IJRIT International Journal Of Research In Information Technology, Volume 3, Issue 7, July 2015, Pg. 111-122

International Journal of Research in Information Technology (IJRIT) www.ijrit.com

ISSN 2001-5569

Handle Packet Flow & Traffic in High Speed Networks By Using Fuzzy Logic CH. Venkata Naryana 1, CH. Sireesha 2 1

Professor , Computer Science And Engineering, Lakireddy Balireddy College Of Engineering Mylavaram, Andhra Pradesh, India [email protected] 2

PG Scholar(M.Tech), Computer Science and Engineering, Lakireddy Balireddy College of Engineering Mylavaram, Andhra Pradesh, India [email protected]

Abstract In present days internet traffic is very high,this paper tells about the distributed traffic management, in which routers are moving from one position to another with intelligent data rate controllers to deal with the traffic crowd, other explicit traffic control protocols also there to estimate network parameters E.g., link latency, bottleneck bandwidth, packet loss rate, or the number of flows in order to estimate the tolerable source sending rate. We can measure the router queue size directly using fuzzy-logic-based controller,hence it avoids various performance problems occuring from parameter estimations while dropping consumption of computation and memory resources in routers. Based on the network parameter, the queue size can be exactly monitored and used to proactively decide if action should be taken to regulate the source sending rate, thus increasing the flexibility of the network to traffic jamming. The communication QoS is secure by the good performances of our scheme Ex.,low queueing delay and good robustness to network dynamics. Simulation results and comparisons have verified the efficiency and showed that our new traffic management scheme can achieve better performances than the existing protocols that depend on the evaluation of network parameters.

Keywords: - Handle packet flow, Fuzzy logic technology, Quality of service, Robustness, Traffic management.

1. Introduction Despite the many years of research efforts, the problem of network congestion control remains a critical issue and a high priority, especially given the growing size, demand, and speed (bandwidth) of the networks. Network congestion is becoming a real threat to the growth of existing packet-switched networks, and of the future deployment of integrated services communication networks. It is a problem that cannot be ignored. Congestion is caused by saturation of network resources, (communication links, buffers, network switches, etc…). For example, if a communication link delivers packets to a queue at a higher rate than the service rate of the queue, then the queue size will grow. If the queue space is finite then, in addition to the delay experienced by the packets until service, losses will also occur. Observe that congestion is not a static resource shortage problem, but rather a dynamic resource allocation problem. Networks need to serve all users requests, which may be unpredictable and bursty in their behavior (starting time, bit rate, and duration). However network resources are finite, and must be managed for sharing among the competing users. Congestion will occur, if the resources are not managed effectively. The optimal control of networks of queues is a well-known, much studied, and notoriously difficult problem, even for the simplest of cases. For example, Papathemetriou and Tsitsiklis. show that several versions of the problem of optimally controlling a simple network of queues with simple arrival and service distributions and multiple customer classes is complete for exponential time (i.e. provably intractable). The effect of network congestion is degradation in the network performance. The user experiences long delays in the delivery of messages, perhaps with heavy losses caused by buffer overflows. Thus there is degradation is the quality of the delivered service, with the need for CH. Venkata Naryana , IJRIT-111

IJRIT International Journal Of Research In Information Technology, Volume 3, Issue 7, July 2015, Pg. 111-122

