A STUDY ON THE PERFORMANCE OF TRANSPORT PROTOCOLS COMBINING EXPLICIT ROUTER FEEDBACK WITH WINDOW CONTROL ALGORITHMS AARTHI HARNA TRIVESALOOR NARAYANAN

Transcription

1 A STUDY ON THE PERFORMANCE OF TRANSPORT PROTOCOLS COMBINING EXPLICIT ROUTER FEEDBACK WITH WINDOW CONTROL ALGORITHMS By AARTHI HARNA TRIVESALOOR NARAYANAN Master of Scence n Computer Scence Oklahoma State Unversty Stllwater, OK 2005 Submtted to the Faculty of the Graduate College of the Oklahoma State Unversty n partal fulfllment of the requrements for the Degree of MASTER OF SCIENCE December, 2005

2 A STUDY ON THE PERFORMANCE OF TRANSPORT PROTOCOLS COMBINING EXPLICIT ROUTER FEEDBACK WITH WINDOW CONTROL ALGORITHMS Thess Approved: Dr. V Sarangan Thess Advsor Dr. J Thomas Dr. N Park A. Gordon Emsle Dean of the Graduate College

3 ACKNOWLEDGEMENTS My two and a half years stay at Oklahoma State Unversty has been wrought wth many changes n my lfe. Through t all, I am lucky to have the blessngs of The Almghty and my parents wthout whch nothng would have been possble. I consder myself fortunate enough to have worked under my Advsor Dr. Venkatesh Sarangan whose able gudance; ntellgent supervson and understandng helped me fnsh my thess. I would also lke to extend my sncere apprecaton to my commttee members Dr. Johnson Thomas and Dr. Nophll Park whose encouragement and support proved nvaluable. I would also lke to thank my relatves and other well wshers back home for ther love and support. I am fortunate to have a lot of good frends. I would lke to thank them for ther mmense help, understandng and support they provded through all my good tmes and bad. I would lke to thank my supervsors at work especally, Dr. Earl Mtchell, Dr. Yousf Sherf and Krst Elrch for belevng n me. Fnally, I would lke to thank the Computer Scence Department for supportng me durng my two and a half years of study.

4 Chapter TABLE OF CONTENTS Page I: INTRODUCTION...1 WINDOW CONTROL ALGORITHMS...3 Bnomal Algorthms...4 SQRT Bnomal Algorthm... 6 Inverse Increase Addtve Decrease Bnomal Algorthm... 6 Multplcatve Increase/ Multplcatve Decrease Algorthm (MIMD)... 7 Bnomal Algorthms Convergence to Farness... 7 Square Increase/ Multplcatve Decrease Algorthm (SIMD)...9 SIMD s Convergence to Farness and Effcency SIMD s Convergence Speed EXPLICIT CONGESTION CONTROL: EXPLICIT ROUTER FEEDBACK...14 II: BACKGROUND AND RELATED WORK...17 INTERNET CONGESTION CONTROL...17 TRANSMISSION CONTROL PROTOCOL (TCP)...18 III: METHODOLOGY...20 XCP ROUTER USING DIFFERENT WINDOW CONTROL ALGORITHMS...21 Effcency Controller (EC)...24 Farness Controller wth SIMD...26 Farness Controller wth Bnomal Algorthms...31 Farness Controller usng MIMD...36 IV: FINDINGS...39 SIMULATION OF XCP USING NS GRAPHS...41 XCP-AIMD...41 XCP-modfed SIMD...45 XCP wth Bnomal Algorthms...49 XCP wth MIMD...54 V: FUTURE WORK AND CONCLUSION...55 FUTURE WORK...55 CONCLUSION...57 REFERENCES...58 v

5 LIST OF TABLES Table Page TABLE I TABLE OF CONTROL EQUATIONS [2]...5 TABLE II COMPARISON OF DIFFERENT WINDOW CONTROL ALGORITHMS...11 v

6 LIST OF FIGURES Fgure Page FIGURE 1 SAMPLE PATH SHOWING CONVERGENCE TO FAIRNESS FOR A BINOMIAL ALGORITHM [2]...8 FIGURE 2 CONVERGENCE TO FAIRNESS [4]...12 FIGURE 3 CONVERGENCE SPEED [4]...14 FIGURE 4 EXPLICIT ROUTER FEEDBACK [3]...15 FIGURE 5 THE CONGESTION HEADER[4]...22 FIGURE 6 A SIMPLE XCP TOPOLOGY INVOLVING A BOTTLENECK...40 FIGURE 7 COMPARISON OF XCP-AIMD CONGESTION WINDOW WITH TCP FLOW...41 FIGURE 8 COMPARISON OF XCP-AIMD SEQUENCE NUMBER WITH TCP FLOW...42 FIGURE 9 COMPARISON OF XCP-AIMD THROUGHPUT...43 FIGURE 10 XCP-AIMD UTILIZATION...44 FIGURE 11 COMPARISON OF XCP- FOURTH ROOT SIMD CONGESTION WINDOW WITH TCP FLOW...45 FIGURE 12 COMPARISON OF XCP- FOURTH ROOT SIMD SEQUENCE NUMBER WITH TCP FLOW...46 FIGURE 13 COMPARISON OF XCP-FOURTH ROOT SIMD THROUGHPUT...47 FIGURE 14 XCP-FOURTH ROOT SIMD UTILIZATION...48 FIGURE 15 CWND COMPARISON FOR XCP-AIMD(300 SEC)...49 FIGURE 16 CWND COMPARISON FOR XCP-FOURTH ROOT SIMD(300 SEC)...49 FIGURE 17 SEQUENCE NUMBER COMPARISON FOR XCP-AIMD(300 SEC)...49 FIGURE 18 SEQUENCE NUMBER COMPARISON FOR XCP-FOURTH ROOT SIMD(300 SEC)...49 FIGURE 19 THROUGHPUT COMPARISON OF XCP-AIMD(300 SEC)...50 FIGURE 20 THROUGHPUT COMPARISON OF XCP-FOURTH ROOT SIMD(300 SEC)...50 FIGURE 21 UTILIZATION OF XCP-AIMD(300 SEC)...50 v

7 FIGURE 22 UTILIZATION OF XCP-FOURTH ROOT SIMD(300 SEC)...50 FIGURE 23 CWND COMPARISON OF XCP-SQRT...52 FIGURE 24 CWND COMPARISON OF XCP-IIAD...52 FIGURE 25 SEQUENCE NUMBER COMPARISON OF XCP-SQRT...52 FIGURE 26 SEQUENCE NUMBER COMPARISON OF XCP-IIAD...52 FIGURE 27 THROUGHPUT COMPARISON OF XCP-SQRT...53 FIGURE 28 THROUGHPUT COMPARISON OF XCP-IIAD...53 FIGURE 29 UTILIZATION OF XCP-SQRT...53 FIGURE 30 UTILIZATION OF XCP-IIAD...53 FIGURE 31 CWND COMPARISON OF XCP-MIMD...54 FIGURE 32 SEQUENCE NUMBER COMPARISON OF XCP-MIMD...54 FIGURE 33 THROUGHPUT COMPARISON OF XCP-MIMD...54 FIGURE 34 UTILIZATION OF XCP-MIMD...54 v

