• 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.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.546-549
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• 1995

Recently, electric utility is facing substantially new stream of business environment, such as pressure of business restructuring, competition with private IPPs, diversification of supply-side and demand-side resource options, environmental externalities and uncertainties. Integrated resource planning(IRP) is very useful and powerful approach for solving complex and diversified electricity supply and demand problems. This paper presents a standardized IRP procedure using multi-attribute decision analysis approach. The selection of the most desirable plan is based on multi-attribute trade-off/risk analysis method and score ranking method. As a case study, 50 plans with 12 scenarios are analyzed.

• Journal of electromagnetic engineering and science
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• v.15 no.1
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• pp.14-19
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• 2015

In multicellular wireless networks, intercell interference limits system performance, especially cell edge user performance. One promising approach to solve this problem is the intercell interference coordination (ICIC) scheme. In this paper, we propose a new ICIC scheme based on a resource partitioning approach to enhance cell edge user performance in a wireless multicellular system. The most important feature of the proposed scheme is that the algorithm is performed at each base station in a distributed manner and therefore minimizes the required information exchange between neighboring base stations. The proposed scheme has benefits in a practical environment where the traffic load distribution is not uniform among base stations and the backhaul capacity between the base stations is limited.

• Journal of Communications and Networks
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• v.15 no.5
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• pp.469-475
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• 2013

In this paper, we propose an efficient interference management technique which controls the number of resource blocks (or subcarriers) shared with other cells based on statistical interference levels among cells. The proposed technique tries to maximize average throughput of a femto cell user under a constraint on non-real time control of a femto cell network while guaranteeing a target throughput value of a macro cell user. In our proposed scheme, femto cells opportunistically use resource blocks allocated to other cells if the required average user throughput is not attained with the primarily allocated resource blocks. The proposed method is similar to the underlay approach in cognitive radio systems, but resource block sharing among cells is statistically controlled. For the statistical control, a femto cell sever constructs a table storing average mutual interference among cells and periodically updates the table. This statistical approach fully satisfies the constraint of non-real time control for femto cell networks. Our simulation results show that the proposed scheme achieves higher average femto user throughput than conventional frequency reuse schemes for time varying number of users.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.10
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• pp.6004-6013
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• 2014

The research on resource sharing in the diversification field is concerned mainly with sharing, but there has been little interest in resources. This research examined resource sharing with the main variables and logics of resource-based view. Based on a survey of 263 affiliates of 35 diversified firms, the impacts of resource quantity, resource quality, and resource exploitability on inter-affiliate resource sharing were hypothesized and verified. To confirm the performance implications of resource sharing, the impacts of the combination of resource quantity and resource sharing, the combination of resource quality and resource sharing, and the combination of resource exploitability and resource sharing on the affiliate performance were also hypothesized and verified. According to the empirical results from multiple regression analyses, resource sharing increases in the order of low resource quantity, high resource quality, and high resource exploitability. The performance was advanced in resource sharing under a low resource quantity and resource sharing under high resource exploitability, but it was not advanced in resource sharing under high resource quality. The results highlight the need for a further study on why the resource quality and resource exploitability affect resource sharing in the opposite directions, as expected in the hypotheses, why resource sharing under high resource quality does not lead to high performance, even though resource quality increases resource sharing, and what they would be if resources are subdivided by the types.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.11B
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• pp.1178-1190
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• 2009

IEEE 802.11e proposed by IEEE 802.11 working group to guarantee QoS has contention based EDCA and contention free based HCCA. HCCA, a centralized polling based mechanism of 802.11e, needs a scheduling algorithm to allocate the network resource efficiently. The existing standard scheduler, however, is inefficient to support for QoS guarantee for real-time service having VBR traffic. To efficiently assign resource for VBR traffic, in this paper, we propose TXOP algorithm based on dual leaky bucket using average resource allocation and peak resource allocation. The minimum TXOP of each station is obtained by using statistical approach to maximize number of stations of which performance satisfy QoS target. Simulation results show that the proposed algorithm has much higher performance compared with reference scheduler in terms of throughput and delay.

• The Transactions of the Korea Information Processing Society
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• v.6 no.11S
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• pp.3350-3355
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• 1999

Modeling for the Internet resource is important to use of limited resource efficiently. Also, the modeling could guide how we can approach the Internet problems. The many studies of price mechanism to internalize the externality, to decrease the externality from congestion, and to use the Internet resource efficiently, are widely going on. The represent styles for Internet pricing are usage-based pricing(UBP) and flat-rat pricing(FRP) and the FRP could prefers for the beginning stage of Internet introduce. The purpose of this study is to examine the efficiency of Internet pricing mechanism based on flat-rate pricing, both consumer and producer side.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.8
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• pp.1825-1842
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• 2013

Rapid growth of the IT industry has led to significant energy consumption in the last decade. Data centers swallow an enormous amount of electrical energy and have high operating costs and carbon dioxide excretions. In response to this, the dynamic consolidation of virtual machines (VMs) allows for efficient resource management and reduces power consumption through the live migration of VMs in the hosts. Moreover, each client typically has a service level agreement (SLA), this leads to stipulations in dealing with energy-performance trade-offs, as aggressive consolidation may lead to performance degradation beyond the negotiation. In this paper we propose a heuristic based resource allocation of VM selection and a VM allocation approach that aims to minimize the total energy consumption and operating costs while meeting the client-level SLA. Our experiment results demonstrate significant enhancements in cloud providers' profit and energy savings while improving the SLA at a certain level.

• ETRI Journal
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• v.43 no.1
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• pp.53-63
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• 2021

Cloud computing has drastically reduced the price of computing resources through the use of virtualized resources that are shared among users. However, the established large cloud data centers have a large carbon footprint owing to their excessive power consumption. Inefficiency in resource utilization and power consumption results in the low fiscal gain of service providers. Therefore, data centers should adopt an effective resource-management approach. In this paper, we present a novel load-balancing framework with the objective of minimizing the operational cost of data centers through improved resource utilization. The framework utilizes a modified genetic algorithm for realizing the optimal allocation of virtual machines (VMs) over physical machines. The experimental results demonstrate that the proposed framework improves the resource utilization by up to 45.21%, 84.49%, 119.93%, and 113.96% over a recent and three other standard heuristics-based VM placement approaches.

• 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.