• Journal of the Korea Society for Simulation
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• v.21 no.2
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• pp.59-67
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• 2012

A grid computing consists of the physical resources for processing one of the large-scale jobs. However, due to the recent trends of rapid growing data, the grid computing needs a parallel processing method to process the job. In general, each physical resource divides a requested large-scale task. And a processing time of the task varies with an efficiency and a distance of each resource. Even if some resource completes a job, the resource is standing by until every divided job is finished. When every resource finishes a processing, each resource starts a next job. Therefore, this paper proposes a dynamic resource reallocation scheduling model (DDRSM). DDRSM finds a waiting resource and reallocates an unfinished job with an efficiency and a distance of the resource. DDRSM is an efficient method for processing multiple large-scale jobs.

• Korean Management Science Review
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• v.31 no.2
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• pp.65-70
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• 2014

This paper considers a single machine scheduling problem to determine release and processing times where both the release times and processing times are linearly decreasing functions of resources. The objective is to minimize the sum of the associated resource consumption cost and scheduling cost including makespan, sum of completion times, maximum lateness, or sum of lateness. This paper proves that the scheduling problem is NP-hard in the strong sense even if the release times are constant.

• Journal of Information Processing Systems
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• v.19 no.4
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• pp.439-449
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• 2023

This paper deals with a resource efficient artificial intelligence (AI) service architecture for multi-channel video streams. As an AI service, we consider the object detection model, which is the most representative for video applications. Since most object detection models are basically designed for a single channel video stream, the utilization of the additional resource for multi-channel video stream processing is inevitable. Therefore, we propose a resource efficient AI service framework, which can be associated with various AI service models. Our framework is designed based on the modular architecture, which consists of adaptive frame control (AFC) Manager, multiplexer (MUX), adaptive channel selector (ACS), and YOLO interface units. In order to run only a single YOLO process without regard to the number of channels, we propose a novel approach efficiently dealing with multi-channel input streams. Through the experiment, it is shown that the framework is capable of performing object detection service with minimum resource utilization even in the circumstance of multi-channel streams. In addition, each service can be guaranteed within a deadline.

• Journal of Information Processing Systems
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• v.8 no.4
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• pp.555-566
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• 2012

The cloud environment makes it possible to analyze large data sets in a scalable computing infrastructure. In the bioinformatics field, the applications are composed of the complex workflow tasks, which require huge data storage as well as a computing-intensive parallel workload. Many approaches have been introduced in distributed solutions. However, they focus on static resource provisioning with a batch-processing scheme in a local computing farm and data storage. In the case of a large-scale workflow system, it is inevitable and valuable to outsource the entire or a part of their tasks to public clouds for reducing resource costs. The problems, however, occurred at the transfer time for huge dataset as well as there being an unbalanced completion time of different problem sizes. In this paper, we propose an adaptive resource-provisioning scheme that includes run-time data distribution and collection services for hiding the data transfer time. The proposed adaptive resource-provisioning scheme optimizes the allocation ratio of computing elements to the different datasets in order to minimize the total makespan under resource constraints. We conducted the experiments with a well-known sequence alignment algorithm and the results showed that the proposed scheme is efficient for the cloud environment.

• Proceedings of the IEEK Conference
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• 2001.10a
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• pp.719-724
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• 2001

The objective of this investigation was to separate silicon and silica for recycling by the liquid-liquid separation technique. In the preparation of silicon (Si) single crystal, a small amount of silicon is fixed on the surface of silica (quartz, $SiO_2$) crucible. The used crucible is crushed for recycling both silicon and silica in a high purity from the mixed powder. Zeta-potential of silicon and silica are almost the same at pH higher than 3. Their separation by simple flotation is ruled out. However, their hydrophobic characteristics are different in several different organic solvent from the measurement of contact angle. Therefore, the liquid-liquid extraction is employed to separate silicon and silica. The result indicates that the organic solvent mixed with dodecyl ammonium acetate could extracted the silicon powder at high purity (97-100%) with high recovery from the silica powder in the water phase.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.1
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• pp.1-8
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• 2006

