A High-Performance Router: Using Fair-Dropping Policy

Transcription

1 Internatonal Journal of Computer Scence and Telecommuncatons [Volume 5, Issue 4, Aprl 2014] A Hgh-Performance Router: Usng Far-Droppng Polcy ISSN Seyyed Nasser Seyyed Hashem 1, Shahram Jamal 2 and Had Motarez 3 1 Young Researchers Club, Ardabl branch, Islamc Azad Unversty, Ardabl, Iran 2 Department of Computer Engneerng, Mohaghegh Ardabl Unversty, Ardabl, Iran 3 Department of Computer Engneerng, Tabrz branch, Islamc Azad Unversty, Tabrz, Iran 1 naser_seyed_hashem@yahoo.com, 2 jamal@ust.ac.r, 3 h.motarez@gmal.com Abstract Actve Queue Management (AQM) employs some types of feedbacks to dssemnate congeston nformaton to the sources. Packet droppng s the most used feedback on the Internet. Ths paper dscusses that an unfar droppng dscplne leads to performance reducton, the unfar bandwdth sharng and the nstablty on the Internet. Then t proposes a far droppng AQM algorthm and shows how ths farness mproves the network performance, farness and stablty. Extensve packet-level smulatons done n ns-2 envronment, show that the proposed algorthm, on one hand, presents a far bandwdth allocaton among compettor flows and on the other hand, ths farness s a launcher for mproved performance n terms of lnk utlzaton, queue sze and packet drop rate. Index Terms Farness, Network Congeston Control and Actve Queue Management C I. INTRODUCTION ONGESTION Control s one of the crtcal ssues n computer networks and therefore, t has attracted a lot of attentons n recent years [1] [7]. The congeston control mechansms on the Internet consst of the congeston wndow algorthms of transmsson control protocol (TCP), runnng at end-systems, and actve queue management algorthms runnng at the routers, seekng to obtan hgh network utlzaton, small amounts of queung delay, and some degree of farness among users. TCP sends data packets to network accordng to addtve ncrease multplcatve decrease (AIMD) algorthm [8] n whch sendng rate ncreases one packet per round trp tme (RTT) for probng avalable bandwdth, when no congeston occurs, and multplcatvely decreases to half of past rate, when congeston detected n network. The actve queue management algorthm n the routers s responsble for producng congeston nformaton for sources. The router notfes source from network congeston by droppng packets or n some other ways such as explct congeston notfcaton (ECN) [9]. RED [10] s the most famous AQM algorthm ntroduced to congeston control n 1993 (Fg. 1). After that, varous works have been done to enhance RED's performance [11] [16]. In spte of the vast amount of researches n ths feld, there are yet many problems such as stablty, effcency and farness that need more attentons to be solved. Farness, whch smply means allocatng the same share to all, has been addressed n some works as [17], [18]. The CHOKe proposed n [17] s a queue management dscplne to far bandwdth allocaton whch doesn t requre any per flow state nformaton. Ths scheme ams to approxmate the far queung polcy and works wth heurstc manner to decde to drop a packet durng congeston n a random toss f t fnds another packet of the same flow. The FABA [18] s a rate control based queue management algorthm whch uses the noton of token buckets per flow for buffer management at the network edge. Ths scheme performs far bandwdth allocaton depend on permtted rate of token addton n a partcular flow bucket. FABA could establsh good far allocaton for both adaptve and nonadaptve flows. In [22] a network rate management protocol (RMP) has been proposed that controls the rate of all flows based on the far target rates computed by the RMP. Upon ther suggeston, each non-tcp aggregate flow s polced by ts respectve edge router and each TCP flow adapts ts rate accordng to the RMP suggested far target rate. On the other hand, there are many publshed works that address performance of the Internet congeston control [19], [20], [21]. Fg. 1: Packet droppng functon of RED As we know, TCP tunes ts sendng rate based on receved feedbacks that are often n the form of dropped packets. Journal Homepage:

