• Journal of Communications and Networks
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• v.9 no.3
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• pp.319-329
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• 2007

This paper presents a price-based quality-of-service (QoS) control framework for two-class network services, in which circuit-switched and packet-switched services are defined as "premium service class" and "best-effort service class," respectively. Given the service model, a customer may decide to use the other class as a perfect or an imperfect substitute when he or she perceives the higher utility of the class. Given the framework, fixed-point problems are solved numerically to investigate how static pricing can be used to control the demand and the QoS of each class. The rationale behind this is as follows: For a network service provider to determine the optimal prices that maximize its total revenue, the interactions between the QoS-dependent demand and the demand-dependent QoS should be thoroughly analyzed. To test the robustness of the proposed model, simulations were performed with gradually increasing customer demands or network workloads. The simulation results show that even with substantial demands or workloads, self-adjustment mechanism of the model works and it is feasible to obtain fixed points in equilibrium. This paper also presents a numerical example of guaranteeing the QoS statistically in the short term-that is, through the implementation of pricing strategies.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.12
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• pp.3132-3143
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• 1996

In the evaluation of Broadband-ISDN, the main issue is to interconnect existing LANs and MANs to B-ISDN based on Asynchrous Transfer Mode. However, LANs provide connectionless services, whereas the ATM network provides connection-oriented services. So some problem arises from accessing ATM switchs to transparently transmit connectionless data via B-ISDN. Two methods then were recommended by ITU-TS to support the connectionaless services. In the paper, we do the numerical analysis, using the stochastic fluid flow method, of dynamic bandwidth allocation sheme with two class traffics in the virtual path established between Interworking Units(JWUS) interconnecting LANs to an ATM network and Connectionless Servers(CLSs). The loss mechanism of each traffic is controlled by the contents of buffer. The vandwidth which the IWU requests the CLS is estimated by the number of On-Off sources in the On state. We use the stochsastic fluide flow method which is to find the steady-state joint probability in each level of the IWU buffer. The length of the IWU buffer as the real random variable is changed by traffics entering the IWU.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.45 no.9
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• pp.39-48
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• 2008

Recently, many multimedia services have emerged in Internet such as real-time and non- real time services. However, in this Internet environment, we have some limitations to satisfy each service feature. To guarantee the service features in Measurement-Based Admission Control(MBAC) based system on the flow-aware network, there is the method applying Dynamic Priority Scheduling(DPS) algorithm that gives a higher priority to an earlier incoming flow in all of the link bandwidth. This paper classifies all flows under several groups according to flow characteristics on per-flow MBAC algorithm based system. In each flow group, DPS algorithm is applied. This paper proposes two methods that are a DPS based bandwidth borrowing method and a bandwidth dynamic allocation method. The former is that if low priority class has available bandwidths, the flow of high priority class borrows the bandwidth of low priority class when high priority flow has insufficient bandwidth to connect a flow call. The later is that the each group has a minimum bandwidth and is allocated the bandwidth dynamically according to the excess rate for available bandwidth. We compare and analyze the characteristics of the two proposed methods through the simulation experiments. As the results of the experiment, the proposed methods are more effective than existing DPS based method on the packet loss and delay characteristics. Consequently the proposed two methods are very useful in various multimedia network environments.

• The KIPS Transactions:PartC
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• v.17C no.2
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• pp.189-196
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• 2010

The LEACH does not use flooding method for data transmission and this makes low power consumption. So performance of the WSN is increased. On the other hand, QoS based algorithm which use restricted flooding method in WSN also achieves low power consuming rate by reducing the number of nodes that are participated in routing path selection. But when the data is delivered to the sink node, the LEACH choose a routing path which has a small hop count. And it leads that the performance of the entire network is worse. In the paper we propose a QoS based energy efficient clustering and routing algorithm in WSN. I classify the type of packet with two classes, based on the energy efficiency that is the most important issue in WSN. We provide the differentiated services according to the different type of packet. Simulation results evaluated by the NS-2 show that proposed algorithm extended the network lifetime 2.47 times at average. And each of the case in the class 1 and class 2 data packet, the throughput is improved 312% and 61% each.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.12C
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• pp.1197-1206
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• 2002

