International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: [email protected], [email protected] Volume 2, Issue 2, March – April 2013 ISSN 2278-6856

CONGESTION CONTROL MECHANISMUSING FUZZY LOGIC Yogini Bazaz1, Sudesh kumar2 and Sanjay Anand3 1,3

Computer science & Engineering, Central University of Rajasthan,India 2

Computer Science, IGNTU, Amarkantak, (M.P.) India

Abstract: Multimedia transmission over the communication network and computer has gained importance from many years. Real time multimedia applications have come into existence like audio, video etc. So the congestion problem is also increasing .Congestion is really a complex problem to define. It arises when the users tries to access the same resource. Congestion is a complex problem. There are many types of techniques to control congestion but we want time sensitive, easier, fast, effective technique to control congestion. To control the problem of congestion many algorithms were designed but fuzzy logic is very easy, simple and effective way to solve the problem of congestion. For multimedia application RTCP is used with RTP and uses TCP for bidirectional client server connection. The rapid internet growth and the demand of usage of internet for time sensitive streaming applications is increasing the utilization and design of effective congestion control techniques. This paper takes the fuzzy logic to control congestion problem. It’s totally easy and faster and is based on human thinking. Fuzzy is based on the logic that takes the values between 0 and 1.It does not take the crisp values. Fuzzy logic toolbox is used in the matlab to make the effective fuzzy system. Congestion control has become an application for the fuzzy logic. In this paper we are proposing a mechanism to control congestion in streaming media applications by using fuzzy logic. Then a model has been generated by the fuzzy logic controller to control the congestion.

Keywords: Streaming media, Congestion, Congestion Control, Fuzzy Logic, Matlab

1. INTRODUCTION The internet stability is on risk if the video and voice traffic continues to increase.UDP is the most suited protocol for streaming media[9][22] but TCP also work good for maximum applications but still they have some problem[15]. More over TCP is a reliable protocol and faces the problem of congestion.UDP works well with streaming applications but is not reliable and also will not give the problem of congestion. Congestion is really a complex problem. It leads to the degradation of performance of network. Congestion arises when the user tries to use the same resource. Congestion control is the term used for controlling congestion[5][6]. So it is accepted that the network congestion control remains a critical issue and a high priority because of the growing demand, size and speed i.e. Bandwidth of the network .So for streaming media applications we are going to control congestion using Fuzzy logic. Although the algorithms [21] which are previously designed for Volume 2, Issue 2 March – April 2013

controlling congestion are really important and work really well but we need an easy, quick and effective mechanism for controlling congestion and that mechanism is fuzzy logic [8]. The concept of Fuzzy Logic (FL) was given by LotfiZadeh, a professor at the University of California at Berkley. It is a way of processing data by allowing partial set membership rather than crisp set membership or non-membership and it is presented as a control methodology. This set theory was never applied to control systems until the 70's due to having small-computer capability of that time. Professor Zadeh reasoned that people are capable of highly adaptive control and do not require precise, numerical information input. If controllers could be programmed to accept imprecise, noisy data they would be much more effective and perhaps easier to implement.

2. METHODOLOGY Before making a system we must first built and consider rules and define all these terms we plan for using and the adjectives that describe them. Fuzzy inference system is a method that interprets the values in input vector and based on some set of rules assigns values to the output vector [24]. Rules used for controlling congestion in our work for fuzzy are as follows:  If (available bandwidth is low) and (change rate is decreasing) then (send rate is low).  If (available bandwidth is low) and (change rate is decreasing) then (send rate is high).  If (TCP response is low) then (send rate is low).  If (available bandwidth is high) then (TCP response is high) then (send rate is high).  If (available bandwidth is high) then (send rate is high).  If (available bandwidth is low) then (send rate is low). There are three inputs in our system. The inputs used in our system are TCPresponse, change rate and available bandwidth. So based on that we are going to describe their membership functions. Membership function is a curve that defines how each point in input space is mapped to a membership value (or degree of membership) between 0 and 1.There are eleven types of membership functions but these membership functions come under four major sub categories Page 313

International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: [email protected], [email protected] Volume 2, Issue 2, March – April 2013 ISSN 2278-6856    

Piece wise linear function Sigmoid curve Gaussian distribution function Quadratic and cubic polynomial curves.

