• Proceedings of the Korea Institute of Convergence Signal Processing
• /
• 2001.06a
• /
• pp.21-24
• /
• 2001

For very large multimedia traffic to be supported successfully in wireless network environment, it is necessary to provide Quality-of-Service(QoS) guarantees between mobile hosts(clients). In order to guarantee the Qos, we have to keep the call blocking probability below target value during handoff session. However, the QoS negotiated between the client and the network may not be guaranteed due to lack of available channels for traffic in the new cell, since mobile clients should be able to continue their on-going sessions. In this paper we propose a efficient load-balancing algorithm based on the adaptive bandwidth reservation scheme for enlarging available channels in a cell. We design a new method to predict the mobility of clients using MPT(mobility profile table). This method is then used to reserve a part of bandwidths for handoff calls to its adjacent cells and this reserved bandwidth can be used for handoff call prior to new connection requests. If the number of free channels is also under a low threshold value, our scheme use a load-balancing algorithm with a adaptive bandwidth reservation. In order to evaluate the performance of our algorithm, we measure the metrics such as the blocking probability of new calls and dropping probability of handoff calls, and compare with other existing schemes.

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

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2014.10a
• /
• pp.527-530
• /
• 2014

DASH is a well-known streaming technology, which was proposed in 2010 by MPEG and standardized in 2011. Major multimedia contents service providers, including Apple, Microsoft, and Adobe are all using this technology to support their media streaming services. Whenever a new service is requested to the server, the DASH technology helps servicing the multimedia streaming to client by recognizing the capacity of network and by adapting the quality of the multimedia contents. In DASH, the quality of multimedia contents will be automatically lowered to meet the fluctuating network status, when undesirable breaks interrupt the network. In this paper, we classified and analysed the advantages and disadvantages of DASH researches in three aspects: bit-rate measurement method, bandwidth aggregation method; rate adaptation metrics, algorithms and logics; user's experiences and QoE.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.10 no.3
• /
• pp.103-110
• /
• 2010

This paper addressed the implementation of the systematic performance analysis of Quality of Service (QoS) by using OPNET simulator in the interworking architecture of IEEE 802.16e (mobile WiMAX) and IEEE 802.11e (WLAN) wireless network. Four simulation cases were provided in OPNET simulator and a voice traffic was simulated with various performance metrics, such as Mean Opinion Score (MOS), end-to-end delay and packet transmission ratio. Based on the simulation results, the MOS value presented better in WiMAX to WiMAX case compared to others in both static and mobility case. Meanwhile, end-to-end delay was not greatly affected by mobility in four cases. However, mobility was affected much in MOS value and packet transmission ratio in WLAN to WLAN case than in others.

• Journal of the Korea Society of Computer and Information
• /
• v.10 no.4 s.36
• /
• pp.337-344
• /
• 2005

UDDI server is the web services registry enabling users to register and search for web services. However, the existing UDDI servers do not provide any information about web services qualify. We designed and developed a UDDI broker system which actively monitors and analyses the qualify of web services. The analysis results are presented to users in statistical figures and graphs. With this information a user can select a web service that meets his/her needs. Availability, performance, and stability were the metrics used for the service qualify measurement and analysis.

• Journal of Internet Computing and Services
• /
• v.22 no.5
• /
• pp.27-33
• /
• 2021

With the broad adoption of the Internet of Things (IoT) in a variety of scenarios and application services, management and orchestration entities require upgrading the traditional architecture and develop intelligent models with ultra-reliable methods. In a heterogeneous network environment, mission-critical IoT applications are significant to consider. With erroneous priorities and high failure rates, catastrophic losses in terms of human lives, great business assets, and privacy leakage will occur in emergent scenarios. In this paper, an efficient resource slicing scheme for optimizing federated learning in software-defined IoT (SDIoT) is proposed. The decentralized support vector regression (SVR) based controllers predict the IoT slices via packet inspection data during peak hour central congestion to achieve a time-sensitive condition. In off-peak hour intervals, a centralized deep neural networks (DNN) model is used within computation-intensive aspects on fine-grained slicing and remodified decentralized controller outputs. With known slice and prioritization, federated learning communications iteratively process through the adjusted resources by virtual network functions forwarding graph (VNFFG) descriptor set up in software-defined networking (SDN) and network functions virtualization (NFV) enabled architecture. To demonstrate the theoretical approach, Mininet emulator was conducted to evaluate between reference and proposed schemes by capturing the key Quality of Service (QoS) performance metrics.