• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.8
• /
• pp.2783-2804
• /
• 2021

In the network function virtualization environment, dynamic changes in network traffic will lead to the dynamic changes of service function chain resource demand, which entails timely dynamic adjustment of service function chain resource configuration. At present, most researches solve this problem through virtual network function migration and link rerouting, and there exist some problems such as long service interruption time, excessive network operation cost and high penalty. This paper proposes a dynamic adjustment method of service function chain resource configuration for the dynamic changes of network traffic. First, a dynamic adjustment request of service function chain is generated according to the prediction of network traffic. Second, a dynamic adjustment strategy of service function chain resource configuration is determined according to substrate network resources. Finally, the resource configuration of a service function chain is pre-adjusted according to the dynamic adjustment strategy. Virtual network functions combination and virtual machine reusing are fully considered in this process. The experimental results show that this method can reduce the influence of service function chain resource configuration dynamic adjustment on quality of service, reduce network operation cost and improve the revenue of service providers.

• IEIE Transactions on Smart Processing and Computing
• /
• v.6 no.1
• /
• pp.60-65
• /
• 2017

Network virtualization enables us to make efficient use of resources in a physical network by embedding multiple virtual networks in the physical network. In this paper, we develop a prototype of a virtual network embedding system. Our system consists of OpenStack, which is an open source cloud service platform, and shell scripts. Because OpenStack does not provide a quality of service control function, we realize bandwidth reservation for virtual links by making use of the ingress policing function of Open vSwitch, which is a virtual switch used in OpenStack. The shell scripts in our system automatically construct the required virtual network on the physical network using the OpenStack command-line interface, and they reserve bandwidth for virtual links using the Open vSwitch command. Experimental evaluation confirms that our system constructs the requested virtual network and appropriately allocates node and link resources to it.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.7
• /
• pp.3393-3412
• /
• 2017

As a main challenge in network virtualization, virtual network embedding problem is increasingly important and heuristic algorithms are of great interest. Aiming at the problems of poor correlation in node embedding and link embedding, long distance between adjacent virtual nodes and imbalance resource consumption of network components during embedding, we herein propose a two-stage virtual network embedding algorithm NA-PVNM. In node embedding stage, resource requirement and breadth first search algorithm are introduced to sort virtual nodes, and a node fitness function is developed to find the best substrate node. In link embedding stage, a path fitness function is developed to find the best path in which available bandwidth, CPU and path length are considered. Simulation results showed that the proposed algorithm could shorten link embedding distance, increase the acceptance ratio and revenue to cost ratio compared to previously reported algorithms. We also analyzed the impact of position constraint and substrate network attribute on algorithm performance, as well as the utilization of the substrate network resources during embedding via simulation. The results showed that, under the constraint of substrate resource distribution and virtual network requests, the critical factor of improving success ratio is to reduce resource consumption during embedding.

• KNOM Review
• /
• v.21 no.2
• /
• pp.1-9
• /
• 2018

Network virtualization refers to a technology creating independent virtual network environment on a physical network. Network virtualization technology can share the physical network resources to reduce the cost of establishing the network for each user and enables the network administrator to dynamically change the network configuration according to the purpose. Although the network management can be handled dynamically, the management is manual, and it does not maximize the profit of network virtualization. In this paper, we propose Machine-Learning technology to allow the network to learn by itself and manage its management dynamically. The proposed approach is to dynamically allocate appropriate resources by predicting resource demand of VNF in service function chaining, which is a core and essential problem in virtual network management. Our goal is to predict the resource demand of the VNF and dynamically allocate the appropriate resources to reduce the cost of network operation while preventing service interruption.

• KNOM Review
• /
• v.23 no.2
• /
• pp.1-10
• /
• 2020

Network Function Virtualization (NFV) is a key technology of 5G networks that has the advantage of enabling building and operating networks flexibly. However, NFV can complicate network management because it creates numerous virtual resources that should be managed. In NFV environments, service function chaining (SFC) composed of virtual network functions (VNFs) is widely used to apply a series of network functions to traffic. Therefore, it is required to dynamically allocate the right amount of computing resources or instances to SFC for meeting service requirements. In this paper, we propose Deep Q-Networks (DQN)-based auto-scaling to operate the appropriate number of VNF instances in SFC. The proposed approach not only resizes the number of VNF instances in SFC composed of multi-tier architecture but also selects a tier to be scaled in response to dynamic traffic forwarding through SFC.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.10 no.10
• /
• pp.1101-1108
• /
• 2015

In this paper, to provide a method and system for providing a network separation virtual machine environment. How to provide this virtual machine environment include phase generating necessary virtual resource requirement for the perform of virtual function and transfer to network changing protocol about request of registration virtual resource. For this reason, Registration procedure is to use a virtual machine for a virtual computing resource allocation and separation combined network any time, it became possible between servers and clients, or mobile phone. At any time, it is possible to process the work in the same environment as in a computer to access the Internet.

