• Journal of Communications and Networks
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• v.8 no.2
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• pp.234-240
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• 2006

We study the dynamic burst assembly based on traffic prediction and offset and delay differentiation in optical burst switching network. To improve existing burst assembly mechanism and build an adaptive flexible optical burst switching network, an approach called quality of service (QoS) based adaptive dynamic assembly (QADA) is proposed in this paper. QADA method takes into account current arrival traffic in prediction time adequately and performs adaptive dynamic assembly in limited burst assembly time (BAT) range. By the simulation of burst length error, the QADA method is proved better than the existing method and can achieve the small enough predictive error for real scenarios. Then the different dynamic ranges of BAT for four traffic classes are introduced to make delay differentiation. According to the limitation of BAT range, the burst assembly is classified into one-dimension limit and two-dimension limit. We draw a comparison between one-dimension and two-dimension limit with different prediction time under QoS based offset time and find that the one-dimensional approach offers better network performance, while the two-dimensional approach provides strict inter-class differentiation. Furthermore, the final simulation results in our network condition show that QADA can execute adaptive flexible burst assembly with dynamic BAT and achieve a latency reduction, delay fairness, and offset time QoS guarantee for different traffic classes.

• Journal of Advanced Navigation Technology
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• v.17 no.4
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• pp.435-441
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• 2013

This paper proposes a neural net based-predictive connection admission control (CAC) scheme for multiclass users in wireless cognitive radio networks. We classifies cognitive users(cu) into real and non real time services, and then permit only real time services to reserve the demanded resource for spectrum handoff in guard channel for provisioning the desired QoS. Neural net is employed to predict primary user's arrival on time and demanded channels. Resource scheduling scheme is based on $C_IA$(cognitive user I complete access) shown in this paper. For keeping primary users from interference, the CAC is performed on only cognitive user not primary user. Simulation results show that our schemes can guarantee the desired QoS by cognitive real time services.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.11
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• pp.369-382
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• 2015

Energy aware server clusters aim to reduce power consumption at maximum while keeping QoS(quality of service) compared to energy non-aware server clusters. They adjust the power mode of each server in a fixed or variable time interval to activate only the minimum number of servers needed to handle current user requests. Previous studies on energy aware server cluster put efforts to reduce power consumption or heat dissipation, but they do not consider energy efficiency well. In this paper, we propose an energy efficient cluster management method to improve not only performance per watt but also QoS of the existing server power mode control method based on autonomous learning. Our proposed method is to adjust server power mode based on a hybrid approach of autonomous learning method with multi level thresholds and power consumption prediction method. Autonomous learning method with multi level thresholds is applied under normal load situation whereas power consumption prediction method is applied under abnormal load situation. The decision on whether current load is normal or abnormal depends on the ratio of the number of current user requests over the average number of user requests during recent past few minutes. Also, a dynamic shutdown method is additionally applied to shorten the time delay to make servers off. We performed experiments with a cluster of 16 servers using three different kinds of load patterns. The multi-threshold based learning method with prediction and dynamic shutdown shows the best result in terms of normalized QoS and performance per watt (valid responses). For banking load pattern, real load pattern, and virtual load pattern, the numbers of good response per watt in the proposed method increase by 1.66%, 2.9% and 3.84%, respectively, whereas QoS in the proposed method increase by 0.45%, 1.33% and 8.82%, respectively, compared to those in the existing autonomous learning method with single level threshold.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.2
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• pp.53-60
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• 2023

Recently, the demand and traffic volume for various multimedia contents are rapidly increasing through real-time streaming platforms. In this paper, we predict real-time streaming traffic to improve the quality of service (QoS). Statistical models have been used to predict network traffic. However, since real-time streaming traffic changes dynamically, we used recurrent neural network-based deep learning models rather than a statistical model. Therefore, after the collection and preprocessing for real-time streaming data, we exploit vanilla RNN, LSTM, GRU, Bi-LSTM, and Bi-GRU models to predict real-time streaming traffic. In evaluation, the training time and accuracy of each model are measured and compared.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.37 no.6
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• pp.381-381
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• 2000