retransmissions of packets (for services intolerant to loss). In the event of retransmissions, there is a drop in the throughput which leads to a collapse of network throughput when a substantial part of the carried traffic is due to retransmissions (in that state not much useful traffic is carried-waste of system resources).Congestion is a complex process to define. It is felt by a degradation of performance. The choice of how to measure congestion and where, apart from the other practical problems such as cost and complexity, can influence to a great degree the achievable control approach, control strategy, and control location. Here we only highlight this potential problem through an example. In the TCP/IP congestion control scheme, packet loss is used to sense congestion. The observed congestion in this case is at an advanced state (has already happened and hence losses are starting to occur). Whereas sensing delay at a node (e.g. queue length) does not necessarily indicate that congestion has happened. (Actually, one may expect that with delay sensing a predictive model can be build to indicate the level of the expected state of congestion over a given future time horizon, thus enabling corrective measures to be taken). Also other factors may influence to a large degree the effectiveness and speed of response of a congestion algorithm. For example, in TCP/IP congestion sensing is binary(presence or absence of congestion), and the round trip time (and feedback delay) are significantly different. (For an in depth discussion of these issues, the effect of location on quality of control, as seen through the control horizon, as well as potential problems of control, and how these influence the design of the controls, see ). One may also identify other potential problems of control, such as Large scale; Distributed nature; Large geographic spread (at its limit it covers the globe); Increasingly processing delay at nodes gets smaller, in comparison to the propagation delay in the links. Large bandwidth delay product makes the control of congestion through feedback potentially difficult; Diverse nature and behaviour of carried traffic (voice, video, www, ftp, ….); Unpredictable and time varying user behaviour ; Lack of appropriate dynamic models for control; and Expectation of the need for guaranteed levels of performance to each user,which can be negotiated with the network.

2. Related Work As an alternative, a class of explicit congestion control protocols has been proposed to signal network traffic level more precisely by using multiple bits. Examples are the XCP,RCP, JetMax and MaxNet .These protocol share their controllers reside in routers and directly feed link information back to sources so that the link bandwidth could be efficiently utilized with good scalability and stability in high BDP networks.Specifically,JetMax and MaxNet signal network congestion by providing the required fair rate or the maximum link price, and then the final sending rate is decided by sources according to some demand functions or utility functions. XCP feeds back the required increment or decrement of the sending rate, while RCP directly signals sources with the admissible sending rate according to which source space their throughput. The advantages of these router-assisted protocols are that 1) they can explicitly signal link traffic levels without maintaining per-flow state, and 2) the sources can converge their sending rates to some social optimum and achieve a certain optimization objective. However, most of these explicit congestion control protocols have to estimate the bottleneck bandwidth in order to compute the allowed source sending rate or link price. Recent studies show that misestimation of link bandwidth (e.g., in link sharing networks or wireless networks) may easily occur and can cause significant fairness and stability problems.There are some latest protocols on wireless applications such as QFCP (Quick Flow Control Protocol and the three protocols called Blind, ErrorS and MAC .They have improved on the estimation error while having high link utilization and fair throughput. However, they still have the fundamental problem of inaccurate estimation resulting in performance degradation. In addition, their bandwidth probing speed may be too slow when the bandwidth jumps a lot. Also, they cannot keep the queue size stable due to oscillations, which in turn affects the stability of their sending rates.

2.1 Purpose FLC (Fuzzy Logic Control) has been considered for IC (Intelligence Control). It is a methodology used to design robust systems that can contend with the common adverse synthesizing factors such as system nonlinearity, parameter uncertainty, measurement and modeling imprecision . In addition, fuzzy logic theory provides a convenient controller design approach based on expert knowledge which is close to human decision making, and readily helps engineers to model a complicated non-linear system. In fact, fuzzy logic control has been widely CH. Venkata Naryana , IJRIT-112

IJRIT International Journal Of Research In Information Technology, Volume 3, Issue 7, July 2015, Pg. 111-122

applied in industrial process control and showed extraordinary and mature control performancein accuracy, transient response, robustness and stability Specifically, the objectives of this paper are: 1) to design a new rate-based explicit congestion controller based on FLC to avoid estimating link parameters such as link bandwidth,the number of flows, packet loss and network latency, while remaining stable and robust to network dynamics (Hence, we make this controller “intelligent”); 2) to provide maxmin fairness to achieve an effective bandwidth allocation and utilization; 3) to generate relatively smooth source throughput,maintain a reasonable network delay and achieve stable jitter performance by controlling the queue size; 4) to demonstrate our controller has a better QoS performance through case study. The contributions of our work lie in: 1) using fuzzy logic theory to design an explicit rate-based traffic management scheme (called the IntelRate controller) for the high-speed IP networks; 2) the application of such a fuzzy logic controller using less performance parameters while providing better performances than the existing explicit traffic control protocols;3) the design of a Fuzzy Smoother mechanism that can generate relatively smooth flow throughput; 4) the capability of our algorithm to provide max-min fairness even under large network dynamics that usually render many existing controllers unstable.