Recently, the research on the construction of optical Internet backbone networks using WDM has been actively progressed. In this paper, we propose the offset based service-differentiation scheme in optical burst switching (OBS) networks. We classify the services into two classes; high class and low class. The proposed scheme decides the offset time of high class so as to satisfy the required burst loss rate (BLR) of services according to the service-differentiation ratio (SDR). The SDR may be set depending on the environment of OBS network and user's requirements, our scheme decides the BLR of high class by using the required SDR and conservation law. Finally, we use the offset time decision (OTD) algorithm to determine the offset time of high class. The proposed scheme is evaluated through simulation. The result of simulation is compared with that of analysis to verify the proposed scheme.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.95-112
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• 2021

In university education, the choice of major class plays an important role in students' careers. However, in line with the changes in the industry, the fields of major subjects by department are diversifying and increasing in number in university education. As a result, students have difficulty to choose and take classes according to their career paths. In general, students choose classes based on experiences such as choices of peers or advice from seniors. This has the advantage of being able to take into account the general situation, but it does not reflect individual tendencies and considerations of existing courses, and has a problem that leads to information inequality that is shared only among specific students. In addition, as non-face-to-face classes have recently been conducted and exchanges between students have decreased, even experience-based decisions have not been made as well. Therefore, this study proposes a recommendation system model that can recommend college major classes suitable for individual characteristics based on data rather than experience. The recommendation system recommends information and content (music, movies, books, images, etc.) that a specific user may be interested in. It is already widely used in services where it is important to consider individual tendencies such as YouTube and Facebook, and you can experience it familiarly in providing personalized services in content services such as over-the-top media services (OTT). Classes are also a kind of content consumption in terms of selecting classes suitable for individuals from a set content list. However, unlike other content consumption, it is characterized by a large influence of selection results. For example, in the case of music and movies, it is usually consumed once and the time required to consume content is short. Therefore, the importance of each item is relatively low, and there is no deep concern in selecting. Major classes usually have a long consumption time because they have to be taken for one semester, and each item has a high importance and requires greater caution in choice because it affects many things such as career and graduation requirements depending on the composition of the selected classes. Depending on the unique characteristics of these major classes, the recommendation system in the education field supports decision-making that reflects individual characteristics that are meaningful and cannot be reflected in experience-based decision-making, even though it has a relatively small number of item ranges. This study aims to realize personalized education and enhance students' educational satisfaction by presenting a recommendation model for university major class. In the model study, class history data of undergraduate students at University from 2015 to 2017 were used, and students and their major names were used as metadata. The class history data is implicit feedback data that only indicates whether content is consumed, not reflecting preferences for classes. Therefore, when we derive embedding vectors that characterize students and classes, their expressive power is low. With these issues in mind, this study proposes a Net-NeuMF model that generates vectors of students, classes through network analysis and utilizes them as input values of the model. The model was based on the structure of NeuMF using one-hot vectors, a representative model using data with implicit feedback. The input vectors of the model are generated to represent the characteristic of students and classes through network analysis. To generate a vector representing a student, each student is set to a node and the edge is designed to connect with a weight if the two students take the same class. Similarly, to generate a vector representing the class, each class was set as a node, and the edge connected if any students had taken the classes in common. Thus, we utilize Node2Vec, a representation learning methodology that quantifies the characteristics of each node. For the evaluation of the model, we used four indicators that are mainly utilized by recommendation systems, and experiments were conducted on three different dimensions to analyze the impact of embedding dimensions on the model. The results show better performance on evaluation metrics regardless of dimension than when using one-hot vectors in existing NeuMF structures. Thus, this work contributes to a network of students (users) and classes (items) to increase expressiveness over existing one-hot embeddings, to match the characteristics of each structure that constitutes the model, and to show better performance on various kinds of evaluation metrics compared to existing methodologies.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.709-713
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• 2009