8 I: INTRODUCTION The Transfer control protocol (TCP) s a connecton-orented, relable, byte stream, endto-end transport layer protocol. TCP provdes an nterface between a network layer and the applcaton layer above. Durng data transfer, TCP senders and recevers can alter the flow of data. Ths s called flow control, congeston control and/or congeston avodance. TCP s a complex and an evolvng protocol. For the last two decades, TCP [16] has been the most wdely used protocol on the Internet. Durng these two decades, the network has evolved drastcally wth changes n power, network speeds and storage capacty. Over the years, multple enhancements have been proposed to allevate the varous problems faced by TCP lke Random Early Detecton (RED) [5,6], Random Early Markng (REM) [7], Fast Recovery [8], Slow Start [8], Fast Retransmt [8], Forward Acknowledgement (FACK) [9,10], Selectve Acknowledgement (SACK) [11], and Hgh-speed TCP [12] beng some of them. Although sgnfcant mprovements have been made to t over the years, due to ncrease n the use of TCP n the Internet for varous applcatons, a need for better methods to avod undue congeston has become a necessty. TCP uses packet drops as a way of detectng congeston and thereby lmtng ts sendng rate. Ths mplct sgnal gves very lttle nformaton on the type of congeston n the network. Thus, even f the congeston level s qute low, rrespectve of how many packets are dropped, the wndow sze s decreased drastcally. Such a bnary nature of 1

9 feedback restrcts the utlzaton of hgh-bandwdth lnks. Another lmtaton of basng the control laws on packet drops s that apart from not gvng any feedback, but t s also not possble to detect the packet loss quckly ether. As tmeout s generally equal to or greater than the RTT of the network, TCP wll use longer tme to detect a packet loss, especally n hgh delay networks where the RTT s hgh. Ths process lmts bandwdth utlzaton and the problem only worsens wth ncreased bandwdth and delay. TCP typcally uses a sldng wndow protocol that provdes handlng durng both tmeouts and retransmssons. TCP make use of sldng wndow algorthms as a part of flow control to relably handle loss, mnmze errors, manage congeston and perform effcently n hgh-speed envronments. SWP (Sldng Wndow Protocol) s a connectonless protocol. It allows data to be sent n one drecton between a par of protocol enttes, subject to a maxmum number of unacknowledged messages. If SWP s operated wth a wndow sze of 1, t s equvalent to the Alternatng Bt Protocol. Both hosts use a wndow for each connecton. A congeston wndow n TCP s not only determned by the recever but also by the congeston n the network. The sender has the recever s advertsed wndow as well as the congeston wndow sze. The actual wndow sze would be the mnmum of these two. Actual wndow sze= mnmum (recever wndow sze, congeston wndow sze) Durng steady-state, TCP uses Congeston Avodance algorthms to lnearly ncrease the value of the congeston wndow (w). The standard Addtve Increase and Multplcatve Decrease wndow algorthm (AIMD) [16] s one used wdely by TCP. The control law for AIMD algorthm s gven n the equaton 1.1 below from [16]. 2

10 Increase: w(t+ rtt) = w(t) + α α>0 (1.1) Decrease: w(t) + δ=w(t) β w(t) 0<β<1 where, w(t) s the wndow sze at tme t δ s the tme to detect packet loss snce the last update. The Increase rule ncreases the congeston wndow sze by one when a postve acknowledgement s receved and the Decrease rule roughly halves the wndow sze when congeston occurs. The dsadvantage of the AIMD control law s that t prevents TCP from quckly grabbng the avalable bandwdth as t only allows TCP to ncrease ts congeston wndow by one packet every round trp tme. When a packet drop occurs, TCP wll reduce ts congeston wndow multplcatvely and from that wndow sze ncrease addtvely by sendng one addtonal packet every RTT. Thus, wth networks havng hgh delay, TCP mght take a long tme to grab the spare bandwdth. These theoretcal dsadvantages of AIMD led to the need for better control laws. Bnomal algorthms [2], Multple Increase/ Multple Decrease (MIMD) [26] and Square Increase Multplcatve Decrease (SIMD) [4] are steps n that drecton. Wndow Control Algorthms The stablty n the Internet s b large drectly affected by the presence of well desgned flow control algorthms. However, huge dfferences n the Internet traffc condtons have led to the need to develop dfferent wndow control algorthms for caterng to the dfferent data needs present n the Internet today. Some of the wndow control algorthms are studed below and tested wth the explct Congeston Control protocol and ther performance s evaluated. 3

11 Bnomal Algorthms The ncreased dversty of Internet applcaton requrements has spurred the need for transport protocols wth flexble transmsson controls. Hence, a new class of non-lnear congeston control algorthms called Bnomal Algorthms [2] was ntroduced for Internet transport protocols and applcatons. These classes of algorthms amed at generalzng the famlar class of lnear control algorthms of whch AIMD s one example. Thus, the control equaton n (1) can be generalzed as gven below n Equaton 1.2. Increase: w(t+ rtt) =w(t) + α/w(t) k α>0 (1.2) Decrease: w(t) + δ= w(t) β w(t) l 0<β<1 where w(t) s the wndow sze at tme t and δ s the tme to detect packet loss snce the last update. These control equatons are also called as Bnomal Congeston Control Algorthms [2]. These rules generalze the class of all lnear control algorthms and other control laws wth non-lnear controls. They are called Bnomal Algorthms because ther control expressons nvolve the addton of two algebrac terms wth dfferent exponents. For the Addtve Increase and Multplcatve Decrease algorthm, the value of k=0 and l=1. They generalze TCP-style addtve ncrease by ncreasng nversely proportonal to the power of k of the current wndow sze and TCP-style multplcatve decrease by decreasng proportonal to the power of l of the current wndow. Thus, there are nfnte numbers of deployable TCP-compatble bnomal algorthms that converge to farness under synchronzed-feedback assumpton. The famly of ncrease and decrease algorthms can be obtaned by changng the values of l and k as shown n the table below. 4

12 Control Law K L AIMD 0 1 MIMD -1 1 AIAD 0 0 MIAD -1 0 IIAD 1 0 SQRT ½ ½ Table I Table of Control Equatons [2] Bnomal algorthms were manly proposed because of ts use n applcatons such as Internet audo and vdeo that does not react well to drastc rate reductons. In [2], the k+l rule s adopted to show how bnomal algorthms are TCP-Compatble. The paper shows how the throughput λ of a flow n related to the packet loss-rate p as λ α S/(R p), where S s the packet-sze and R s the connecton s round-trp tme [2] and depcts how an algorthm s sad to be TCP-Compatble only f ts throughput λ α S/(R p) wth the same constant of proportonalty as for a TCP connecton wth the same packet sze and roundtrp tme. The k+l rule states that the bnomal algorthms are TCP-compatble only f k+l=1 and l<=1 for sutable α and β. Of ths wde (k, l) space, [2] have proposed two bnomal algorthms. 5

13 SQRT Bnomal Algorthm One of the nterestng TCP-Compatble algorthms n the (k,l) space s the Square Root algorthm (SQRT) algorthm. It s so called because both ts ncrease s nversely proportonal and the decrease s proportonal to the square root of the current wndow. Increase: w(t+ rtt) =w(t) + α/ w(t) α>0 (1.3) Decrease: w(t) + δ= w(t) β w(t) 0<β<1 where w(t) s the wndow sze at tme t and δ s the tme to detect packet loss snce the last update. SQRT s seen from [2] to be less oscllatory and have a faster convergence to farness than TCP for smlar network condtons. Inverse Increase Addtve Decrease Bnomal Algorthm The Inverse ncrease and Addtve Decrease algorthm (IIAD), where the ncrease s nversely proportonal to the current wndow and ts decrease s addtve s another nterestng algorthm n the famly of bnomal equatons. Its ncrease/decrease equatons are shown below. Increase: w(t+ rtt) =w(t) + α/w(t) α>0 (1.4) Decrease: w(t) + δ= w(t) β 0<β<1 where w(t) s the wndow sze at tme t and δ s the tme to detect packet loss snce the last update. The IIAD s seen to be convergng to farness under synchronzed farness assumpton f k+l=1, at least k or l are postve and the throughput of a bnomal algorthm λ s gven as 6