3.Research Background 3.1 Fuzzy Logic Application For Congestion Control A network system is a large distributed complex system, with difficult often highly non-linear, time varying and chaotic behaviour. There is an inherent fuzziness in the definition of the controls (declared objectives and observed behaviour).Dynamic or static modelling o f such a system for (open or closed loop) control is extremely complex. Measurements on the state of the network are incomplete, often relatively poor and time delayed. Its sheer numerical size and geographic spread are mind-boggling. For example, customers (active services) in the 10s of millions, network elements in the 10s of million,and global coverage.Therefore, in designing the network control system, a structured approach is necessary. The traditional techniques of traffic engineering, queuing ana1ysis, decision theory, etc. should be supplemented with a variety of novel control techniques,including (nonlinear) dynamic systems, computational intelligence and intelligent control (adaptive control, learning models, neural networks, fuzzy systems, evolutionary/genetic algorithms), and artificia1 intelligence. Computational Intelligence (CI) is an area of fundamental and applied research involving numerical information processing (in contrast to the symbolic information processing techniques of Artificial Intelligence (AI)). Nowadays, CI research is very active and consequently its applications are appearing in some end user products. The definition of CI can be given indirectly by observing the exhibited properties of a system that employs CI components “A system is computationally intelligent when it: deals only with numerical (low-level) data, has a pattern recognition component, and does not use knowledge in the AI sense; and additionally, when it(begins to) exhibit · · · ·

computational adaptivity; computational fault tolerance; speed approaching human-like turnaround; error rates that approximate human performance.

The major building blocks of CI are artificial neural networks, fuzzy logic, and evolutionary computation.”While these techniques are not a panacea (and it is very important to view them as supplementing proven traditional techniques), we are beginning to see a lot of interest not only from the academic research community , but also from telecommunication companies. Fuzzy Logic Controllers (FLCs) may be viewed as alternative, non-conventional way of designing feedback controllers where it is convenient and effective to build a control algorithm without relying on formal models of the controlled system and control theoretic tools. The control algorithm is encapsulated as a set of commonsense rules. FLCs have been applied successfully to the task of controlling systems for which analytical models are not easily obtainable or the model itself, if available, is too complex and highly nonlinear.In recent years, a handful of research papers have been published on the investigation of solutions to congestion control issues in ATM networks. Given the complexity of ATM networks, rich variety of traffic sources that operate on them, and difficulty of obtaining formal models for in depth analysis, it is not surprising to see that FLCs are favored by the researchers involved in ATM network development. Purely reactive congestion control techniques will not be effective in ATM based CH. Venkata Naryana , IJRIT-113

IJRIT International Journal Of Research In Information Technology, Volume 3, Issue 7, July 2015, Pg. 111-122

multimedia and multiservice networks. Therefore, the researchers who applied the computational intelligence methods to congestion control problem mostly looked at predictive congestion control schemes. In general, the Schemes observe the short term behavior of a link to estimate the future of cell arrivals in order to predict the onset of congestion and take proactive measures to prevent its occurrence. Liu and Douligeris have proposed a combination system consisting of a leaky bucket and a fuzzy logic cell rate controller. Jensen has proposed a fuzzy system for controlling the transmission rate of sources to protect links against overload in the case of connections exceeding their negotiated traffic parameters.

