck state of the path and their links' bandwidth availabilities. The results of the simulation applying the proposed method to an evaluation network showed that the proposed method may provide the gain of 4 ${\sim}$ 17 % compared to that in the legacy one in terms of the number of maximally admittable flows.

• 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.

• Journal of the Korean Operations Research and Management Science Society
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• v.24 no.4
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• pp.81-94
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• 1999

Organization change projects such as Business Process Redesign(BPR) have been implemented by many firms for enhancing their organizational performance. However, management is reluctant to initiate these projects due to enterprise-wide impact and steeper project cost than the traditional system development projects. Thus, there emerges a need to accurately assess the value of the redesigned organizational process for the successful implementation of BPR projects. The purpose of this study is to assess the value of process redesign in the point of resource utilization and allocation, and cycle time and cost reduction. The candidate process and the design alternatives are identified from organizational requirements analysis. The variables and their relations are defined to perform task activity analysis, bottleneck analysis, cycle cost analysis, and resource utilization analysis. A Case study of a manufacturing company indicates that the assessments method proposed in this study is a promising approach to identify the business alternative process that lead to the highest organizational performance.

• JSTS:Journal of Semiconductor Technology and Science
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• v.14 no.3
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• pp.284-299
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• 2014

Coarse-grained reconfigurable architecture (CGRA)-based multi-core architecture aims at achieving high performance by kernel level parallelism (KLP). However, the existing CGRA-based multi-core architectures suffer from much energy and performance bottleneck when trying to exploit the KLP because of poor resource utilization caused by insufficient flexibility. In this work, we propose a new ring-based sharing fabric (RSF) to boost their flexibility level for the efficient resource utilization focusing on the kernel-stream type of the KLP. In addition, based on the RSF, we introduce a novel inter-CGRA reconfiguration technique for the efficient pipelining of kernel-stream on CGRA-based multi-core architectures. Experimental results show that the proposed approaches improve performance by up to 50.62 times and reduce energy by up to 50.16% when compared with the conventional CGRA-based multi-core architectures.

• Korea Journal of Hospital Management
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• v.16 no.4
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• pp.161-182
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• 2011

In today's competitive dental markets, it is of paramount importance to improve service quality and at the same time to use scarce resource efficiently. In this study, we present appointment policies and scheduling rules for private dental clinics to reduce the waiting time of patients and to increase the revenue by utilizing resource more effectively. This study validates the proposed appointment policies and scheduling rules based on simulation models. We show that the bottleneck-based appointment policy is the most effective among appointment policies, followed by the multiple-block appointment one. The shortest processing time among scheduling rules contributes most to the performance of the appointment system.

• Journal of Microbiology and Biotechnology
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• v.25 no.6
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• pp.880-886
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• 2015

Bacterial heme was produced from a genetic-engineered Escherichia coli via the porphyrin pathway and it was useful as an iron resource for animal feed. The amount of the E. coli-synthesized heme, however, was only few milligrams in a culture broth and it was not enough for industrial applications. To analyze heme biosynthetic pathways, an engineered E. coli artificially overexpressing ALA synthase (hemA from Rhodobacter sphaeroides) and pantothenate kinase (coaA gene from self geneome) was constructed as a bacterial heme-producing strain, and both the transcription levels of pathway genes and the intermediates concentrations were determined from batch and continuous cultures. Transcription levels of the pathway genes were not significantly changed among the tested conditions. Intracellular intermediate concentrations indicated that aminolevulinic acid (ALA) and coenzyme A (CoA) were enhanced by the hemA-coaA co-expression. Intracellular coproporphyrinogen I and protoporphyrin IX accumulation suggested that the bottleneck steps in the heme biosynthetic pathway could be the spontaneous conversion of HMB to coproporphyrinogen I and the limited conversion of protoporphyrin IX to heme, respectively. A strategy to increase the conversion of ALA to heme is discussed based on the results.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.49-54
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• 2022