14 λ α 1/ p 1/k+l+1 where λ s the throughput of the flow and p s the loss rate t encounters. Ths k+l rule represents the fundamental tradeoffs between probng aggressveness and congeston responsveness. Although the bnomal algorthms have good smoothness and almost negatve oscllatons, they are much slower n respondng to bandwdth changes and hence are less aggressve n probng for extra bandwdth. Multplcatve Increase/ Multplcatve Decrease Algorthm (MIMD) The Multplcatve Increase/ Multplcatve Decrease algorthm s another algorthm that can be obtaned from the generalzed bnomal equaton shown above by havng k=-1 and l=1. In ths algorthm, the wndow sze n ncreased upon postve acknowledgement proportonal to the current wndow sze and smlarly, the wndow sze s also decrease upon packet drops to a value proportonal the current wndow sze. It has the followng control law: Increase: w(t+ rtt) =w(t) + α*w(t) α>0 (1.5) Decrease: w(t) + δ= w(t) β* w(t) 0<β<1 The MIMD control law s used n the slow start phase of the TCP wndow control. The MIMD s seen to be more stable than the other bnomal control laws lke AIAD and MIAD. MIMD s also seen to be more aggressve than other control laws. Bnomal Algorthms Convergence to Farness The farness of the bnomal congeston control algorthms were studed by [2]. A network of two sources (x1,x2) that mplement the same bnomal algorthm wth k,l 0 7

15 s shown by [2]. It s seen that these converge to a far and effcent operatng pont (x1=x2=1/2), provded that (k+l>0). It s also assumed that the network provdes synchronzed feedback about the congeston to all the sources. It s supposed that f x1<x2, then the area above the x2-x1 equ-farness lne s seen. Now a wndow ncrease s plotted for these two sources and compared wth a wndow ncrease of a AIMD algorthm where k has a value of 0. Fgure 1 Sample path showng convergence to farness for a bnomal algorthm [2] Now, wth wndow reducton, when l=0 (addtve decrease), the wndow reducton occurs along the 45 -lne (DE) whch actually worsens the farness [2]. When l=1, the wndow decrease s multplcatve and therefore the farness remans unchanged. Now, for the bnomal algorthms whch l value les between 0 and 1, the decrease occurs as a curve as shown n the Fgure [1]. Thus, ths ntersects the maxmum-utlzaton lne at a pont whch s closer to the far-allocaton pont that s present at the ntersecton of the two lnes that the other wndow algorthms. Another wndow control algorthm that has been proposed s the Square Increase/ Multplcatve Decrease Algorthm (SIMD). 8

16 Square Increase/ Multplcatve Decrease Algorthm (SIMD) The author n [4] ntroduced another wndow control mechansm that s qute dfferent from the exstng laws and whch shows a dstnct mprovement over the standard control laws. Square Increase and Multplcatve Decrease (SIMD) algorthm s one such algorthm that uses hstory nformaton n addton to current wndow sze n the farness controller of XCP. It has been shown n [4] that SIMD has allowed for a more progressve expanson of the wndow based algorthms wth just a smple addton to the control laws. SIMD s shown n [4] to be fundamentally dfferent from AIMD and Bnomal algorthms as SIMD uses as hstory nformaton, the wndow sze at the tme of detectng last loss whch s helpful n mprovng transent behavor. Also, [4] has shown how SIMD, beng TCP- frendly, has shown hgh smoothness, aggressveness and responsveness. We say that the Bnomal and other control mechansms are memory-less because ther control laws only consders the current wndow sze w(t) whle the SIMD control laws consder the sze of the wndow after the last decrease w(0). The control laws of SIMD are as gven below. Increase : w( t + rtt) = w( t) + α w( t) w0 ; α > 0 Decrease : w( t + δ ) = w( t) βw( t);0 < β < 1 (1.6) Unlke AIMD, n the ncrease law, SIMD consders both the sze of the wndow at present and also the wndow sze just before congeston,.e. reducton n wndow sze. In the congeston control Equaton 3 gven above, the SIMD scheme takes n the wndow sze before the last congeston epoch as w max and w 0 s taken as the wndow sze after the last decrease. The value of α SIMD too s not a constant as n other control schemes but s a factor of w max whch keeps changng after each congeston epoch. A congeston epoch s 9

17 defned n [4] as a sequence of wndow ncrements followed by one wndow decrement. Hence, the sequence of wndow ncrements s the value of w max and the value of the wndow sze rght after the decrement s taken as w 0. α, unlke AIMD and other memoryless control equatons, does not reman constant but rather vares wth the value of w max. The value of α SIMD has been gves n [4] as: α SIMD 3 β 3β = fβ << 1, (1.7) 2β (1 ) 2w 2wmax max 3 The dfferent wndow control algorthms were then compared wth a few general parameters to check ther performance based on certan crtera namely Aggressveness, Responsveness, Farness, Smoothness, Lnk Utlzaton and Multple Connectons. On comparng AIMD, the two Bnomal Algorthms namely SQRT and IIAD and SIMD, t s seen that n a TCP-lke stuaton, f these algorthms are plugged n nstead of TCP s AIMD that exst currently, then SIMD s seen to perform better especally n terms of farness and aggressveness. PROPERTIES AIMD [1] BINOMIAL [2] SIMD [4] Aggressveness AIMD s not very aggressve n probng for extra bandwdth as wndow ncrease s lnear. Least Aggressve n probng for extra bandwdth as wndow ncrease s sub-lnear. Most aggressve as wndow ncrease s super-lnear. Grows aggressve wth tme. Responsveness Upon congeston, AIMD Least Responsve as when Smlar to AIMD, wndow reduces wndow sze l=0, wndow decreases decrease s multplcatve. multplcatvely. only by a constant. 10

18 Farness AIMD has good convergence to farness. IIAD converges very slowly to farness when compared to AIMD and SIMD. SIMD converges faster and closer to the farness lne than AIMD or the bnomal algorthms. Smoothness Least Smooth. AIMD Good Smoothness. Hgh Smoothness. has notceable amount of Bnomal Algorthms have SIMD can provde hgh oscllatons even when neglgble oscllatons but smoothness n steady state. the loss rate s constant. are slower to respond to bandwdth changes. Lnk Utlzaton AIMD has average lnk Bnomal Algorthms have SIMD has good lnk utlzaton but does not good lnk utlzaton and utlzaton and allows two grab the spare bandwdth show hgher long term competent flows to quckly fast. throughput than AIMD. and smoothly transt to steady state. Multple Connectons AIMD s far to other IIAD s far to other IIAD SIMD s also far to other AIMD connectons n and also to TCP connect - competng flows n the the network. ons except at hgh loss network but at hgh loss rates, where TCP gans. rates, SIMD gans. Table II Comparson of Dfferent Wndow Control Algorthms As shown n Table 1.1, SIMD s superor to AIMD because of hgh smoothness n steady state and faster convergence to farness compared to AIMD. It s also more aggressve and more responsve especally when the network condtons change frequently and drastcally. The hstory nformaton used by SIMD s a good ndcator for the congeston 11