4. Used Algorithms 4.1 RED Algorithm

QUEUE ESTIMATION Standard EWMA: avg = (1-wq) avg + wq qlen U want pper bound on wq depends on minth to set wq to allow a certain burst size L Ex: minth=5 and L=50, wq < 0.0042 CH. Venkata Naryana , IJRIT-114

IJRIT International Journal Of Research In Information Technology, Volume 3, Issue 7, July 2015, Pg. 111-122

Lower bound on wq to detect congestion relatively quickly Ex: wq = 0.002

THRESHOLDS minth determined by the utilization requirement need to be high for fairly bursty traffic maxth set to twice minth rule of thumb difference must be larger than queue size increase in one RTT bandwidth dependence PACKET MARKING Marking probability based on queue length Pb = maxp(avg - minth) / (maxth - minth) Just marking based on Pb can lead to clustered marking ⇒global synchronization Better to bias Pb by history of unmarked packets Pb = Pb/(1 - count×Pb) where count is the number of unmarked packets that have arrived since the last marked packet.

4.2. Fuzzy Logic Fuzzy Sets and Fuzzy Logic We showed in the last chapter that the learning problem is np-complete for a broad class of neural networks. learning algorithms may require an exponential number of iterations with respect to the number of weights until a solution to a learning task is found. a second important point is that in back propagation networks, the individual units perform computations more general than simple threshold logic. since the output of the units is not limited to the values 0 and 1, giving an interpretation of the computation performed by the network is not so easy. the network acts like a black box by computing a statistically sound approximation to a function known only from a training set. in many applications an interpretation of the output is necessary or desirable. in all such cases the methods of fuzzy logic can be used.

4.2.1 Fuzzy Logic Steps 4.2.1.1 Fuzzification and Defuzzification A fuzzy logic system (FLS) can be defined as the nonlinear mapping of an input data set to a scalar output data . A FLS consists of four main parts: fuzzier, rules, inference engine, and defuzzier. Fuzzy logic consists of following components are: -

Figure 1. Basic components of fuzzy model The process of fuzzy logic maintains the following steps: Firstly, a crisp set of input data are gathered and converted to a fuzzy set using fuzzy linguistic variables, fuzzy linguistic terms and membership functions. This CH. Venkata Naryana , IJRIT-115

IJRIT International Journal Of Research In Information Technology, Volume 3, Issue 7, July 2015, Pg. 111-122

step is known as fuzzification. Afterwards, an inference is made based on a set of rules. Lastly, the resulting fuzzy output is mapped to a crisp output using the membership functions, in the defuzzification step. Fuzzification is a process where inputs are a set of fuzzy inputs and the output is crisp values.

4.2.1.2 Fuzzy Inputs According to Gaussian, the membership function for fuzzy input sets depends on two types of parameter, standard deviation σ and mean c. The equation for membership function is:f(x; σ; c)= exp( ) In designing fuzzy inference system, it is easy to understand that membership functions are associated with term sets, which normally appears in the antecedent or consequent of rules. We have divided parameter certainty c into five categories according to its values. They are: TABLE 1 RANGES OF CERTAINTY

Class Certainty Range Symbols Name Value Very Low 0.0-0.2 VLc Low 0.1-0.4 Lc Average 0.3-0.7 Avg.c High 0.6-0.9 Hc Very 0.8-1.0 VHc High Following the same way, we have divided parameter average rating t into five categories according to its values in table 1:Though we classify the parameters value according to the ranges described above, it can be varied from persons to persons. For this, we take helps from fuzzy logic. TABLE 2 RANGES OF AVERAGE RATING

Class Name Avg. Rating Range Value Very 1.0-2.0 Low 1.5-3.0 Average 2.0-4.0 High 3.0-4.5 Very 4.25-5.0 High

Symbols VL Ltt Avg.t Ht VH t

Following the Gaussian membership function equation, we have got the figures stated below:-

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IJRIT International Journal Of Research In Information Technology, Volume 3, Issue 7, July 2015, Pg. 111-122

Figure 2. Membership Functions for certainty

Here X- axis represents the certainty deviation.

