• Journal of the Korea Society for Simulation
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• v.23 no.4
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• pp.111-119
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• 2014

The cloud environment need resource management method that to enable the big data issue and data analysis technology. Existing resource management uses the limited calculation method, therefore concentrated the resource bias problem. To solve this problem, the resource management requires the learning-based scheduling using resource history information. In this paper, we proposes the ART (Adaptive Resonance Theory)-based adaptive resource management. Our proposed method assigns the job to the suitable method with the resource monitoring and history management in cloud computing environment. The proposed method utilizes the unsupervised learning method. Our goal is to improve the data processing and service stability with the adaptive resource management. The propose method allow the systematic management, and utilize the available resource efficiently.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.12
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• pp.2294-2314
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• 2011

The key issue in providing fast and reliable access on cloud services is the effective management of resources in a cloud system. However, the high variation in cloud service access rates affects the system performance considerably when there are no default routines to handle this type of occurrence. Adaptive techniques are used in resource management to support robust systems and maintain well-balanced loads within the servers. This paper presents an adaptive resource management for cloud systems which supports the integration of intelligent methods to promote quality of service (QoS) in provisioning of cloud services. A technique of dynamically assigning cloud services to a group of cloud servers is proposed for the adaptive resource management. Initially, cloud services are collected based on the excess cloud services load and then these are deployed to the assigned cloud servers. The assignment function uses the proposed proportional ordering which efficiently assigns cloud services based on its resource consumption. The difference in resource consumption rate in all nodes is analyzed periodically which decides the execution of service assignment. Performance evaluation showed that the proposed dynamic service assignment (DSA) performed best in throughput performance compared to other resource allocation algorithms.

• International Journal of Computer Science & Network Security
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• v.21 no.12
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• pp.110-116
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• 2021

The article focuses on the need to deepen the issue of human resource management in logistics processes. It is noted that changes in market conditions and turbulence in the institutional environment require managers to form a highly effective human resources policy capable to ensure the innovative development of the enterprise. Functional strategies for human resource management in logistical processes are proposed, namely: adaptive, innovative, selective and exclusive. Innovative technologies that should be used in the adaptive human resources policy process are identified.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.10
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• pp.2382-2388
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• 2014

To provide network reliability in indoor wireless communication systems, we should resolve the problem of unexpected network failure rapidly. In this paper, we propose an adaptive resource management (ARM) scheme to support seamless connectivity to users. In consideration of system throughput and user fairness simultaneously, the ARM scheme adaptively determines the set of healing channels, and performs scheduling and power allocation iteratively based on a constrained non-convex optimization technique. Through intensive simulations, we demonstrate the superior performance results of the proposed ARM scheme in terms of the average cell capacity and user fairness.

• Journal of Distribution Science
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• v.18 no.11
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• pp.91-103
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• 2020

The recent Covid-19 outbreak has caused severe disruption of the global supply chain, which tests firms' ability to survive and build resilience. The concept of adaptive supply chain management (A-SCM) has never been tested against a severe supply chain disruption, such as a pandemic. Purpose: The aim of this study is to examine how firms in Indonesia develop resilience through the implementation of components of adaptive supply chain management, namely risk management, resource reconfiguration and supply chain flexibility, in order to survive severe supply chain disruption. Research design, data and methodology: A qualitative method and PLS-SEM were used to analyze 120 data collected from Indonesian manufacturing firms in various industries. Results: The findings show that risk management, resource reconfiguration, and supply chain flexibility are important components that make up A-SCM. However, only risk management contributes to help build firm resilience in the presence of severe supply chain disruption. Conclusions: The components of A-SCM have been empirically tested. The implication is that managers should carefully use RM to prepare firms for different scenarios to develop contingency strategies. This research contributes to the supply chain management body of knowledge in the context of pandemic-level disruption and broadens the dynamic capabilities perspective.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.7
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• pp.536-542
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• 2016

To enhance tracking efficiency, a target-tracking filter with a resource management algorithm is required. One of the resource management algorithms chooses or evaluates the proper sampling time using cost functions which are related to the target tracking filter. We propose a resource management algorithm for bearing only tracking environments. Since the tracking performance depends on the system observability, the bearing-only tracking is one of challenging target-tracking fields. The proposed algorithm provides the adaptive sampling time using the variation rate of the error covariance matrix from the target-tracking filter. The simulation verifies the efficiency performance of the proposed algorithm.

• Journal of Wetlands Research
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• v.21 no.4
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• pp.267-280
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• 2019

Adaptive Management (AM) is one of the best available approaches for managing natural resources in the presence of uncertainty. In spite of the limitations, AM has been widely applied in nature resource management policies and plans internationally, while application of AM in nature resource management in Korea is limitedly used. Accordingly, this study reviews application of AM in nature resource management research in Korea with respect to its definitions, procedures, impediments and considerations. The present study also reviews recent ecological modelling studies which is an essential component of AM approach. Finally, management of artificial sea forest, coastal wetlands and fisheries are suggested as the recommended fields to adopt AM.

• Journal of Environmental Policy
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• v.5 no.4
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• pp.19-49
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• 2006

As global climate change is expected to influence regional water resources, water resource managers need to establish adaptive management to cope with climate change. We examined adaptive management efforts in the US, UK, Canada, Australia, and the Netherlands. Each country is implementing different levels of adaptation efforts based on current water management practices, institutional arrangements, as well as the varying degree of water availability, current climate effects and expected climate change effects. Based on the comparison of these countries, we suggest policy implications for the sustainable water resource management of Korea under climate changes.

• Journal of the Korea Society of Computer and Information
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• v.14 no.8
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• pp.81-88
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• 2009

Many studies and policies are suggested for customer satisfaction to survive in multimedia content service markets. there are policies like a segregating the clients using the contents service and allocating the media server's resources distinctively by clusters using the cluster analysis method of CRM. The problem of this policy is fixed allocation of media server resources. It is inefficient for costly media server resource. To resolve the problem and enhance the utilization of media server resource, the ACRFA (Adaptive Client Request Filtering Algorithm) was suggested per cluster to allocate media server resources by flexible resource allocation method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3584-3602
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• 2022

NFV realizes flexible and rapid software deployment and management of network functions in the cloud network, and provides network services in the form of chained virtual network functions (VNFs). However, using VNFs to provide quality guaranteed services is still a challenge because of the inherent difficulty in intelligently scaling VNFs to handle traffic fluctuations. Most existing works scale VNFs with fixed-capacity instances, that is they take instances of the same size and determine a suitable deployment location without considering the cloud network resource distribution. This paper proposes a traffic forecasted assisted proactive VNF scaling approach, and it adopts the instance capacity adaptive to the node resource. We first model the VNF scaling as integer quadratic programming and then propose a proactive adaptive VNF scaling (PAVS) approach. The approach employs an efficient traffic forecasting method based on LSTM to predict the upcoming traffic demands. With the obtained traffic demands, we design a resource-aware new VNF instance deployment algorithm to scale out under-provisioning VNFs and a redundant VNF instance management mechanism to scale in over-provisioning VNFs. Trace-driven simulation demonstrates that our proposed approach can respond to traffic fluctuation in advance and reduce the total cost significantly.