trapmf membership function comes under piece wise linear function This membership function curve is used for output i.e for send rate.this is known as triangular membership function.dsigmf and psigmf membership function comes under Sigmoid curve. This membership function curve is used for input i.e for available bandwidth.This are known as sigmonidal functions.gaussmf membership function comes under Gaussian Distribution curve. This membership function curve is used for input i.e for Tcp response.This are known as Gaussian distribution curves. pimf membership function comes under polynomial based curves This membership function curve is used for input i.e for Change Rate.These are known as Gaussian distribution curves.So the membership curves are created.these curves are used because of their advantages.Some curves are used because of having the advantage of simplicity like piece wise linear function.some are having the advantage of smoothness like gaussian curves.Fuzzy inference is the process of deciding the mapping..The process of fuzzy inference is the combination of membership functions,rules and logical operators.There are two types of fuzzy inference system :  Mamdani type fuzzy inference system  Sugeno type fuzzy inference system. The difference between these system is their output..Mamdani fuzzy inference system is the first commonly and properly used system.It was proposed by Ebrahim mamdani. Mamdani inference system gives the output mambership function to be fuzzy sets as compared to sugeno type system can be used to make any inference system in which the output membership functions are either linear or constant.In this work of congestion control we are using mamdani fuzzy inference system because we want the output fuzzy not linear not constant like in sugeno which gives linear or constant results.Fuzzy inference system is divided into five parts:     

Fuzzification fuzzy operator Implication Aggregation Defuzzification.

For fuzzification our inputs are available bandwidth which are set up to the range of 0-1.so 0 represents low bandwidth and 1 represents high bandwidth,then TCP response which are set up to the range of 0-1 where 0 represents low and 1 represents high,then change rate which are set up to the range of -1-1 where -1 represents decreasing and increasing and the output which are set up to the range of 0-1 where 0 represents low and 1 represents high.The operators used in our work are AND Volume 2, Issue 2 March – April 2013

method and OR method. In our work of congestion control we have used the methods AND and OR.For AND method we have used min and for OR method we have used max. Rules weighting is done by implication method .we are using weight 1 for the implication process.There are mainly three types of aggregation methods and these three types are Max (maximum),Probor (Probabilistic OR) and Sum (sumof each rules).In this work, we are using max aggregation method to do the aggregation .So that we can get the certain number of outputs.There are five types of defuzzification methods and these are Centroid, Bisector,Middle of max,Largest ofmax and Smallest of max.In this work, we are using centroid defuzzification process which gives the centre of area of the curve. Some tools used to built fuzzy system are:  Fuzzy inference system(fis) editor: Here FIS editor uses the input variables as available bandwidth,tcp response,change rate and output is send rate.  Membership function editor: Here in our work curves are made up of all the input varaiable and output variables.like available bandwidth,change rate,tcp response and send rate.  Rule editor: Here in our work we have made six rules while adding , deleting and putting rules.Also we have put operators to it (AND ,OR). Weights are also given in it.  Rule viewer: We have used six rules to seeits working in rule viewer.  Surface viewer: Here in our work we use the surface viewer to see the output surface.It shows how the output of one is dependent on inputs of two. For our work we are saving this fuzzy file as named send.fis. Whenever we open it, we open it with command. Fuzzy send.fis When FIS editor opens up we will name three input variables available bandwidth, Tcp response, change rate and output as send rate.We are using mamdani fuzzy inference system. For AND method we are using min, OR method we are using max, for implication method we are using min and aggregation we are using max and for defuzzification we are using centroid defuzzification method.Now it’s time to map curves for all the input and output and gives names to them and set their range.For input available bandwidth we are setting the range of 0 to 1 with sigmonidal curves where 0 represents low bandwidth and 1 represents high bandwidth.

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International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: [email protected], [email protected] Volume 2, Issue 2, March – April 2013 ISSN 2278-6856 For input change rate we are setting the range of 0 to 1 with polynomial curves with display range -1 to 1 where 1 represents and 1 represents decreasing change rate and increasing change rate.

Figure 4 For input send rate we are setting the range of -1 to1with piece wise linear function i.e. triangular curve. Figure 1

Figure 2 For input tcpresponse we are setting the range of 0 to 1 .curves used in it are Gaussian curves. Where 0 represents low and 1 represents high.

