A survey on Red and Some It s Varients Incongestioncontrol Mechanism

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1 Volume-4, Issue-4, August-2014, ISSN No.: International Journal of Engineering Management Research Available at: Page Number: A survey on Red Some It s Varients Incongestioncontrol Mechanism r. Vinodani Katiyar 1, Anamol Ch Jain 2 1 ean, Faculty of Computer Science & Engineering, SRMU, Lucknow, INIA 2 Research Scholar, epartment of Computer Application, TMU Moradabad, INIA ABSTRACT The present paper is a surveyof rom early detection with some of its variants (, G, AG, G) for congestion avoidance mechanism. The study is conducted based on delay, throughput, packet loss average queue length, with the aim improve the network performance. Keywords-- Congestion control, Gentle rom early detection (G), Adaptive G, ynamic Rom early detection (), G, Performance evaluation. I. INTROUCTION The high reliable exchange of data using the Internet has been important for its explosive growth utilization. The Transmission Control Procol, which is called TCP, is well known as this exchange. Under TCP, a windowflow-control mechanism is used set its transmission rate. In this mechanism, TCP increases the window size during successful data transmission. Conversely TCP cuts the window size in half whenever a data does not reach the receiver. Such data losses called packet losses can affect network performance. One of causes of this is that TCP has no information of network mechanisms contributing packet loss [1]. Some kinds of AQM schemes are proposed, e.g. Rom Early etection() [2], Virtual Queue [3], Rom Early Marking(REM), Adaptive Virtual Queue(AVQ) [4] Proportional Integral Controller [5]. Based on the control theory, it seems possible design that congestion controllers(aqm schemes) achieve better performances those AQM schemes do. AQM design problems are important become useful in future researches because AQM is embedded in the router having much information about circumstances of current networks. The drawbacks of congestions are as follows. Congestion plays a major role in worsening network performance by increasing the packet dropping probability (p) increasing packet loss probability (PL). In addition, congestion may lead an increase in the mean queue length (mql) the mean waitingtime () of packets, which will finally degrade the amount of packets passing through the buffer of the routers, namely, the throughput (T) [6]. Congestion is associated with the status of the average queue length (aql) which in turnaffects network performance. When aql value increases, T value likewise increases. At thesame time, PL increase, the router buffer overflows. By contrast, when the aql value relatively decreases, T likewise decrease. Network efficiency is decreased in both cases. Thus, congestion control is required maintain a stable aql value, optimize the utilization of network resources, enhance its performance. Enormous congestion control algorithms, such as Rom Early etection() [7],Gentle (G) [8], Adaptive Gentle (AG) [9], G[10] have been proposed. II., G, AG G Enormous algorithms for congestion control, such as, G, AG, other time-discrete queue analytical models, have been built based on AQM. Generally, detects the congestion by initially computing the aql comparing it with the minthres hold maxthreshold. Congestion does not occur when aql is smaller the minthreshold. Therouters, therefore, do not drop any packet. If the aql is between the two thresholds, thearriving packet is dropped the probability is calculated asp alleviate congestion.finally, when the aql is above the maxthreshold, all arriving packets are dropped a pvalue equal one.generally, 's drawback is the varying aql computed according the congestion status. Hence, if the congestion status is light, the aql value will be close the minthreshold. If the congestion status is severe, the aql value will be close the maxthreshold; thus, the packet p will increase, the buffer will overflow. Another drawback is the relianceof the computed aql on the traffic load (number of connections). 184

2 Fig.1 Single routerbuffer for If the traffic load is high, the aql value may exceed the maxthreshold. In such a case, network performance in manyaspects will worsen. Therefore, the router buffer will drop every arriving packet. Thus, the parameters must be set at particular values ensure satisfacry performance. Ifthe traffic load is low, the aql will normally be lesser the minthreshold. Consequently,no arriving packet is dropped. Overall, cannot stabilize its aql value between the minthres hold maxthreshold when the traffic load changes suddenly (i.e., bursty traffic)[11, 12]. G was proposed overcome some of the limitations in [12,13].Similar, the G algorithm mainly aims manage control the congestion networks at the early stage. G implements its algorithm by stabilizing the aql ata certain level. G employs a approach used by in calculating the p. However, G utilizes three thresholds, namely, minimum, maximum, doublemaximum. G also has some limitations. First, G deals with several thresholdvalues. Second, G must set its parameters specific values obtain satisfacry performance (i.e. parameterization). Third, when the aql is less the minthreshold heavy congestion occurs, the aql will take time adjust, during which the router buffer will likely overflow. Thus, no packets are dropped despite the overflowing Grouter buffer. The AG algorithm is proposed improve the performance of G during routerbuffer congestion (i.e., deriving better quality results with reference the mql,, PL performance measures). In addition, the AG algorithm aims enhance theparameter settings (e.g., the maxthreshold the maximum value of int, which is themax of G). The calculation of the aql in AG is also that in G. Fig.2 Single router buffer for G AN AG Therefore, AG decides on packet dropping in a manner that in G.The main difference between the G the AG lies in the calculation of the init value (the initial packet p). In AG, the init value varies between the maxvalues 0.5, as long as the aql value is between the maxthreshold doublemaxthreshold.in G, when the aql value is between the maxthreshold the doublemaxthreshold,the calculated init value of G varies from the max value 1.0. G is an extension of G. Gemploys a dynamic maxthreshold 185