Recently, a lot of research that applies data acquired from devices such as cameras and RFIDs to context aware services is being performed in the field on Life-Log and the sensor network. A variety of analytical techniques has been proposed to recognize various information from the raw data because video and audio data include a larger volume of information than other sensor data. However, manually watching a huge amount of media data again has been necessary to create supervised data for the update of a class or the addition of a new class because these techniques generally use supervised learning. Therefore, the problem was that applications were able to use only recognition function based on fixed supervised data in most cases. Then, we proposed a method of acquiring supervised data from a video sharing site where users give comments on any video scene because those sites are remarkably popular and, therefore, many comments are generated. In the first step of this method, words with a high utility value are extracted by filtering the comment about the video. Second, the set of feature data in the time series is calculated by applying functions, which extract various feature data, to media data. Finally, our learning system calculates the correlation coefficient by using the above-mentioned two kinds of data, and the correlation coefficient is stored in the DB of the system. Various other applications contain a recognition function that is used to generate collective intelligence based on Web comments, by applying this correlation coefficient to new media data. In addition, flexible recognition that adjusts to a new object becomes possible by regularly acquiring and learning both media data and comments from a video sharing site while reducing work by manual operation. As a result, recognition of not only the name of the seen object but also indirect information, e.g. the impression or the action toward the object, was enabled.

• The Transactions of the Korea Information Processing Society
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• v.3 no.4
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• pp.697-708
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• 1996

In this paper, we study reactive congestion control with priority in ATM network. The priority schemes for buffer access, partial buffer sharing have been investigated in order to improve the utilization of ATM network resources the network and to satisfy the most demanding traffic class. We consider in this paper a discrete-time queueing model for partial buffer sharing with two Markov modulated Poisson inputs. This model can be used to analyze the the effects of the partial buffer sharing priority scheme on system performance for realistic cases of bursty services. Explicit formulae are derived for the number of cells in the system and the loss probabilities for the traffic. Congestion may still occur because of unpredictable statistical fluctuation of traffic sources even when preventive control is performed in the network. In this Paper, we study reactive congestion control, in which each source changes its cell emitting rate a daptively to the traffic load at the switching node. Our intention is that,by incorporating such a congcstion control method in ATM network,more efficient congsestion control is established. We develope an analytical model,and carry out an approximateanalysis of reactive congestion con-trol with priority.Numerical results show that several orders of magnitude improvement in the loss probability can be achieved for the high priority class with little impact on the low priority class performance.And the results show that the reactive congestion control with priority are very effective in avoiding congestion and in achieving the statistical gain.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.11A
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• pp.859-866
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• 2003

In this paper, we designed the bandwidth allocation scheme with priority consideration for upstream channel access of EthernetPON. The objective of our scheme is to control the multi services in more effective way according to their CoS(Class of Service) or QoS(Quality of Service). The designed scheme considers transmission priority in the both side of OLT and ONU. In the OLT's view, the Two-step scheduling algorithm is applied with which we can support multiple bandwidth allocation policies simultaneously, i.e. SBA for the time-sensitive, constant rate transmission services and DBA for the best-effort services. This Two-step scheduling algorithm reduces the scheduling complexity by separating the process of transmission start time decision from the process of grant generation. In the ONU's view, the proposed scheme controls 8 priority queues of the 802.1d recommended 8 service classes. Higher priority queue is serviced in prior during the allowed GATE time from OLT. The OPNET modeling and simulation result compares the performance of each bandwidth allocation policy with SBA or DBA only approach.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.9
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• pp.1283-1294
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• 1993

The conventional transport protocols, developed on top of the low-bandwidth high-error network services, exhibit poor performances when operating in the new high-bandwidth low-error networks. A new transport protocol HSTP(High Speed Transport Protocol) was proposed to take advantage of the new network environment. This paper gives a performance evaluation by simulation technique of the proposed HSTP and conventional TP4(Transport Protocol class 4) in their data trans-fer characteristics. The performance variation by HSTP and TP4 are investigated when their various error control techniques are employed, three different techniques for HSTP, and two for TP4. The performance enhancement in HSTP is also evaluated when the rate control technique is used as part of the flow control mechanisms.