Figure 5

Figure 6. Figure3 Volume 2, Issue 2 March – April 2013

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International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: [email protected], [email protected] Volume 2, Issue 2, March – April 2013 ISSN 2278-6856 Here in our work we have made six rules which we can add , delete and putting rules.Also we have put operators to it (AND ,OR). Weights are also given in it like 1.Rule viewer helps to view the rules of the system.It shows how each rule behave in the system.It shows the output with aggregation done and lastly the defuzzification output is displayed with the vertical bold line.we have used six rules to see its working in rule viewer. Surface viewer helps to show the whole surface in micro form.It also helps to see the system in x,y,z axis.The two inputs selected are available bandwidth and tcp response and output as sendrate. We can see all the three in three domensionnal form..For smoothness we will give plot value.By default its 101.By clicking evaluate all the calculations will be done.For changing X axis and Y axis after the suface is in view,change the input field and press enter..If there will be more inputs then we use Ref. Input field.Now this whole FIS file is saved in fuzzy logic controller [3] block so that it can take the inputs like available BW, tcp response, change rate to produce one output send rate .Now the file which have been saved and put it in controller will give us the output in the form of model. This model will take all the inputs of the system and named as such inputs name like available bandwidth to input MF, then Tcp response and last change rate and output as send rate. Rules are applied to the entire rule column which produces the aggregation method of maximum. Aggregation gives the range of outputs but the defuzzification takes the centroid defuzzification and produces one output and gives one output. The whole mechanism is used to control congestion. This fuzzy system helps to control congestion .in faster, easier and better way.

Figure 7 Through surface viewer we can now see our system in micro form which was not possible with the help of rule Volume 2, Issue 2 March – April 2013

viewer.This evaluation is done by filling all the X input,Y input and Z output.after that whole sysem evaluation is done. The inputs which we select for suface viewing are available bandwidth,tcpresponse and output as sendrate.With the help of menu’s like file ,edit and view we can save edit and view our system rule by rule.This surface viewing grid helps us to see our system in actual form and we can also command its working.After surface view we save this whole file. (send.fis)

Figure 8- Model for Controlling Congestion

3. CONCLUSION Fuzzy inference system is the mapping of input space to output space. Mamdani fuzzy inference system is used to control congestion. Here the Fuzzy Inference system file send.fis is created in which there are input values like available bandwidth, Tcp response and change rate but this whole system is based on six rules. This fuzzy system consists of inputs, membership function curves which describe the curves of inputs and also the output. We can view all our system through Rule viewer and Surface viewer .Rule viewer helps us to see the whole functioning of the system and the Surface viewer helps us to see the whole system in micro form where change rate is set to zero because computer system can’t show the surface in more than X, Y and Z axis. Then a model is generated by using fuzzy logic controller and send.fis file which takes all inputs, rules, operators, implication method(min) ,aggregation method(max) and gives the output send rate .We can also view this system file in code form which gives the summary of the send.fis file. If we want this system should work in live then we need to generate code in C language and then call the controller so that it can run in any other environment or in live environment. We can also precede this test not only for controlling congestion but also for Tcp Page 316

International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: [email protected], [email protected] Volume 2, Issue 2, March – April 2013 ISSN 2278-6856 friendliness, Tcp smoothness and Multimedia’s Quality Of Service (QOS) [6][10][18] like delay, jitter and losses. More over fuzzy inference system remains an active research area. More applications can be designed for fuzzy logic like DC motor; water level control etc and also great improvements can be done for the previous applications which already exist.

REFERENCES: [1.] Jon C. Ervin, Sema E. Alptekin, “Fuzzy Logic Control of a Model Airplane”, International Conference on Systems, Man and Cybernetics, IEEE 1998. [2.] Chrysostomou, A. Pitsillides, G. Hadjipollas, M. Polycarpou, A. Sekercioglu, “Fuzzy Logic Control for Active Queue Management in TCP/IP networks”, 12th IEEE Mediterranean Conference on Control and Automation (MED’04), Kusadasi, Aydin, Turkey, 69 June 2004. [3.] PuminDuangmanee and PeerapongUttansakul, “Implementation of Real time video streaming with fuzzy logic controller”, International Conference on Wireless and Signal processing, IEEE 2010. [4.] SomchaiLekcharoenand Chun Che Fung, “An Adaptive Fuzzy Control Traffic Shaping Scheme over Wireless Networks”, Proceedings of Asia-Pacific Conference on Communications, Pg no 177-180, 2007. [5.] S. Mercy Shalinie, G. Preetha, S. Dina Nidhya, B.S. Kiruthika Devi, “ Fuzzy Adaptive Tuning of Router Buffers for Congestion Control” International Journal of Advancements in Technology ,Vol 1, Pages 85-94, June 2010. [6.] Runtong Zhang, Xiaomin Zhu, “Congestion Control Using Fuzzy Logic in QoS Networks” IEEE. vol , 2006 . [7.] ChuenChien lee, “Fuzzy-Logic in Control systems: Fuzzy logic controller Part –I”, IEEE Transactions on systems and cybernetics, vol.20, no 2 1990. [8.] Fuzzy Logic Toolbox User’s Guide for using MATLAB. [9.] Franc Kozamernik “Media streaming over the internet an overview of delivery technologies” EBU Technical Department, page no 1- 15, oct 2002. [10.] Yazeed A. Al-Sbou,“Fuzzy Logic Estimation System of Quality of Service for Multimedia Transmission”, International Journal of QoS Issues in Networking, Vol. 1, No. 1, December 2010. [11.] VeselinRakocevic“congestion control for multimedia applications in the wireless internet”. [12.] Rahul Malhotra, TejbeerKaur, “Dc Motor Control Using Fuzzy Logic Controller”, International Journal Of Advance Engineering Sciences And Technologies, Vol No. 8, Issue No. 2, 291 – 296, 2011. [13.] Dapeng Wu, Yiwei Thomas Hou, Wenwu Zhu, YaQin Zhang, Jon M. Peha, “Streaming Video over the Volume 2, Issue 2 March – April 2013