19 n the network at present and t can be used to predct the congeston that s gong to occur n the network n the future. SIMD s Convergence to Farness and Effcency SIMD s shown to have better convergence behavor than the memory-less AIMD n [4]. In [4], the authors use the vector space used n [15] to show how multple users wth synchronzed feedbacks can converge to farness when usng the SIMD control scheme. Frst, they llustrate the convergence to farness for two users shown on the vector-space as x 1 and x 2. The two-user resource allocaton s represented by a pont X(x 1, x 2 ), where x s the resource allocaton for the th user, =1,2. [4] defnes the farness ndex as x max( x 1 2 x, x 2 1 ). The lne x 1 = x 2 s the farness lne and the lne x 1 + x 2 =1 s the effcency lne. Thus, every control scheme would try to brng the system to an ntersecton between these two lnes. Fgure 2 Convergence to Farness [4] 12

20 A comparson between SIMD and AIMD s shown above where each of the congeston wndows are analyzed on ther ncrease laws. The multplcatve decrease law does not affect the farness. If the farness ndex of ether law s closer to unty, then the resource allocaton s sad to be farer. It s seen from Fg. 2 that as the system evolves, wth ncreases and decreases, the SIMD trajectory moves much closer to the farness lne as compared to the AIMD lne for the same two-users. As the system evolves, both the algorthms brng the resource allocaton pont towards farness but the SIMD outperforms AIMD n that t s able to converge to farness faster than AIMD and then oscllate around the effcency lne, mantanng the farness whle t takes AIMD longer tme to converge to farness. SIMD s Convergence Speed In [4], SIMD s convergence speed s analyzed. A case of l 1 = 1 k + 1 s chosen, satsfed by the Square Increase, Multplcatve Decrease (SIMD) algorthm whch has values of k=-0.5 and l=1. [4] shows ntutvely as well as analytcally how SIMD converges more quckly to farness than AIMD. X(x 1, x 2 ) s consdered to be underutlzed wth x 1 + x 2 <1 and x 1 < x 2. The pont of ntersecton of the effcency lne and the 1+ x 1 x2 1 x1 + x2 trajectory for AIMD s shown to be (, ) 2 2 and that of SIMD s shown x2 x1 to be ( x1 x2 +, x2 x1 + ) x + x x + x Then, the authors of [4] compare then compare the farness ndex of these two schemes, and show that ntutvely, SIMD reaches a more far ntersecton when x 1 + x 2 >1/3. 13

21 Fgure 3 Convergence Speed [4] The Fgure 3 from [4] shows that three categores have been plotted; AIMD, l>1/(k+1)-1 and l=1/(k+1)-1. The thrd lne, l=1/(k+1)-1.e. l=1 lne depcts the SIMD control law. As the area (1) where x 1 + x 2 >1/3, s much larger than area (2), t can be seen ntutvely that SIMD converges much faster than AIMD and other control laws that fall nto the l>1/ (k+1)-1 category. explct Congeston control: Explct Router Feedback Another qute dfferent approach to avod congeston was also proposed. [3] Introduces XCP: explct Congeston Control, whch on the other hand, uses a precse congeston control feedback where the source s explctly nformed when congeston occurs. [3] gves a new archtecture for Internet congeston control that decouples the control of congeston from the bandwdth allocaton polcy. It alters the wndow sze based on the level of congeston n the network and not just because of a smple packt loss as an ndcaton of congeston. The wndow equatons of XCP are gven below n Equaton 4. Increase: w(t+ rtt) = w(t) + α(t) α>0 (1.8) Decrease: w(t) + δ= w(t) β(t)*w(t) 0<β<1 14

22 Where, α(t) and β(t): feedback parameters that change when the congeston level of the network vares. Here, every packet sent s gven a congeston header that gves suffcent nformaton to the routers along the path about the connecton. The method gven n [3] has been shown to be hghly effcent due to ts marked dfference from the TCP s standard AIMD. AIMD algorthms use a black and whte feedback mechansm as there are only two states that are ndcated.e. postve acknowledgement or congeston. Thus, TCP s unable to dstngush whether the congeston s from the losses or the lnk errors n the network. Hence, unnecessary drops result due to lnk errors. XCP on the other hand, can explctly gauge the level of congeston n the network. It s used effcently n hgh bandwdthdelay product envronments as t can correctly estmate how much spare bandwdth s avalable and the wndow sze can ncrease accordngly. Fgure 4 Explct Router Feedback [3] 15

23 In XCP, the routers along the path from the sender and the recever play an mportant role by computng the per-packet feedback and estmatng the postve and negatve feedback n a control nterval. Here, t uses two controllers called the Effcency Controller (EC) and the Farness Controller (FC) at each router. The farness controller uses the control equatons to compute the total feedback at each router. In [3] the standard Addtve Increase and Multplcatve Decrease (AIMD) control law s used to dstrbute ths feedback farly among all flows. Here, ths document proposes to use The Square Increase and Multplcatve Decrease (SIMD) algorthm proposed n [4] n the farness controller (FC) of the XCP. It can be shown through equatons and wth ns-smulatons that usng XCP coupled wth SIMD would produce a far more superor means to detect and avod congeston. 16

24 II: Background and Related Work Ths chapter gves a bref overvew on the general pcture of the Internet Congeston Control and also gves some of the prevous work that has already been done n congeston control and the relevance of those n ths thess. Internet Congeston Control Wth the ntroducton of fber networks and Ggabt Ethernets, the Internet congeston as we know of has changed. There s now a deluge of data that s beng sent from dfferent parts of the world. Thus, the standard TCP/IP protocol that was desgned when the network demands were qute dfferent from what s present today; t does not reman very competent for the current network condtons. The control of Internet congeston s done wth two dfferent enttes workng and co-operatng together. The end-systems (senders and the recevers) are the end-hosts that manage the data. But, data along the network path s sent from one host to another wth the ad of the routers present n the network. Tradtonally, the responsblty of the routers has been lmted forwardng the packets along the network path and f the traffc s too great and t s unable to handle that many packets at a tme, then the packets are dropped. The senders have a congeston control protocol that montors the packet drop and when a packet drop occurs, the sender takes that as a sgn of congeston n the network and reacts to t by decreasng the sendng rate (decreasng the sendng wndow sze). In the current Internet, predomnantly ths type of 17

25 congeston control exsts. Drop-Tal routers that drop packets upon ther buffer becomng too full and networks that run TCP protocol s very predomnant n today s network. The research communty s now slowly movng towards placng more control n the routers. Some such proposed changes are Random Early Detecton (RED) [22], Actve Vrtual Queue (AVQ)[23], Random Early Marker (REM)[24], ARED[25] etc. These are collectvely referred to as Actve Queue Management schemes (AQMs). Transmsson Control Protocol (TCP) The Transport Control Protocol (TCP) s the backbone of Internet today. The TCP s a set of rules that s used n tandem wth the Internet Protocol (IP) to send and receve data as packets between end hosts over the nternet. TCP s responsble for sendng each small unt of data that a message gets dvded nto from the sender to the recever. TCP s responsble for selectng the most relable, smallest and n general most effcent routng across the Internet. TCP s a connecton-orented protocol. Here, a connecton s establshed by means of Handshakng process where the sender ntates a communcaton and f the recever s ready to accept communcaton from the sender, t sends an acknowledgement. Once the acknowledgement s receved by the sender, t can then start sendng data across. Ths Handshakng process s very mportant as the two end hosts settle dfferent parameters of whch the recever s buffer sze s also one. Ths recever s buffer sze s set as the sender s maxmum wndow sze. Ths s done to ensure that the sender does not 18