3 Fig.3 Single router buffer for G doublemaxthreshold control the congestion in therouter buffer at the early stage before it overflows. In G algorithm a new defined value called Target aql(taql) is calculatedbetween the minthreshold maxthreshold which provids betterperformance results. These better performance resultsare represented by the results of mean queue length, average queuing delay packetloss probability when heavy congestion has occurred.g also updates the maxthreshold doublemaxthreshold parameters at therouter buffer enhance network performance. G uses the G algorithm'spolicy in dropping packets with probability when the aql is between the minthreshold doublemaxthreshold[14]. The comparison gone through using several performance metrics (e.g., mql, T,, PL, p), which are discussed below. III. COMPARISON BASE ONMQL, THROUGHPUT, ELAY,PACKET LOSS p The mql for all algorithms is identical up certainvalue of the probability of packet arrival (e.g., 0.33). In such a low probability value, thereis at most a light congestion state because the probability of packet arrival is lower that of packet departure (a < B). In such case, all the compared algorithms sustain agood stable mql. However, for a higher probability value, congestion is more likely exist at the router buffers. Accordingly, the mql of the AQM algorithms increasesexponentially. AG G have good performance in terms of delay. This result isdue the fewer dropped packets in G those in, G, AG. The throughput of the compared algorithms give Tresults, whether the probability of packet arrival is set a value lower or higher theprobability of packet departure value. On thecontrary, the T results for all compared algorithms are stabilized at the value of the packetdeparture probability when there is congestion at the router buffer of the algorithms. The performance measure results of PL P are computedafter the system reaches a steady state for, G, AG G. The results of PL P are obtained as beforeby running the algorithm simulations ten times with various rom speeds, then takingthe mean of the ten results. When the packet arrival probability is smaller thepacket departureprobability, all algorithms provide PL results under either a lightcongestion or no congestion situation.the G algorithm evidently drops more packetsat the router buffer the, G, AG algorithms when the probabilityof packet arrival is higher the probability of packet departure. Similarly, the reasonfor this result is because the router buffer in the G algorithm overflows at an earliertime compared with those in, G, AG [15]. Generally, the disadvantages of the existing congestion control algorithm can be summarized as follows. With bursty traffic, a heavy congestion signal is given out, which then leads large packet drops. Conversely, network performance becomes degraded when the probability of packet dropping is set o low. Specifically, p, PL, mql, will increase, T will decrease. Consequently, a dynamic mechanism is required implement packet dropping based on the congestion status. Improving network performance involves alleviating PL obtaining more satisfacry performance measurement results with reference mql when heavy congestion occurs at the router buffers of networks. 186

4 Mechani sm /Parame ters AQL THROU GHPUT (T) ELAY () PACKE T LOSS (PL) PACKE T ROP (P) IV. COMPARISON TABLE G AGRE AQL Throug hput is G, AGRE, GRE delay packet loss Packet drop is low AG, G if packet arrival probabili ty is not higher Through put is, AG, G AG, G if packet arrival probabili ty is not high greater G AG Packet drop is lower G AGRE but greater G GRE Throug hput is, G, GRE G, GRE G higher GRE G lower GRE G AG G Through put is, AG GRE Lowest Lowest Highest V. CONCLUSION The, G, AG, G algorithms provide performance measure results (mql, T,, PL p) when the probability of packet arrival is set a value lower the probability of packet departure or in the event of light or no congestion.ag G have good performance in terms of delay. This result is due the fewer dropped packets in G those in, G, AG. In addition, the, G, AG, G algorithms obtain T results with such values of packet arrival probability. REFERENCES [1] S.Low, F.Paganini J.oyle: Internet Congestion Control, IEEE Control Systems Magazine, Vol. 22, No. 2, pp (2002). [2] S.Floyd V.Jacobson, Rom early detection gateways for congestion avoidance, IEEE/ACM Trans. On Networking, Vol. 1, no. 4, pp (1993). [3] R.Gibbson F.Kelly, istributed connection acceptance control for a connectionless network, in Proceedings of the 16th Intl. Telegraphic Congress (1999). [4] S.Kunniyur R.Srikant, Analysis design of an adaptive virtual queue, in Proceedings of ACM/SIGCOMM (2001). [5] C.Hollot, V.Misra,.Towsley, W.Gong, Analysis design of controllers for AQM routers supporting TCP flows, IEEE Trans. on Aumatic Control, Vol. 47, No. 6, pp (2002). [6]. Lin R. Morris, ynamics of rom early detection, Proc. of ACM SIGCOMM, New York,NY, USA, pp , [7] S. Floyd V. Jacobson, Rom early detection gateways for congestion avoidance, IEEE/ACMTransactions on Networking, pp , [8] S. Floyd, Recommendations on Using the Gentle Variant of [9] M. Bakliziet.al., Performance assessment of AG, G congestion control algorithms, Information Technology Journal, vol.11, pp , [10]M. Baklizi1, et.al. ynamic schastic early discovery: a new congestion control technique improve networks performance,icic, International Journal of InnovativeComputing, Information Control2013 ISSN Volume 9, Number 3, March 2013 pp [11] F. Wu-chang et al., The blue active queue management algorithms, IEEE/ACM Transactions on Networking, vol.10, pp , [12] S. Floyd et al., Adaptive : An algorithm for increasing the robustness of 's active queue management, AT&T Centre for Internet Research,

5 [13] J.Aweya et al., A control theoretic approach active queue management, Computer Networks, vol.36, pp , [14] B.Mahamoud et.al. ynamic Schastic Early iscovery: A NewCongestion Control Technique To ImproveNetworks Performance,International Journal of InnovativeComputing, Information Control, Volume 9,pp , Number 3, March 2013, ISSN , [15] J. Ababneh et al., erivation of three queue nodes discrete-time analytical model based on algorithm", Proc. of the 7th International Conference on Information Technology: NewGenerations,pp ,2010. Copyright Vana Publications. All Rights Reserved. 188