Internet: Approaches and Directions” IEEE Transactions on Circuits and Systems for Video Technology, Vol. 11, No. 1, 2001. [14.] Mani Zarei, Amir MasoudRahmani, RaziehFarazkish, Sara Zahirnia ,“Fairness Congestion Control for a distrustful wireless sensor network using Fuzzy logic” 10th International Conference on Hybrid Intelligent Systems , 2010. [15.] Y.-G. Kim, J. W. Kim, and C.-C. J. Kuo, “TCPfriendly Internet video with smooth and fast rate adaptation and network-aware error control,”IEEE Trans. Circuits Syst. Video Technol., vol. 14, page no. 256–268, Feb. 2004 [16.] S. Mercy Shalinie, G. Preetha, S. Dina Nidhya, B.S. Kiruthika Devi, “Fuzzy Adaptive Tuning of Router Buffers for Congestion Control” International Journal of Advancements in Technology ,Vol 1, Pages 85-94, June 2010. [17.] Emmanuel Jammeh, Martin Fleury, and Mohammed Ghanbari, “Delay-based Congestion Avoidance for Video Communication with Fuzzy Logic Control”, Packet Video, IEEE 2007. [18.] Ditze. “Resource Adaptation for Mobile AV Devices in the UPnP QoS Architecture. In Journal of MobileMultimedia, vol4 ,2006. [19.] Michael Ditze, Matthias Grawinkel, “Fuzzy Logic Based Admission Control for Multimedia Streams in the UPnP QoS [20.] hitecture” 21st International Conference on Advanced Information Networking and Applications Workshops IEEE ,2007 [21.] Danny H. K. Tsang, BrahimBensaou, Shirley T. C. Lam, “Fuzzy-Based Rate Control for Real-Time MPEG Video”, IEEE Transactions no. 4 on Fuzzy system, VOL. 6 1998 [22.] BehrouzSafaiezadeh, Amir MasoudRahmani and EbrahimMahdipour, “A New Fuzzy Congestion Control Algorithm in Computer Networks”, IEEE International Conference on Future Computer and Communication”, page no 314-317 April 03-05, 2009 [23.] Michael, “Streaming Media Demystified” McGraw Hill, 2002. http://books.mcgraw-hill.com/cgibin/pbg/007138877X? Ms. Yogini Bazaz received M.Tech. degree in Computer Science & Engineering with specialization in Information Security from Central University of Rajasthan, Bandarsindri, India in 2012. She has been lecturing at the SLITE University and current research area is Information security and Fuzzy logic. Mr.Sudesh Kumar received the M.Sc. degree in mathematics from Bikaner University, India in 2005, and ME degree in Computer Science & Engineering from Page 317

International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: [email protected], [email protected] Volume 2, Issue 2, March – April 2013 ISSN 2278-6856 Thapar University, Patiala, India in 2009. Presently, he is working as Assistant Professor in IGNTU, Amarkantak and his current research area is Number Theory application for cryptography, Advance DSA and Fuzzy Logic & Application. Mr. Sanjay Kumar Anand received the M.Sc. degree in IT and M.Tech. Degree in Information Technology from C-DAC, Noida, India in 2008. Presently, he is working as an Assistant Professor in Central University Rajasthan, India and his area of interest is NLP and Data Mining.

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