This paper proposes a real-time resource allocation algorithm for minimizing the completion time of overall workflow process. The jobs in a workflow process are interrelated through the precedence graph including Sequence, AND, OR and Loop control structure. A resource should be allocated for the processing of each job, and the required processing time of the job can be varied by the resource allocation decision. Each resource has several inherent restrictions such as the functional, geographical, positional and other operational characteristics. The algorithm suggested in this paper selects an effective resource for each job by considering the precedence constraint and the resource characteristics such as processing time and the inherent restrictions. To investigate the performance of the proposed algorithm, several numerical tests are performed for four different workflow graphs including standard, parallel and two series-parallel structures. In the tests, the solutions by the proposed algorithm are compared with random and optimal solutions which are obtained by a random selection rule and a full enumeration method respectively.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.310-328
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• 2022

Internet of things (IoT) has emerged as the most popular technique that facilitates enhancing humans' quality of life. However, most time sensitive IoT applications require quick response time. So, processing these IoT applications in cloud servers may not be effective. Therefore, fog computing has emerged as a promising solution that addresses the problem of managing large data bandwidth requirements of devices and quick response time. This technology has resulted in processing a large amount of data near the data source compared to the cloud. However, efficient management of computing resources involving balancing workload, allocating resources, provisioning resources, and scheduling tasks is one primary consideration for effective computing-based solutions, specifically for time-sensitive applications. This paper provides a comprehensive review of the source management strategies considering resource limitations, heterogeneity, unpredicted traffic in the fog computing environment. It presents recent developments in the resource management field of the fog computing environment. It also presents significant management issues such as resource allocation, resource provisioning, resource scheduling, task offloading, etc. Related studies are compared indifferent mentions to provide promising directions of future research by fellow researchers in the field.

• Geomechanics and Engineering
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• v.18 no.2
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• pp.205-213
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• 2019

A calculation model of reservoir pressure field distribution around multiple production wells in a heavy oil reservoir is established, which can overcome the unreasonable uniform-pressure value calculated by the traditional mathematical model in the multiwell mining areas. A calculating program is developed based on the deduced equations by using Visual Basic computer language. Based on the proposed mathematical model, the effects of drainage rate and formation permeability on the distribution of reservoir pressure are studied. Results show that the reservoir pressure drops most at the wellbore. The farther the distance away from the borehole, the sparser the isobaric lines distribute. Increasing drainage rate results in decreasing reservoir pressure and bottom-hole pressure, especially the latter. The permeability has a significant effect on bottom hole pressure. The study provides a reference basis for studying the dynamic pressure field distribution before thermal recovery technology in heavy oilfield and optimizing construction parameters.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1192-1200
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• 2019

Recently, artificial intelligence techniques have been widely used in the computer science field, such as the Internet of Things, big data, cloud computing, and mobile computing. In particular, resource management is of utmost importance for maintaining the quality of services, service-level agreements, and the availability of the system. In this paper, we review and analyze various ways to meet the requirements of cloud resource management based on artificial intelligence. We divide cloud resource management techniques based on artificial intelligence into three categories: fog computing systems, edge-cloud systems, and intelligent cloud computing systems. The aim of the paper is to propose an intelligent resource management scheme that manages mobile resources by monitoring devices' statuses and predicting their future stability based on one of the artificial intelligence techniques. We explore how our proposed resource management scheme can be extended to various cloud-based systems.

• Journal of Information Processing Systems
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• v.14 no.2
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• pp.377-395
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• 2018

Aiming at the problem of service reliability in resource reservation in cloud computing environments, a model of dynamic cloud resource reservation based on trust is proposed. A domain-specific cloud management architecture is designed in which resources are divided into different management domains according to the types of service for easier management. A dynamic resource reservation mechanism (DRRM) is used to test users' reservation requests and reserve resources for users. According to user preference, several resources are chosen to be candidate resources by fuzzy cluster analysis. The fuzzy evaluation method and a two-way trust evaluation mechanism are adopted to improve the availability and credibility of the model. An analysis and simulation experiments show that this model can increase the flexibility of resource reservation and improve user satisfaction.