26 overwhelm the recever wth more data than t can process because sometmes the sender s processng speed s greater than the recever s processng speed, then the recever s buffer sze becomes full and the recever cannot accept any more data packets from the sender. Ths s called Flow Control. TCP uses Sldng Wndows, a technque whch s also called as wndowng as a method to control the flow of data packets from the sender to the recever. As TCP s a relable protocol, t requres every packet to be acknowledged by the recevng host. The orgnal specfcaton of TCP ncluded the wndow based scheme of controllng the sender s flow as a means of governng the amount of data sent by the sender to the recever. The orgnal TCP dd not nclude congeston control schemes. Van Jacobson [16] begnnng n the year 1986, developed a congeston avodance scheme that have not become a part of the standard TCP mplementaton. These mechansms operate on the host systems and when congeston s encountered n the network, the TCP connectons back-off or the sender reduces the sendng rate to allevate the congeston. These are called the TCP congeston avodance algorthms. These provded the fx that prevented the Internet meltdown and ensures that the network does not collapse due to congeston. 19

27 III: METHODOLOGY The evolvng Internet has made t essental to look for better schemes to avod congeston n the network and to respond to congeston wth mnmal delays and maxmum utlzaton of the resources. Studes have shown that the current TCP wth AIMD wndow control scheme s not very sutable for the current Internet wth ts hgh-bandwdth-delay product envronments and ncreasng users and traffc. AIMD s not qute aggressve n probng for extra bandwdth and t also suffers from a low responsveness and poor convergence to farness. Also, the Bnomal algorthms proposed n [2], though t does have some mprovements over the basc AIMD algorthms, t does not respond well to sudden ncrease n congeston-responsveness and t s not very aggressve n probng for spare bandwdth due to tradeoffs n the value of k and l for throughput. It has been shown n [3] that regardless of the queung scheme; TCP becomes unstable and neffcent when the bandwdth-delay product ncreases. The XCP-SIMD couplng, on the other hand, has shown to be far more superor n all the aspects mentoned above although AIMD or bnomal algorthms may have some attrbutes that outdo SIMD n some cases. The XCP- SIMD couplng s a step towards explorng the space between memory-less control schemes and equaton-based schemes wth precse and explct congeston feedback as an ndcaton for current and future congeston. 20

28 XCP Router usng Dfferent Wndow Control Algorthms The XCP, whch generalzes the Explct Congeston Notfcaton Proposal [5], uses routers to nform senders about the degree of congeston n the network. It also decouples farness control from utlzaton control. Effcency nvolves montorng only the aggregate traffc s behavor whereas farness nvolves montorng the relatve throughput of the flows n the lnk. Utlzaton n XCP s done by usng the spare bandwdth and the feedback delay to adjust ts aggressveness n probng for bandwdth. Ths prevents oscllatons, provdes stablty and helps make effcent use of the network resources especally n hgh bandwdth-delay product envronments. Farness control s mantaned by takng away the extra bandwdth from those flows that utlze more than ther far share and allocatng them to other flows. Decouplng farness and utlzaton control opens up dfferent methods for servce dfferentaton usng schemes that provde desred bandwdth allocatons, yet are too aggressve or too weak for controllng congeston. Also, congeston losses and error losses n the lnks can then be easly dentfed. If modfcatons need to be made n any one of the controllers, t can be done wthout affectng the other controller. It makes desgn and analyss of these controllers smpler and effcent. In XCP, the router has both an Effcency Controller (EC) and a Farness Controller (FC). Senders mantan ther congeston wndow (w) and round trp tme (rtt) and send ths nformaton to the routers along the path by means of a congeston header placed n every packet. In the basc XCP framework, the senders mantan ther congeston wndows cwnd and the average round trp tme (rtt). The throughput s sent to each router from these two 21

29 values.e. cwnd/rtt. RTT s also sent n the congeston header to each of the routers. Routers montor the nput traffc rate to each of ther output queues. Now, the dfference between the lnk bandwdth and the nput rate of traffc wll provde the router wth a good pcture of the network performance. As each router along the path calculates ths dfference and updates the feedback feld present n the congeston header, then the feedback feld would contan the value of the most congested router n the network.e. t wll have the feedback from the bottleneck along the network path from the sender to the recever. H_cwnd ( set to sender s current cwnd) H_rtt ( set to sender s rtt estmate) H_feedback (set to the sender s desred ncrease value) Fgure 5 The congeston Header[4] Ths congeston header s used to communcate the flow s state to the routers and the feedback from the routers onto the recevers. Each tme a packet s sent; the sender sets the H_cwnd feld to ts current cwnd and sets the H_rtt feld to ts estmate. The routers along the path montor the nput traffc and after obtanng the dfference between the lnk bandwdth and ts nput traffc rate, each router modfes the congeston header accordng to the level of congeston to ndcate to the other flows sharng the lnk to ether ncrease or decrease ther congeston wndows. Ths feedback s then placed n the H_feedback feld. The routers along the path can modfy only ths feld. Ths feedback then reaches the recever, whch then changes ts congeston wndow (cwnd) accordngly and sends ts acknowledgement to the sender. When the sender receves a postve acknowledgement, 22

30 t ncreases ts congeston wndow whle a negatve acknowledgement makes the sender to reduce ts congeston wndow. The congeston wndow can be estmated as [12] : cwnd = max ( cwnd + H_feedback, s) (3.1) where, cwnd : current congeston wndow H_feedback : Feld n the congeston header whch the sender ntalzes to request ts desred wndow ncrease s : packet sze The feedback sent by the router s computed usng the farness and utlzaton controller. The XCP controllers make a sngle control decson every average RTT. [14] defnes some basc parameters n order to explan the system. An estmaton nterval of T e s defned as the nterval over whch the routers estmate all the parameters and t s usually an estmate of the average rtt of the flows n the lnk. Thus, T e s dynamc and keeps changng wth the average rtt. The control nterval, T c, on the other hand s a constant nterval durng whch a sngle decson s appled. After the end of one T c, the router reads all the estmators and decdes what knd of feedback t should send n ths control nterval. The XCP routers take nto account the nput traffc rate, whch the total number of bts that s sent to a queue n one control nterval dvded by the nterval duraton and spare bandwdth, whch s the dfference between the lnk capacty and the nput traffc rate. The router also estmates the average rtt. The average rtt computed n one estmaton nterval gves the duraton for the next T e. The rtt and the cwnd of each flow are normally provded by the sender tself n the congeston header. The average rtt s computed as: 23

31 rtt = s rtt * r s r (3.2) where s s the packet sze n bytes and r s the throughput n bytes/sec. The router mantans a per-lnk estmaton-control tmer that has a value equvalent to the average RTT of the flows traversng the lnk. Tradtonally, the effcency and the farness have been grouped together under the same control law. Conceptually, however, they are dfferent. Whle Effcency nvolves only the traffc behavor as a whole where t concerns le wth the nput traffc rate, farness on the other hand, nvolves the relatve throughput of the flows sharng a network. A scheme s sad to be far, only when all the flows n the lnk have the same throughput, whether congeston exst or not. [16]. Thus, n [16] a decouplng of these two fundamentally dfferent approaches s done. One of the major advantages of such a decouplng s that t also smplfes the desgn of each controller where any one of them can be modfed wthout affectng the other s performance. Thus, two controllers, namely Effcency Controller (EC) and Farness Controller (FC) exst. Effcency Controller (EC) The effcency controller s used to maxmze lnk utlzaton and to mnmze the aggregate traffc. The XCP-EC computes the desred ncrease or decrease n the number 24