2 Seyyed Nasser Seyyed Hashem et al. Hence, t seems that by accurately tunng of these feedbacks, we can drect the congeston control scheme to a stable, hghperformance, and far operatonal pont. In ths paper we study the farness ssue from far droppng pont of vew and dscuss how t provdes far bandwdth allocaton along wth mproved performance metrcs. It proposes a modfcaton over the packet droppng pattern of RED algorthm, to acheve a far packet droppng scheme consderng the flows' RTT. We show that ths far droppng scheme not only leads to far bandwdth sharng among passng flows, but also t wll have an nterestng and mportant effect, namely, mproved performance on ndvdual sources as well as the whole network. The proposed algorthm s called, FDD-RED (Far Droppng Dscplne over RED). Rest of ths paper s organzed as follows. Secton 2 presents the proposed algorthm. In secton 3, we brng extensve packet-level smulaton results and fnally our conclusons are gven n secton 4. II. FDD-RED: FAIR DROPPING DISCIPLINE FOR RED Ths secton addresses two mportant ssues that affect success of an actve queue management.e. the performance and the farness. The performance can be characterzed n terms of parameters such as utlzaton, queue length and packet drops count. The farness, on the other hand, s an mportant feature that s pursued n any congeston control scheme. It means that all flows passng a bottleneck lnk share the same amount of the bandwdth. It s clear that packet droppng functon n AQM algorthms affects both farness and performance ssues. A flow that encounters more drops wll have smaller congeston wndow sze and so lower sendng rate and flows that encounter fewer drops wll have more sendng rate. Hence, t seems that packet droppng functon s a place that has enough potental to manage farness ssue. On the other hand, packet droppng functon affects performance metrcs such as throughput and queue sze. When a flow's packets are dropped t reduces ts throughput and consequently the queue wll be shortened. We beleve that performance, farness and stablty of the network can be realzed by a far packet droppng functon. In an unfar droppng scheme almost all flows are damaged. Obvously flows that encounter many drops wll have low throughput and even may face the tmeout. On the other hand, when these sources encounter burst drops, some other sources lkely won't see any drop and hence they won't be nformed about exstence of the network congeston. The result s that they don t contrbute n the congeston control process and snce they contnue to send wth current rate, n the next round they may be faced wth many drops. The network, on the other hand, s damaged n exstence of such unfar packet droppng scheme. When AQM drops many packets from a certan flow wth congeston wndow sze of w, for the frst drop the source's wndow sze wll be reduced by w/2, but for the second drop, the reducton amount wll be w/4 and for the thrd one t wll be w/8. Ths means that n an unfar droppng scheme, whle some sources don t receve any reducton commands from the AQM, some other sources receve many reducton commands but ther reacton to these commands follows a decreasng trend. In other words, unfarness leads to reduced effcency of the congeston control feedbacks that prevents the congeston control scheme reachng ts target such as hgh performance, farness and stablty. A short revew over the exstng AQM algorthms such as Drop tal and RED shows that they often drop burst packets from a small number of flows. Ths paper proposes an mprovement over RED algorthm that equps t wth a far droppng dscplne. The proposed approach, called FDD- RED, also takes nto account a well-known drawback of TCP.e. ts unfar behavor aganst flows wth dfferent RTTs and tres to penalze the short RTT flows that utlze more than ther far share. To ths end t mantans a Far Droppng Index, defned as (1) for some recent flows and tres to keep t at the same level for varous flows. Accordng to ths ndex a source whose RTT s lower than average of RTTs encounters more drops compared to the source whose RTT s longer than average of RTTs. RTTavg FDI Drop for 1, 2,..., n RTT (1) Where n s the number of flows that have passed through the queue durng the last RTT avg. Drop s the number of packets that have been dropped from flow, RTT s RTT of flow and RTT avg stands for the average of RTTs of all flows passng from the congested lnk. Note that, there are several methods avalable to onlne estmaton of network parameters. For example, accordng to the methods proposed n [23] [25] the network parameters such as RTT and number of flow connectons (n) can be determned, accurately. The detaled descrpton of FDD-RED algorthm has been gven n Fg. 2. Note that ths algorthm keeps a lmted amount of nformaton only for those flows that are passng durng the last RTT avg and hence has low overhead and s scalable. Varables: 1: lastdropmarktme[]: an array for last drop/mark tme; 2: penalty[]: an array to keep penalty value of flows; 3: RTT[]: an array to keep round trp tme of flows; 4: avgrtt: average RTT of flows; 5: pkt: network packet;