• KNOM Review
• /
• v.21 no.2
• /
• pp.35-45
• /
• 2018

As Network Slicing functionality gets applied to mobile networking, a mechanism that enables the selection of network slices becomes indispensable. Following the 3GPP Technical Specification for the 5G Architecture, the inclusion of the Network Slice Selection Function (NSSF) in order to leverage the process of slice selection is apparent. However, actual implementation of this network function needs to deal with the dynamic changes of network instances, due to this, a platform that supports the orchestration of Virtual Network Functions (VNF) is required. Our proposed solution include the use of the Central Office Rearchitected as a Data Center (CORD) platform, with the specified profile for mobile networks (M-CORD) that integrates a service orchestrator (XOS) alongside solutions oriented to Software Defined Networking (SDN), Network Function Virtualization (VNF) and virtual machine management through OpenStack, in order to provide the right ecosystem where our implementation of NSSF can obtain slice information dynamically by relying on synchronization between back-end services and network function instances.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.3
• /
• pp.896-915
• /
• 2023

The network function virtualization (NFV) uses virtualization technology to separate software from hardware. One of the most important challenges of NFV is the resource management of virtual network functions (VNFs). According to the dynamic nature of NFV, the resource allocation of VNFs must be changed to adapt to the variations of incoming network traffic. However, the significant delay may be happened because of the reallocation of resources. In order to balance the performance between delay and quality of service, this paper firstly made a compromise between VNF migration and energy consumption. Then, the long short-term memory (LSTM) was utilized to forecast network traffic. Also, the asymmetric loss function for LSTM (LO-LSTM) was proposed to increase the predicted value to a certain extent. Finally, an experiment was conducted to evaluate the performance of LO-LSTM. The results demonstrated that the proposed LO-LSTM can not only reduce migration times, but also make the energy consumption increment within an acceptable range.

• Journal of The Korean Society of Integrative Medicine
• /
• v.12 no.1
• /
• pp.109-124
• /
• 2024

Purpose : The purpose of this study was to examine the effects of a virtual reality-based complex cognitive training program for depression, cognitive function, and digital divide reduction in the elderly who have not been diagnosed with dementia or MCI. Methods : We enrolled 16 participants who were over 65 years old and not been diagnosed with dementia or MCI. We randomly divided into three groups (A, B, C). Participants underwent an 8-week virtual reality-based complex cognitive training program (60 minutes each session, twice per week). At a baseline, all participants completed questionnaires on general features, depression and cognitive function. After four weeks, all participants completed questionnaires on depression and cognitive function. After the end of the last program, participants conducted questionnaires on depression, cognitive function, and usability evaluation. Results : At the 8-week follow-up, 16 participants completed the program. Compared to the baseline, the average score of cognitive function was increased (from 26.5 to 28.5), although it was not statistically significant (p<.061). There were no significant differences between baseline and post-training evaluations on depression scores. The average score of usability evaluation was 75.56, which corresponds to good. Conclusion : Even though the results showed no statistically significant findings in cognitive function and depression after the virtual reality-based complex cognitive training intervention, this pilot study proposed the possibility of utilizing the virtual reality program as a tool that provides active learning opportunities for the elderly and helps improve their cognitive function through multi-sensory components. Also, the findings of this study suggested a positive reevaluation of the elderly's digital access capabilities while reducing the digital divide. A virtual reality-based complex cognitive training program improved the social network of the elderly. We expect that it will expand in size and help with their social participation of the elderly.

• Journal of Institute of Control, Robotics and Systems
• /
• v.15 no.7
• /
• pp.694-699
• /
• 2009

This paper presents the implementation of the real-time object tracking control of the ROBOKER head. The visual servoing technique is used to track the moving object, but suffers from ill-estimated Jacobian of the virtual link design. To improve the tracking performance, the RBF(Radial Basis Function) network is used to compensate for uncertainties in the kinematics of the robot head in on-line fashion. The reference compensation technique is employed as a neural network control scheme. Performances of three schemes, the kinematic based scheme, the Jacobian based scheme, and the neural network compensation scheme are verified by experimental studies. The neural compensation scheme performs best.