Figure 3. Membership Functions for average rating

4.2.1.3 Inference Rules Fuzzy inference is the process of formulating the mapping from a given input to an output using fuzzy logic. The mapping then provides a basis from which decisions can be made, or patterns discerned. There are two concepts of fuzzy logic systems . They are: - linguistic variables and fuzzy if then else rule. The linguistic variables’ values are words and sentences where if then else rule has two parts; antecedents and consequent parts which contain propositions of linguistic variables. Numerical values of inputs xiϵUi(i=1,2….n) are fuzzified into linguistic values, F1, F2…..Fn. Here Fi denotes the universe of discourse U = U1*U2*……..*Un. The output linguistic variables are G1,G2,……,Gn. The if-then-else rule can be defined as: R(j): IF xiϵF1j and……and xnϵFnj THEN y ϵ Gj.

( 1)

Where, j = 1,2,….., M. M is the number of rules. According to rules discussed above, we have proceeded for our proposed model. There are 25 fuzzy rules in our extension model. They are (R represents rule):R1:- If certainty is very low and average rating is very low, then trust is very low. R2:- If certainty is low and average rating is very low, then trust is very low. R3:- If certainty is average and average rating is very low, then trust is very low.

CH. Venkata Naryana , IJRIT-117

IJRIT International Journal Of Research In Information Technology, Volume 3, Issue 7, July 2015, Pg. 111-122

R4:- If certainty is high and average rating is very low, then trust is very low. R5:- If certainty is very high and average rating is very low, then trust is very low. R6:- If certainty is very low and average rating is low, then trust is very low. R7:- If certainty is low and average rating is low, then trust is low. R8:- If certainty is average and average rating is low, then trust is low. R9:- If certainty is high and average rating is low, then trust is average. R10:- If certainty is very high and average rating is low, then trust is average. R11:- If certainty is very low and average rating is average, then trust is very low. R12:- If certainty is low and average rating is average, then trust is low. R13:- If certainty is average and average rating is average, then trust is average. R14:- If certainty is high and average rating is average, then trust is average. R15:- If certainty is very high and average rating is average, then trust is high. R16:- If certainty is very low and average rating is high, then trust is very low. R17:- If certainty is low and average rating is high, then trust is low. R18:- If certainty is average and average rating is high, then trust is average. R19:- If certainty is high and average rating is high, then trust is high. R20:- If certainty is very high and average rating is high, then trust is high. R21:- If certainty is very low and average rating is very high, then trust is very low. R22:- If certainty is low and average rating is very high, then trust is low. R23:- If certainty is average and average rating is very high, then trust is average. R24:- If certainty is high and average rating is very high, then trust is high. R25:- If certainty is very high and average rating is very high, then trust is very high. According to these inference rules stated above, we have got the fuzzy input sets shown in figure 1 and figure 2. From that, we have got figure 3 output crisp values.

4..2.1.4 Fuzzy Outputs From the input fuzzy sets described above, passing those fuzzy sets through inference rules and fuzzy base rules, we get crisp values for our new parameter trust T. Plotting those values according to Gaussian membership function equation we have got the figure… for Trust T parameter. It can also be classified into five categories after finding out and plotting:-

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TABLE 3.

Class NameLow Very Low Average High Very High

RANGES OF OUTPUT TRUST

Trust Range Value 0%-20% 10%-40% 30%-70% 60-90% 80%-100%

Symbols VLT LT Avg.T HT VHT

Figure 4. Membership Functions for Output Trust

Here X-axis represents the trust values. 4.2.1.5 Defuzzification The input for the defuzzification process is a fuzzy set and the output of defuzzification process is a crisp value obtained by using some defuzzification method such as centroid, height and maximum. Among them, centroid defuzzification is used mostly

4.2.1.6 Applying Implication Method The prerequisite for applying implication to any fuzzy set is finding out rule’s weight We have found out the weights in figure 4 Membership functions output is de-fined as the weights for every rules. The input for the implication process is a single number given by the antecedent, and the output is a fuzzy set. Implication is implemented for each rule. Let, one of the rules is:“If certainty is high and average rating is aver-age, then trust is average.” Let, the value for certainty is C=0.7 and value for average rating is t = 3.0. Now, according to the implication method, we get the following output:-

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IJRIT International Journal Of Research In Information Technology, Volume 3, Issue 7, July 2015, Pg. 111-122

Figure 5. Implication for R14

4.2.1.7. Aggregate all Outputs Aggregation is the process by which the fuzzy sets that represent the outputs of each rule are combined into a single fuzzy set. Aggregation only occurs once for each output variable. It is the second last phase of defuzzification. The input of the aggregation process is the list of truncated output functions returned by the implication process for each rule. The output of the aggregation process is one fuzzy set for each output variable. In this proposed model, the fuzzy input set is certainty set and average rating set and the output fuzzy set is trust set.