32 of bytes n an average RTT. [16] gves a method to compute ths feedback φ n each average RTT as: Φ= α. S β. Q/d, (3.3) where: α, β are constant parameters, d s the average RTT S s the spare bandwdth and Q s the persstent queue sze. The spare bandwdth s calculated as the dfference between the nput traffc rate and the lnk capacty. The persstent queue sze s calculated by takng the mnmum queue seen by a packet that has just arrved durng the last propagaton delay. Thus, the feedback φ s proportonal to the spare bandwdth S. It s also proportonal to the persstent queue sze Q. So, when the lnk s underutlzed, S>=0, a postve feedback s sent and when S<0, the lnk s over utlzed.e. congeston and a negatve feedback s sent. Ths gves the feedback φ n bytes. Thus, to dran the persstent queue, the feedback s made proportonal to the persstent queue too. Fnally, because the feedback s n bytes/sec, to match the unt of Q, the Q value s dvded by the control nterval d. Ths s also necessary as scalng down Q by a factor of d ensures that the system remans stable for any feedback delay. The effcency controller does not bother about farness of all the flows n the network at all but s only concerned that the lnk s utlzed to the maxmum whle the drop rate and persstent queues are at a mnmum [16]. 25

33 α and β are constant and set to values of 0.4 and respectvely based on stablty analyss [16]. Thus, ths value of feedback s then assgned to the H_feedback feld present n the congeston header. Now, as each router along the lnk traversng from the sender to the recever changes the H_feedback feld, t wll at the end contan the feedback from the bottleneck router. [16] Farness Controller (FC) The farness controller n [16] used Addtve Increase and Multplcatve Decrease (AIMD) [1] control law to converge to farness and to allocate feedback to ndvdual packets. But, as was ndcated by the comparson chart n Secton 1, dfferent wndow control algorthms can be used nstead of AIMD and ts performance can be seen. The bref descrpton s ts mplementaton n XCP s farness control s as descrbed here. The job of the farness controller s to dvde the total feedback equally and to allocate them among the ndvdual flows to acheve farness. The farness controller n [16] uses AIMD control law. Thus, for such a control law, ntutvely, the followng polcy can be appled: If Φ>0, allocate feedback equally among all the flows If Φ<0, allocate feedback to flows proportonal to the current throughput [16] Farness Controller wth SIMD In order to allocate postve and negatve feedback to ndvdual packets, the Farness Controller can use the Square Increase, Multplcatve Decrease Algorthm that was explaned n Sec Ths algorthm ncreases ts congeston wndow proportonal to the 26

34 square of the tme elapsed snce the detecton of the last loss event [4] and decreases multplcatvely. Increase : w( t + rtt) = w( t) + α w( t) w0 ; α > 0 Decrease : w( t + δ ) = w( t) βw( t);0 < β < 1 (3.4) 3 β where α = 2β (1 ) 2w 3 max fβ << 1, 3β 2w max Here, w max s gven the value of the wndow sze at the end of the last congeston epoch, whch s a seres of ncrements followed by one decrement and w 0 s the wndow sze of the decrease. Thus, wth the decrease rule, w 0 can be obtaned from w max and vce versa. For example, w 0 1 β ) = ( w. max Thus, the feedback polcy can be slghtly altered to sut SIMD control law. If Φ>0, allocate feedback equally among all the flows by square root ncrease If Φ<0, allocate feedback to flows proportonal to the current throughput As the router should calculate the feedback and place t n the congeston header of each packet, the router needs to calculate the postve feedback (p) and the negatve feedback (n) separately and then t would be easer to compute the actual per-packet feedback H_Feedback whch would be the dfference between the postve and negatve feedback. H_Feedback=p -n And the shuffled traffc can be computed as h=max(0, γ.y- Φ ) [16] where y s the nput traffc rate and γ s a constant equal to

35 Per-packet Postve Feedback The per-packet postve feedback s computed when the feedback s postve (Φ>0). When the feedback s postve, then the throughput needs to be ncreased by the same amount to ensure farness. As accordng to the SIMD ncrease equaton shown above, for a flow to ncrease ts throughput every rtt proportonally to r ( t) r0, then the sum of the postve feedback for all the flows together that t receves n a rtt should also be proportonal to r ( t) r0, where r (t) s the current Throughput of the flow and r 0 the Throughput of a flow after a congeston epoch.e. after a decrease. Therefore, the postve feedback per-packet should be proportonal to r ( t) r. Now, the total change 0 n the throughput of a flow s the sum of the per-packet feedback t receves. Hence, the per-packet feedback can be calculated by dvdng the change n throughput by the expected number of packets from a flow that a router sees n a control nterval d[16]. The expected number of packets n a control nterval s proportonal to the flow s r throughput dvded by the packet sze. As d s a constant durng a control nterval, the s per-packet postve feedback s nversely proportonal to ts throughput dvded by ts packet sze 1 r / s. Hence, the per-packet postve feedback can be gven as: p s = ξ p * * r r0 (3.5) r = where ξ p s a constant. The total ncrease n the aggregate traffc rate s h+max(φ,0),where max(φ,0) makes sure that the postve feedback s only beng computed. Now, ths value s equal to the sum of 28

36 the ncrease n the rates of all the flows, whch s nothng but the postve feedback of a flow. [16] L h+ max(φ,0)= p (3.6) where L s the number of packets seen by the router durng a control nterval d. From ths ξ p can be derved. ξ p = h + max( φ,0) s r (3.7) Thus, the per-packet postve feedback s computed usng the Square Increase, Multple Decrease algorthm where a flow s proportonal to the square root of the dfference n the throughputs. As the α value n SIMD does not reman constant and keeps changng wth the value of the maxmum throughput, the per-packet postve feedback needs to be altered to accommodate that too. Thus, for a flow to ncrease ts throughput every rtt proportonally to r ( t) r0, then the sum of the postve feedback for all the flows together that t receves n a rtt should also be proportonal to r ( t) r0, where r (t) s the current Throughput of the flow and r 0 the Throughput of a flow after a congeston epoch.e. after a decrease. Now, the throughput of a flow s also proportonal to the αsimd. Thus the per-packet postve feedback s also proportonal to 3 β 2β (1 ) 3 2r max where r max s the 29

37 wndow sze just before the decrease and β s a constant set to 1.236, computed after stablty analyss. Thus, the per-packet postve feedback s now computed as: p s 3 β = ξ p * * r r0 * (3.8) r 2β (1 ) 2rmax 3 Per-packet Negatve Feedback The per-packet negatve feedback can be computed smlar to the per-packet postve feedback. The SIMD has a multple decrease smlar to the orgnal XCP-AIMD and hence the negatve feedback calculaton remans unchanged. The per-packet negatve feedback s calculated when feedback s negatve (Φ<0). To compute the negatve feedback, the throughput of any flow I should be proportonal to ts current throughput (.e. Δr α r ). The desred per-packet feedback s the change n the throughput dvded by thr expected number of packets that a router wll see n a control r nterval. As d s constant for a control nterval, t wll be nversely proportonal to. s Thus, the r values cancel out each other and per-packet negatve feedback s proportonal to the packet sze. n = ξ * s (3.9) n where ξ n s a constant. Smlar to the postve feedback computaton, the total decrease n the aggregate traffc s the sum of the decrease n the rates of all flows. 30