3 Internatonal Journal of Computer Scence and Telecommuncatons [Volume 5, Issue 4, Aprl 2014] Intalzaton: 1: lastdropmarktme[] 0; 2: penalty[] 0; New flow arrval functon(): 1: for a new flow (newflow) arrval 2: RTT[newflow] = Estmate new flow s RTT; 3: avgrtt = Estmated average RTT; New packet arrval functon(): 1: for each Packet(pkt) arrval from flow(flow) 2: Calculate_drop_probablty(pkt); 3: f pkt must be Drop/Mark then 4: PenalzeFlow(flow) 5: end for PenalzeFlow functon(flow): 1: fd = select flow that has latest tme n lastdropmarktme[]; 3: penalty[flow] = penalty [flow] + avgrtt/rtt[flow]; 4: penalty [fd] = penalty [fd] + avgrtt / RTT[fd]; 5: fd = select flow that has greatest value n Penalty[]; 6: pkt = select a packet from flow (fd); 8: penalty [fd] = penalty [fd] 1; 9: drop/mark(pkt); 10: lastdropmarktme[fd] = now; Fg. 2: The FDD-RED algorthm envronments wth dfferent RTTs. Our smulatons also show that FDD-RED drops less packets, dampens oscllatons and smoothly converges to hgh utlzaton, small queue sze and far bandwdth allocaton. III. Fg. 3: The network topology PACKET LEVEL SIMULATION RESULTS In order to evaluate the proposed algorthm, we mplement t as an extenson to RED module of ns-2 smulator [27]. We present a group of smulaton results to demonstrate the valdty of our desgn. We demonstrate through extensve smulatons that FDD-RED outperforms RED especally n A) Smulaton Setup Our smulatons use the topology n Fg. 3. The bottleneck capacty s 1 Mbps, and the number of flows vares accordng to the objectve of the experments. The buffer sze wll be dfferent n varous scenaros. The data packet sze s 500 bytes. Smulaton duraton vares dependng on the propagaton delay but s always as long as 20 seconds. All smulatons were run long enough to ensure the system has reached a consstent behavor. The basc parameters of RED are selected as follows: mn th = 25 packets, max th = 75 packets, max p = 0.01 and w q = We present three smulaton scenaros. In the frst scenaro we study about farness and performance of FDD-RED algorthm n a network wth dentcal RTTs. Second scenaro studes the mpact of the buffer sze and number of flows on the performance of FDD-RED and fnally scenaro 3

4 Seyyed Nasser Seyyed Hashem et al. examnes how dfferent RTTs affect FDD-RED's behavor. In each scenaro the network s smulated once under RED algorthm and then under FDD-RED algorthm to compare the results. B) Scenaro 1: Farness study for a Network wth Identcal RTTs In ths experment 5 long-lved flows wth RTT of 100 ms share the bottleneck n topology of Fg. 3 and the buffer sze s set to 100 packets n each router. Smulaton results are gven n Fg. 4 Fg. 8. The Fg. 4 shows how the congeston wndows of varous sources evolve durng the tme, when the queue s managed by RED algorthm. Ths fgure shows clearly that dfferent congeston wndows have dfferent trends durng the tme and hence don t follow a far manner. Ths unfarness stems from the fact that RED drop dfferent numbers of packets from varous sources and hence they wll have dfferent wndow szes. But as shown n Fg. 5 when router's queue s managed by FDD-RED, varous congeston wndows follow smlar trends and hence converge to a far pont. Ths farness has roots n the far droppng functon of FDD-RED. a). TCP wndow sze n node 1 b). TCP wndow sze n node 2 c). TCP wndow sze n node 3 d). TCP wndow sze n node 4 e). TCP wndow sze n node 5 Fg. 4: The congeston wndow sze of sources wth orgnal-red a). TCP wndow sze n node 1 b). TCP wndow sze n node 2 c). TCP wndow sze n node 3 d). TCP wndow sze n node 4 e). TCP wndow sze n node 5 Fg. 5: The congeston wndow sze of sources wth FDD-RED