4.2.1.8 Defuzzification Results Using defuzzification , we can get the defuzzified output. According to it, the defuzzified output is:’= = 4 3

5 . Advantages of fuzzy logic Fuzzy logic is not the only way to reason with am-biguou concepts but it seems to be the most apt to function approximation in control engineering. .- FI om the previous considerations, some of the most important advantages the use of fuzzy logic can en- tail to control system design are here detailed: 1. Flexible,

intuitive

knowledge

base design.. Control and supervision speak the same lan-guage.

2.Convenient user interface. Easier end-user interpretation when the final user is not a control engineer Easy computation,Widely available toolboxes and dedicated integrated circuits Learning. 3.Linear in parameter systems (in most cases) make possible least squares, dead- zone algorithms and other results from adaptive control.

learning

4.Validation. Consistency, redundancy and completeness can be checked in rule bases (knowl- edge acquisition supervision]. That could speed up automated learning and improve user interpretability Ambiguousness 5. Fuzzy logic is a "natural" way ,of expressing uncertain information research must be done in reasoning with incompleteness, i.e..., concluding different actions depending on the possibility or necessity of certain plant situations, Some tools for it are already available • Combine

regulation

algorithms

and

• Its conceptual model (granulation, (RBFN, local models)'

logic reasoning,

allowing for integrated

control schemes

soft com- mutation has been used in many other paradigms

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IJRIT International Journal Of Research In Information Technology, Volume 3, Issue 7, July 2015, Pg. 111-122

• FLC can incorporate a conventional design (PID, state feedback) and fine-tune it to CeI- tam plant nonlinearities due to universal ap- proximation capabilities

6. Conclusion In conclusion, there is a real challenge in the control of congestion in communication networks, especially the ones supporting video, voice and data applications simultaneously. Computational Intelligence techniques are expected to play a central role, especially in the large scale, geographically distributed network systems. Hybrids are also expected to supplement these techniques and prove useful, especially in optimising the overall network objectives. A novel traffic management scheme, called the IntelRate controller, has been proposed to manage the Internet congestion in order to assure the quality of service for different service applications. The controller is designed by paying attention to the disadvantages as well as the advantages of the existing congestion control protocols. As a distributed operation in networks, the IntelRate controller uses the instantaneous queue size alone to effectively throttle the source sending rate with max-min fairness. Unlike the existing explicit traffic control protocols that potentially suffer from performance problems or high router resource consumption due to the estimation of the network parameters, the IntelRate controller can overcome those fundamental deficiencies. To verify the effectiveness and superiority of the IntelRate controller, extensive experiments have been conducted in OPNET modeler. In addition to the feature of the FLC being able to intelligently tackle the nonlinearity of the traffic control systems, the success of the IntelRate controller is also attributed to the careful design of the fuzzy logic elements.