38 L h+ max(-φ,0)= n (3.10) where L s the number of packets seen by the router durng a control nterval d. From ths ξ p can be derved. ξ p = h + max(-φ,0) s Thus the per-packet negatve feedback s calculated usng multplcatve decrease. All the parameters for the postve and negatve feedback can be easly obtaned by the router. All these parameters are present n the congeston header of the packet. The Farness Controller tracks the total amounts of postve and negatve allocaton when a control nterval starts. It stops computng the postve feedback when the sum of the postve feedback that has been allocated n that control nterval becomes equal to h+ max(φ,0) and the stops computng negatve feedback when the negatve feedback allocated n that control nterval becomes equal to h+ max(-φ,0). Ths s done to ensure that the allocaton error s bounded and that t follows the decson of the Effcency Controller. Farness Controller wth Bnomal Algorthms There are two man bnomal algorthms that have been taken nto consderaton here; the Square Root Algorthm (SQRT) and the Inverse Increase, Addtve Decrease Algorthm (IIAD). 31

39 The general bnomal equatons are: Increase: w(t+ rtt) =w(t) + α/w(t) k α>0 Decrease: w(t) + δ= w(t) β w(t) l 0<β<1 () SQRT The farness controller s plugged n wth the Square Root bnomal algorthm. Ths has the followng control law: Increase: w(t+ rtt) =w(t) + α/ w(t) α>0 Decrease: w(t) + δ= w(t) β w(t) 0<β<1 Per-packet Postve Feedback: The per-packet postve feedback can be calculated wth the ncrease n the throughput of all flows to be proportonal to 1/ r. The expected number of packets n a control nterval s proportonal to the flow s r throughput dvded by the packet sze. As d s a constant durng a control nterval, the s per-packet postve feedback s nversely proportonal to ts throughput dvded by ts packet sze 1 r / s. Hence, the per-packet postve feedback can be gven as: p = ξ p * (3.11) r s * r where ξ p s a constant. The total ncrease n the aggregate traffc rate s h+max(φ,0),where max(φ,0) makes sure that the postve feedback s only beng computed. Now, ths value s equal to the sum of 32

40 the ncrease n the rates of all the flows, whch s nothng but the postve feedback of a flow. [16] L h+ max(φ,0)= p, where L s the number of packets seen by the router durng a control nterval d. From ths ξ p can be derved. ξ p = h + max( φ,0) s r * r Per-packet Negatve Feedback: To compute the negatve feedback, the throughput of any flow I should be proportonal to ts current throughput (.e. Δr α r ). The desred per-packet feedback s the change n the throughput dvded by the expected number of packets that a router wll see n a control nterval. As d s constant for a control nterval, t wll be nversely proportonal to r. s n s * * r = ξ n (3.12) r where ξ n s a constant. Smlar to the postve feedback computaton, the total decrease n the aggregate traffc s the sum of the decrease n the rates of all flows. L h+ max(-φ,0)= n, 33

41 where L s the number of packets seen by the router durng a control nterval d. From ths ξ n can be derved. ξ n = h + max(-φ,0) s * r r () IIAD The farness controller s plugged n wth the Inverse Increase Addtve Decrease bnomal algorthm. Ths has the followng control law: Increase: w(t+ rtt) =w(t) + α/w(t) α>0 Decrease: w(t) + δ= w(t) β 0<β<1 Per-packet Postve Feedback: The per-packet postve feedback can be calculated wth the ncrease n the throughput of all flows to be proportonal to 1/r The expected number of packets n a control nterval s proportonal to the flow s r throughput dvded by the packet sze. As d s a constant durng a control nterval, the s per-packet postve feedback s nversely proportonal to ts throughput dvded by ts packet sze 1 r / s. Hence, the per-packet postve feedback can be gven as: p s = ξ p * (3.13) r 2 where ξ p s a constant. 34

42 The total ncrease n the aggregate traffc rate s h+max(φ,0),where max(φ,0) makes sure that the postve feedback s only beng computed. Now, ths value s equal to the sum of the ncrease n the rates of all the flows, whch s nothng but the postve feedback of a flow. [16] L h+ max(φ,0)= p, where L s the number of packets seen by the router durng a control nterval d. From ths ξ p can be derved. ξ p = h + max( φ,0) s 2 r Per-packet Negatve Feedback: To compute the negatve feedback, the throughput of any flow I should be proportonal to ts current throughput (.e. Δr α constant). The desred per-packet feedback s the change n the throughput dvded by the expected number of packets that a router wll see n a control nterval. As d s constant for a control nterval, t wll be nversely proportonal to r. s s n * = ξ n (3.14) r where ξ n s a constant. Smlar to the postve feedback computaton, the total decrease n the aggregate traffc s the sum of the decrease n the rates of all flows. 35

43 L h+ max(-φ,0)= n, where L s the number of packets seen by the router durng a control nterval d. From ths ξ n can be derved. ξ n = h + max(-φ,0) s r Farness Controller usng MIMD The farness controller s plugged n wth the Multplcatve Increase, Multplcatve Decrease algorthm. Ths has the followng control law: Increase: w(t+ rtt) =w(t) + α*w(t) α>0 Decrease: w(t) + δ= w(t) β* w(t) 0<β<1 Per-packet Postve Feedback: The per-packet postve feedback can be calculated wth the ncrease n the throughput of all flows to be proportonal to r The expected number of packets n a control nterval s proportonal to the flow s r throughput dvded by the packet sze. As d s a constant durng a control nterval, the s per-packet postve feedback s nversely proportonal to ts throughput dvded by ts packet sze 1 r / s. Hence, the per-packet postve feedback can be gven as: p = ξ * s (3.15) p 36

44 where ξ p s a constant. The total ncrease n the aggregate traffc rate s h+max(φ,0),where max(φ,0) makes sure that the postve feedback s only beng computed. Now, ths value s equal to the sum of the ncrease n the rates of all the flows, whch s nothng but the postve feedback of a flow. [16] L h+ max(φ,0)= p, where L s the number of packets seen by the router durng a control nterval d. From ths ξ p can be derved. ξ p = h + max( φ,0) s Per-packet Negatve Feedback: To compute the negatve feedback, the throughput of any flow I should be proportonal to ts current throughput (.e. Δr α r ). The desred per-packet feedback s the change n the throughput dvded by the expected number of packets that a router wll see n a control nterval. As d s constant for a control nterval, t wll be nversely proportonal to r. s n = ξ n *s (3.16) where ξ n s a constant. Smlar to the postve feedback computaton, the total decrease n the aggregate traffc s the sum of the decrease n the rates of all flows. L h+ max(-φ,0)= n, 37

45 where L s the number of packets seen by the router durng a control nterval d. From ths ξ n can be derved. ξ n = h + max(-φ,0) s 38

46 IV: FINDINGS Smulaton of XCP usng ns-2 The man XCP protocol sute was downloaded from the ISI webste [18]. The sute had a set of C++ codes along wth a few ns test smulatons. The mplementaton of XCP was frst tested n Red Hat 2 [20] and Fedora Core 2 [21] Lnux platforms. As Red Hat does not have some basc functonalty needed to run ns-2 Fedora was then chosen. Lately, the C++ codes and the ns smulatons were tested on Cygwn [19] a UNIX platform that can be run on Wndows. The code that was downloaded from the XCP webste had many errors and many other fles that were not part of the package were also needed. All the errors and bugs were fxed and any new fles were found on the nternet and the program was recompled wth the complete lst. Once the orgnal XCP was compled t was made to run on the dfferent platforms. Next, the XCP codes were changed to operate usng dfferent Wndow Control Algorthms. New ns smulatons were wrtten to make XCP run on dfferent network topologes. For dfferent Wndow Control algorthms, the performance of XCP was tested and dfferent graphs plotted. A smple topology havng 3 XCP flows and 1TCP flow sharng a bottleneck lnk was created. 39