In this paper, we propose a real-time bandwidth allocation scheme of multiplexed VBR MPEG video sequences over ATM network. In order to real time processing, multiplexed source is estimated by linear-prediction per measurement period. If the result CLR value were not sufficient, we proposed a overallocation method and a reallocation one to guarantee QoS. We used two kinds of sources, one is random multiplexed source made of foot different video sources, the other is the one considered the arrange of I frame in the sequence. With those sources, we analyzed the linear prediction, compared overallocation with reallocation method. As a result, in both schemes, the objected CLR value is achieved, the sufficient value COR under 10% when mp is over 1.8 sec, the utilization is over 0.9. Especially, the COR value of the reallocation scheme is better at the same condition.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.37 no.6
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• pp.21-27
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• 2000

In this paper, we propose a real-time bandwidth allocation scheme of multiplexed VBR MPEG video sequences over ATM network. In order to real time processing, multiplexed source is estimated by linear-prediction per measurement period. If the result CLR value were not sufficient, we proposed a overallocation method and a reallocation one to guarantee QoS. We used two kinds of sources, one is random multiplexed source made of foot different video sources, the other is the one considered the arrange of I frame in the sequence. With those sources, we analyzed the linear prediction, compared overallocation with reallocation method. As a result, in both schemes, the objected CLR value is achieved, the sufficient value COR under 10% when mp is over 1.8 sec, the utilization is over 0.9. Especially, the COR value of the reallocation scheme is better at the same condition.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.2B
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• pp.44-52
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• 2005

This paper proposes a neural network prediction model to improve the valid packet transmission rate for the QoS(Quality of Service) of multimedia transmission. The Round Trip Time(RTT) and Packet Loss Rate(PLR) are predicted using a neural network and then the transmission rate is decided based on the predicted RTT and the PLR. The suggested method will improve the transmission rate since it uses the rate control factors corresponding to time of data is being transmitted, while the conventional one uses the transmission rate determined based on the past informations. An experimental set-up has been established using a Linux PC system, and the multimedia data are transmitted using UDP protocol in real time. The valid transmitted packets are about 5% higher than the one in the conventional TCP-Friendly congestion control method when the suggested algorithm was applied.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.3 s.345
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• pp.78-83
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• 2006

In this paper, we propose Active bandwidth allocation scheme of multiplexed streamed MPEG video sequences over BcN network. In order to real time processing, multiplexed source is estimated by linear-prediction per measurement period. n the result target quality value were not sufficient, we proposed a over-allocation method and a reallocation one to guarantee QoS. We used two kinds of sources, one is random multiplexed source made of four different video sources, the other is the one considered the arrange of I frame in the sequence. With those sources, we analyzed the linear prediction, compared over-allocation with reallocation method. As a result, In both schemes, the objected target quality value is achieved, the sufficient valuce bandwidth under 10% when measurement period is over 1.8 sec, the utilization is over 0.9. Especially, the Target of quality value of the reallocation scheme is better at the same condition.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.6A
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• pp.927-934
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• 2000

In this paper, we propose a predictive(transient) connection admission control(CAC) scheme for satellite systems that supports on-board packet switching of multimedia traffic with predefined quality of service(QoS) requirements. The CAC scheme incorporates the unique characteristics of satellite systems, e.g. large propagation delays, no onboard buffer, and low computational requirement. The CAC scheme requires the estimation of the On-Off traffic characteristics ($\lambda$, $\mu$) of the traffic sources. These estimated values are used to predict the transient cell loss ratio at each downlink. In case the QoS requirements are not met the proposed CAC scheme rejects the new connection. The numerical results obtained suggest that the proposed scheme is an excellent candidate for real time burst and cell level connection prediction and control in broadband on-board satellite networks.

• 전자공학회논문지 IE
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• v.45 no.1
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• pp.15-23
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• 2008

In this paper, an algorithm, that provisions absolute differentiation of packet delays is proposed, simulated, and implemented with VHDL on XPC 860 CPU based test board with an objective for enhancing quality of service (QoS) in future packet networks. It features a scheme that compensates the deviation for prediction on the traffic to be arrived continuously. It predicts the traffic to be arrived at the beginning of a time slot and measures the actual arrived traffic at the end of the time slot and derives the difference between them. The deviation is utilized to the delay control operation for the next time slot to offset it. As it compensates the prediction error continuously, it shows superior adaptability to the bursty traffic as well as the exponential traffic. It is demonstrated through both simulation and the real traffic test on the board that the algorithm meets the quantitative delay bounds and shows superiority to the traffic fluctuation in comparison with the conventional non-adaptive mechanism.