• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.4B
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• pp.255-269
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• 2006

The differentiated services(DiffServ) architecture provides packet level service differentiation through the simple and predefined Per-Hop Behaviors(PHBs). The Assured Forwarding(AF) PHB proposed as the assured services uses the RED-in/out(RIO) approach to ensusre the expected capacity specified by the service profile. However, the AF PHB fails to give good QoS and fairness to the TCP flows. This is because OUT(out- of-profile) packet droppings at the RIO buffer are unfair and sporadic during only network congestion while the TCP's congestion control algorithm works with a different round trip time(RTT). In this paper, we propose an Adaptive Regulating Drop(ARD) marker, as a novel dropping strategy at the ingressive edge router, to improve TCP fairness in assured services without a decrease in the link utilization. To drop packets pertinently, the ARD marker adaptively changes a Temporary Permitted Rate(TPR) for aggregate TCP flows. To reduce the excessive use of greedy TCP flows by notifying droppings of their IN packets constantly to them without a decrease in the link utilization, according to the TPR, the ARD marker performs random early fair remarking and dropping of their excessive IN packets at the aggregate flow level. Thus, the throughput of a TCP flow no more depends on only the sporadic and unfair OUT packet droppings at the RIO buffer in the core router. Then, the ARD marker regulates the packet transmission rate of each TCP flow to the contract rate by increasing TCP fairness, without a decrease in the link utilization.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.1
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• pp.110-122
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• 1993

When voice is transmitted through packet switching network, there needs a overload control, that is, a control for the congestion which lasts short periods and occurrs in local extents. In this thesis, we analyzed the performance of the statistical packet voice multiplexer using the overload control strategy by bit dropping. We assume that the voice is coded accordng to (4,2) embedded ADPCM and that the voice packet is generated and transmitted according to the procedures in the CCITT recomendation G. 764. For the performance analysis, we must model the superposed packet arrival process to the multiplexer as exactly as possible. It is well known that interarrival times of the packets are highly correlated and for this reason MMPP is more suited for the modelling in the viewpoint of accuracy. Hence the packet arrival process in modeled as MMPP and the matrix geometric method is used for the performance analysis. Performance analysis is similar to the MMPP IG II queueing system. But the overload control makes the service time distribution G dependent on system status or queue length in the multiplexer. Through the performance analysis we derived the probability generating function for the queue length and using this we derived the mean and standard deviation of the queue length and waiting time. The numerical results are verified through the simulation and the results show that the values embedded in the departure times and that in the arbitrary times are almost the same. Results also show bit dropping reduces the mean and the variation of the queue length and those of the waiting time.

• Journal of information and communication convergence engineering
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• v.9 no.4
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• pp.411-419
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• 2011

In this paper, we propose an Adaptive Regulating Drop (ARD) marker, as a novel dropping strategy at the ingressive edge router, to improve TCP fairness in assured services (ASs) without a decrease in the link utilization. To drop packets pertinently, the ARD marker adaptively changes a Temporary Permitted Rate (TPR) for aggregate TCP flows. The TPR is set larger than the current input IN packet rate of aggregate TCP flows while inversely proportional to the measured input OUT packet rate. To reduce the excessive use of greedy TCP flows by notifying droppings of their IN packets constantly to them without a decrease in the link utilization, the ARD marker performs random early fair remarking of their excessive IN packets to OUT packets at the aggregate flow level according to the TPR. In addition, an aggregate dropper is combined to drop some excessive IN packets fairly and constantly according to the TPR. Thus, the throughput of a TCP flow no more depends on only the sporadic and unfair OUT packet droppings at the RIO buffer in the core router. Then, the ARD marker regulates the packet transmission rate of each TCP flow to the contract rate by increasing TCP fairness, without a decrease in the link utilization.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.420-421
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• 2011

This paper derive an optimal distributed power control strategy under both the probability of dropping a packet due to buffer overflow constraints at the secondary users and the interference constraints to the primary users.

• Journal of Communications and Networks
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• v.16 no.2
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• pp.193-201
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• 2014

In cognitive radio networks, the packet transmissions of the secondary users (SUs) can be interrupted randomly by the primary users (PUs). That is to say, the PU packets have preemptive priority over the SU packets. In order to enhance the quality of service (QoS) for the SUs, we propose a spectrum access strategy with an ${\alpha}$-Retry policy. A buffer is deployed for the SU packets. An interrupted SU packet will return to the buffer with probability ${\alpha}$ for later retrial, or leave the system with probability (1-${\alpha}$). For mathematical analysis, we build a preemptive priority queue and model the spectrum access strategy with an ${\alpha}$-Retry policy as a two-dimensional discrete-time Markov chain (DTMC).We give the transition probability matrix of the Markov chain and obtain the steady-state distribution. Accordingly, we derive the formulas for the blocked rate, the forced dropping rate, the throughput and the average delay of the SU packets. With numerical results, we show the influence of the retrial probability for the strategy proposed in this paper on different performance measures. Finally, based on the trade-off between different performance measures, we construct a cost function and optimize the retrial probabilities with respect to different system parameters by employing an iterative algorithm.

• Journal of Communications and Networks
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• v.5 no.1
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• pp.73-81
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• 2003

The controlled load service defined within the IETF's Integrated Services architecture for quality-of-service (QoS) in the Internet requires source nodes to regulate their traffic while the network, in combination with an admission control strategy, provides a guarantee of performance equivalent to that achieved in a lightly loaded network. Packets sent in violation of the traffic contract are marked so that the network may assign them a lower priority in the use of bandwidth and buffer resources. In this paper, we define the requirements of a scheduler serving packets belonging to the controlled load service and present a novel scheduler that exactly achieves these requirements. In this set of requirements, besides efficiency and throughput, we include an additional important requirement to bound the additional delay of unmarked packets caused due to the transmission of marked packets while dropping as few marked packets as possible. Without such a bound, unmarked packets that are in compliance with the traffic contract are not likely to experience delays consistent with that in al lightly loaded network. For any given desired bound ${\alpha}$ on this additional delay, we present the CL(${\alpha}$) scheduler which achieves the bound while also achieving a per-packet work complexity of O(1) with respect to the number of flows. We provide an analytical proof of these properties of the CL(${\alpha}$) scheduler, and we also verify this with simulation using real traces of video traffic. The scheduler presented here may be readily adapted for use in scheduling flows with multi-level priorities such as in some real-time video streams, as well as in other emerging service models of the Internet that mark packets to identify drop precedences.