47 N0 R0 Bottleneck on ths lnk R1 N1 N2 Reverse Traffc/acks Fgure 6 A smple XCP topology nvolvng a bottleneck N0, N1 and N2 are three XCP flows that share a network path from R0 to R1. Packets are sent from N0, N1 and N2 to ther respectve snks through the lnk R0 and R1. Ths lnk R0 to R1 s also shared by a TCP flow. Thus, the lnk from R0 to R1 not only has the traffc from the three XCP flows, t also has traffc from a TCP flow. Also the reverse traffc and acknowledgements from each of the destnatons (snks) back to the sources need to be consdered. The statstcs of the lnk are as follows: The qtype s set to XCP, the bandwdth of the bottleneck as well as the as that of the each of the nodes are set to 20MB, the delay s set to 10ms, the buffer sze s always set to the bandwdth-delay product and the data packet sze s set to 1000 for all the XCP flows as well as the TCP flow. The XCP flows and the TCP flow s started at dfferent tmes to emulate a real tme network wth bottleneck. Smulatons are run for these four flows and the performance of XCP wth dfferent Wndow Control Algorthms s evaluated wth each other and wth the TCP flow. 40

48 Graphs XCP-AIMD The orgnal XCP flow wth Addtve Increase and Multplcatve Decrease was run for the topology mentoned above and the graphs were plotted. Four graphs were plotted: () Comparng the congeston Wndow of the dfferent XCP flows wth the TCP flow for the duraton of 30sec. Fgure 7 Comparson of XCP-AIMD Congeston wndow wth TCP flow 41

49 () Comparng the sequence numbers of the packets sent for the dfferent XCP flows wth the TCP flow for the duraton of 30sec. Fgure 8 Comparson of XCP-AIMD Sequence Number wth TCP flow 42

50 () Comparng the throughput of the dfferent XCP flows for duraton of 30sec. Fgure 9 Comparson of XCP-AIMD Throughput 43

51 (v) The utlzaton of the XCP flows for a duraton of 30sec. Fgure 10 XCP-AIMD Utlzaton 44

52 XCP-modfed SIMD The XCP was next plugged n wth the control equatons of SIMD. It was seen that the SIMD equatons wth ther square ncrease component dd not mprove the overall performance of the XCP when compared to the exstng AIMD graphs. After tryng dfferent algorthms, the fourth root ncrease component and alpha value correspondng to that, and a beta value of was found to be optmal. The results are shown below. () Comparng the congeston Wndow of the dfferent XCP flows wth the TCP flow for the duraton of 30sec. Fgure 11 Comparson of XCP- fourth root SIMD Congeston wndow wth TCP flow 45

53 () Comparng the sequence numbers of the packets sent for the dfferent XCP flows wth the TCP flow for the duraton of 30sec. Fgure 12 Comparson of XCP- fourth root SIMD Sequence Number wth TCP flow 46

54 () Comparng the throughput of the dfferent XCP flows for a duraton of 30sec. Fgure 13 Comparson of XCP-fourth root SIMD Throughput 47

55 (v) The utlzaton of the XCP flows for a duraton of 30sec. Fgure 14 XCP-fourth root SIMD Utlzaton 48

56 XCP-AIMD and fourth root SIMD run for 300 seconds The XCP wth both AIMD and fourth root SIMD s run for 300 seconds to montor ther behavor over a longer perod of tme and the graphs are plotted. () A comparson of the congeston wndow (cwnd) of the dfferent XCP flows wth TCP. Fgure 15 Cwnd comparson for XCP-AIMD(300 sec) Fgure 16 Cwnd Comparson for XCP-fourth root SIMD(300 sec) () A comparson of the sequence number of the dfferent XCP flows wth TCP flow. Fgure 17 Sequence Number Comparson for XCP-AIMD(300 sec) Fgure 18 Sequence Number Comparson for XCP-fourth root SIMD(300 sec) 49

57 () A comparson of the throughputs of the dfferent XCP flows. Fgure 19 Throughput Comparson of XCP- AIMD(300 sec) (v)utlzaton for the dfferent XCP flows. Fgure 20 Throughput Comparson of XCP-fourth root SIMD(300 sec) Fgure 21 Utlzaton of XCP-AIMD(300 sec) Fgure 22 Utlzaton of XCP-fourth root SIMD(300 sec) 50

58 From the XCP graphs that are plotted for both the Addtve Increase Multplcatve Decrease (AIMD) algorthm as well as the fourth-root Square Increase Multplcatve Decrease (SIMD) algorthm that s mplemented after modfyng the exstng SIMD (wth square root) algorthm, t s seen that the change to fourth-root, modfyng w(t) and settlng on a beta value of has proven favorable n terms of performance and operablty. In the XCP scenaro, t has been observed that the modfed SIMD algorthm performs better than the exstng XCP-AIMD algorthm. From the graphs n Fgures 7-22, an overall mprovement s seen n the graphs that were obtaned for XCP-fourth root SIMD when compared to XCP-AIMD. There s an ncrease to the congeston wndow (cwnd) n XCP-fourth root SIMD and there are also lesser oscllatons. Also, the number of packets beng sent (sequence number) s more n XCP-fourth root SIMD when compared to XCP-AIMD as seen from the graph where the XCP-AIMD graph shows only 11 packets at the maxmum beng set n the duraton of 30 seconds whereas n XCPfourth root SIMD, more than 12 packets are beng sent for the same amount of tme. The Throughputs of XCP-fourth root SIMD s also shown to be far more superor to XCP- AIMD. XCP-fourth root SIMD s seen to utlze the bottleneck resources much better than XCP-AIMD wth XCP-fourth root SIMD s utlzaton beng around 1.0 and XCP- AIMD s utlzaton around 0.8. There fgures reman the same when the smulatons were run for a longer perod of tme (300 seconds) and the behavor of the algorthms reman unmodfed wth XCP-fourth root SIMD emergng the obvous wnner n terms of performance over XCP-AIMD that s beng used at present n the XCP protocol. 51

59 XCP wth Bnomal Algorthms The XCP protocol was then plugged wth the two man bnomal algorthms, SQRT and IIAD and the performance analyzed. () A comparson of the congeston wndow (cwnd) of the dfferent XCP flows wth TCP. Fgure 23 Cwnd Comparson of XCP-SQRT Fgure 24 Cwnd Comparson of XCP-IIAD () A comparson of the sequence number of the dfferent XCP flows wth TCP flow. Fgure 25 Sequence Number Comparson of XCP-SQRT Fgure 26 Sequence Number Comparson of XCP-IIAD 52

60 () A comparson of the throughputs of the dfferent XCP flows. Fgure 27 Throughput Comparson of XCP-SQRT Fgure 28 Throughput Comparson of XCP-IIAD (v)utlzaton for the dfferent XCP flows. Fgure 29 Utlzaton of XCP-SQRT Fgure 30 Utlzaton of XCP-IIAD It can be seen from the two dfferent graphs of XCP-SQRT and XCP-IIAD, that although the XCP-SQRT s slghtly better n performance when compared to XCP-IIAD, they are not better than XCP-AIMD or XCP-fourth root SIMD. 53

61 XCP wth MIMD The XCP protocol was then plugged wth the Multplcatve Increase, Multplcatve Decrease algorthm and the performance was studed. Fgure 31 Cwnd Comparson of XCP-MIMD Fgure 32 Sequence Number Comparson of XCP-MIMD Fgure 33 Throughput Comparson of XCP-MIMD Fgure 34 Utlzaton of XCP-MIMD It can be seen that the performance of the MIND algorthm s also not that effcent. It only worsens the exstng performance provded by the XCP-AIMD algorthm. 54