7. References [1] M. Welzl, Network Congestion Control: Managing Internet Traffic. John Wiley & Sons Ltd., 2005. [2] R. Jain, “Congestion control and traffic management in ATM networks: recent advances and a survey,” Computer Networks ISDN Syst., vol. 28, no. 13, pp. 1723–1738, Oct. 1996. [3] V. Jacobson, “Congestion avoidance and control,” in Proc. 1988 SIGCOMM, pp. 314–329. [4] V. Jacobson, “Modified TCP congestion avoidance algorithm,” Apr. 1990. [5] K. K. Ramakrishnan and S. Floyd, “Proposals to add explicit congestion notification (ECN) to IP,” RFC 2481, Jan. 1999. [6] D. Katabi, M. Handley, and C. Rohrs, “Congestion control for high bandwidth-delay product networks,” in Proc. 2002 SIGCOMM, pp. 89– 102. [7] S. H. Low, F. Paganini, J.Wang, et al., “Dynamics of TCP/AQM and a scalable control,” in Proc. 2002 IEEE INFOCOM, vol. 1, pp. 239–248. [8] S. Floyd, “High-speed TCP for large congestion windows,” RFC 3649, Dec. 2003. [9] W. Feng and S. Vanichpun, “Enabling compatibility between TCP Reno and TCP Vegas,” in Proc. 2003 Symp. Applications Internet, pp. 301– 308. [10] M. M. Hassani and R. Berangi, “An analytical model for evaluating utilization of TCP Reno,” in Proc. 2007 Int. Conf. Computer Syst. Technologies, p. 14-1-7. [11] N. Dukkipati, N. McKeown, and A. G. Fraser, “RCP-AC congestion control to make flows complete quickly in any environment,” in Proc. 2006 IEEE INFOCOM, pp. 1–5. [12] Y. Zhang, D. Leonard, and D. Loguinov, “JetMax: scalable max-min congestion control for high-speed heterogeneous networks,” in Proc. 2006 IEEE INFOCOM, pp. 1–13. [13] B. Wydrowski, L. Andrew, and M. Zukerman, “MaxNet: a congestion control architecture for scalable networks,” IEEE Commun. Lett., vol. 7, no. 10, pp. 511–513, Oct. 2003. [14] Y. Zhang and M. Ahmed, “A control theoretic analysis of XCP,” in Proc. 2005 IEEE INFOCOM, vol. 4, pp. 2831–2835. [15] Y. Zhang and T. R. Henderson, “An implementation and experimental study of the explicit control protocol (XCP),” in Proc. 2005 IEEE INFOCOM, vol. 2, pp. 1037–1048. [16] J. Pu and M. Hamdi, “Enhancements on router-assisted congestion control for wireless networks,” IEEE Trans. Wireless Commun., vol. 7, no. 6, pp. 2253–2260, June 2008. [17] F. Abrantes, J. Araujo, and M. Ricardo, “Explicit congestion control algorithms for time varying capapcity media,” IEEE Trans. Mobile Comput., vol. 10, no. 1, pp. 81–93, Jan. 2011. [18] L. Benmohamed and S. M. Meerkov, “Feedback control of congestion in packet switching networks: the case CH. Venkata Naryana , IJRIT-121

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of a single congested node,” IEEE/ACM Trans. Netw., vol. 1, no. 6, pp. 693–708, Dec. 1993. [19] Y. Hong and O. Yang, “Design of adaptive PI rate controller for best effort traffic in the Internet based on phase margin,” IEEE Trans. Parallel Distrib. Syst., vol. 18, no. 4, pp. 550–561, 2007. [20] W. Hu and G. Xiao, “Design of congestion control based on instantaneous queue size in the routers,” in Proc. 2009 IEEE GLOBECOM, pp. 1–6. [21] S. Chong, S. Lee, and S. Kang, “A simple, scalable, and stable explicit rate allocation algorithm for max-min flow control with minimum rate guarantee,” IEEE/ACM Trans. Netw., vol. 9, no. 3, pp. 322–335, June 2001. [22] Y. Hong and O. Yang, “An API-RCP design using pole placement technique,” in Proc. 2011 IEEE ICC, pp. 1– 5. [23] B. Ribeiro, T. Ye, and D. Towsley, “Resource-minimalist flow size histogram estimator,” in Proc. 2008 ACM SIGCOMM Conf. Internet Measurement, pp. 285–290. [24] Y. H. Long, T. K. Ho, and A. B. Rad, “An enhanced explicit rate algorithm for ABR traffic control in ATM networks,” Int. J. Commun. Syst, vol. 14, pp. 909–923, 2011. [25] L. Roberts, “Enhanced PRCA proportional rate control algorithm,” AFTM- R, Aug. 1994.

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