With the recent widespread adoption of general-purpose GPUs (GPGPUs) in cloud systems, maximizing the resource utilization through multitasking in GPGPU has become an important issue. In this article, we show that resource allocation based on the workload classification of computing-bound and memory-bound is not sufficient with respect to resource utilization, and present a new thread block scheduling policy for GPGPU that makes use of fine-grained resource utilizations of each workload. Unlike previous approaches, the proposed policy reduces scheduling overhead by separating profiling and scheduling, and maximizes resource utilizations by co-locating workloads with different bottleneck resources. Through simulations under various virtual machine scenarios, we show that the proposed policy improves the GPGPU throughput by 130.6% on average and up to 161.4%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.874-890
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• 2021

With the widespread deployment of the fifth-generation (5G) communication networks, various real-time applications are rapidly increasing and generating massive traffic on backhaul network environments. In this scenario, network congestion will occur when the communication and computation resources exceed the maximum available capacity, which severely degrades the network performance. To alleviate this problem, this paper proposed an intelligent resource allocation (IRA) to integrate with the extant resource adjustment (ERA) approach mainly based on the convergence of support vector machine (SVM) algorithm, software-defined networking (SDN), and mobile edge computing (MEC) paradigms. The proposed scheme acquires predictable schedules to adapt the downlink (DL) transmission towards off-peak hour intervals as a predominant priority. Accordingly, the peak hour bandwidth resources for serving real-time uplink (UL) transmission enlarge its capacity for a variety of mission-critical applications. Furthermore, to advance and boost gateway computation resources, MEC servers are implemented and integrated with the proposed scheme in this study. In the conclusive simulation results, the performance evaluation analyzes and compares the proposed scheme with the conventional approach over a variety of QoS metrics including network delay, jitter, packet drop ratio, packet delivery ratio, and throughput.

• Journal of Internet Computing and Services
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• v.23 no.2
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• pp.1-7
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• 2022

Machine learning (ML) algorithms have been intended to seamlessly collaborate for enabling intelligent networking in terms of massive service differentiation, prediction, and provides high-accuracy recommendation systems. Mobile edge computing (MEC) servers are located close to the edge networks to overcome the responsibility for massive requests from user devices and perform local service offloading. Moreover, there are required lightweight methods for handling real-time Internet of Things (IoT) communication perspectives, especially for ultra-reliable low-latency communication (URLLC) and optimal resource utilization. To overcome the abovementioned issues, this paper proposed an intelligent scheme for traffic steering based on the integration of MEC and lightweight ML, namely support vector machine (SVM) for effectively routing for lightweight and resource constraint networks. The scheme provides dynamic resource handling for the real-time IoT user systems based on the awareness of obvious network statues. The system evaluations were conducted by utillizing computer software simulations, and the proposed approach is remarkably outperformed the conventional schemes in terms of significant QoS metrics, including communication latency, reliability, and communication throughput.

• Korean Journal of Computational Design and Engineering
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• v.10 no.3
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• pp.210-216
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• 2005

Digital manufacturing could be very effective in shipbuilding in order to estimate the process time, to improve the operation efficiency, and to prevent bottleneck processes in advance. The subassembly process having done research consists of piece arrangement, tack welding, robot welding, manual welding and so on. The robot welding of them was the focus of the simulation. The analysis and modeling were carried out by using UML (Unified Modeling Language) as well as $IDEF\phi$ (Integration DEFinition). The characteristics of the process resources were analyzed using the shipyard data, and the layout of the subassembly line was designed with the resources. Using the constructed resource and process model, the productivity and efficiency of changed robot welding stage were investigated. It was simulated how much the variations in the resource performance have influence on improvement of productivity. One of the important outputs in this simulation was the cycle time during a certain period's work. The cycle time prediction was also undertaken for the different torch and the different piece arrangement. The proposed model was established three-dimensionally in a digital environment so that interferences among objects and space allocations for the resources could be easily investigated.