5 Internatonal Journal of Computer Scence and Telecommuncatons [Volume 5, Issue 4, Aprl 2014] Fg. 6, on the other hand, shows bandwdth usage of varous flows over bottleneck lnk for RED and FDD-RED algorthms. Fg. 6 (a) shows that under governance of RED, varous sources use dfferent amounts of bandwdth, but as shown n Fg. 6 (b), n case of FDD-RED throughput the varous sources are near to the far amount. exhbts more far behavor and ts throughput for varous flows are dentcal approxmately. Throughput of the bottleneck lnk has been gven n Fg. 8 for RED and FDD- RED algorthms. Ths fgure shows that FDD-RED converges more rapdly to a steady-state n whch the bottleneck s fully utlzed. a). unfar bandwdth allocaton by orgnal RED Fg. 8: Bottleneck lnk throughputs Fg. 9 shows nstantaneous queue length n bottlenecked lnk for both RED and FDD-RED. It can be observed that far behavor of queue management dscplne could affect stablty of queue length. The queue length fluctuaton n FDD-RED s obvously lower than orgnal RED that has been shown n Fg. 9 (a) and Fg. 9 (b). b). far bandwdth usage by FDD-RED Fg. 6: Bandwdth allocatons for sources (a) orgnal-red (b) FDD-RED a). RED's queue evoluton Fg. 7: Average throughputs for flows Although farness of FDD-RED was expected due to ts far droppng functon, smulaton results show another mportant achevement for FDD-RED.e., ts hgh performance n compared wth RED. Average throughput of RED and FDD- RED have been gven n Fg. 7 accordng to whch FDD-RED b). FDD-RED's queue evoluton Fg. 9: Instantaneous queue lengths for (a) orgnal-red (b) FDD-RED

6 Seyyed Nasser Seyyed Hashem et al. 03 Table 1 compares number of packets dropped by RED and FDD-RED algorthms. It shows that RED drops more packets than FDD-RED algorthm. Snce FDD-RED drops packets unformly from all passng flows then ts feedbacks wll be tmely and effcent. Consequently there wll be no need to further feedbacks n form of more dropped packets. Table 1: Packet droppng for dfferent RTTs Algorthm total dropped (packet) avg. droppng rate (packet/s) RED FDD-RED C) Scenaro 2: Effect of Flows Number and Buffer Capacty Ths experment fxes the bottleneck bandwdth at 1 Mbps and RTT at 100 ms and repeats the smulaton by varous numbers of FTP sources and dfferent buffer szes. Other parameters have the same values used n the prevous experment. Fg. 10 shows the number of dropped packets for dfferent buffer szes rangng from 10 to 200 packets. Accordng to ths fgure, FDD-RED drops fewer packets compared wth RED. Fg. 11, on the other hand, shows the dropped packets number for varous flow numbers rangng from 5 to 100 flows. Agan t can be found that FDD-RED has lower drop count than RED algorthm for dfferent flow numbers. Fg. 10: Dropped packet versus buffer sze Fg. 11: Dropped packet versus flows number D) Scenaro 3: Effect of Heterogeneous RTTs In ths scenaro the network setup s as n scenaro 1, except that t consders heterogeneous RTTs to study about farness of the proposed algorthm. For ths purpose we consder two experments. In the frst experment all flows have same RTT of 100 ms, but n the second scenaro the fve flows have RTT values 20 ms, 40 ms, 60 ms, 80 ms and 100 ms. In both experments the network s smulated once under FDD-RED algorthm and then under RED algorthm and we measure ther farness by usng Jan s farness ndex (FI) [26] shown n equaton (2). FI n n 1 * x n 1 Where n s the number of current flows and x s the average throughput of flow. Value of FI s always no more than 1 and ts larger value ndcates better farness performance. For example when all the competng flows n a network acheve defntely equvalent throughput, FI wll be equal to 1. Table 2 shows the smulaton results. In both experment, FDD-RED s farer than RED algorthm and exhbts better performance n terms of flows throughput and bottleneck utlzaton. Ths better performance results from the decreased drop rate. 2 x 2 (2) Table 2: The smulaton result for FI Homogeneous RTT Heterogeneous RTT RTT Orgnal-RED FDD-RED RTT Orgnal-RED FDD-RED Flow0 100 ms bps bps 20 ms bps bps Flow1 100 ms bps bps 40 ms bps bps Flow2 100 ms bps bps 60 ms bps bps Flow3 100 ms bps bps 80 ms 2448 bps bps Flow4 100 ms bps bps 100 ms bps bps Average flows' Throughput bps bps bps bps Aggregated Throughput bps bps bps bps FI

7 Internatonal Journal of Computer Scence and Telecommuncatons [Volume 5, Issue 4, Aprl 2014] 03 IV. CONCLUSION Ths paper studed the Internet congeston control scheme from a novel perspectve and proposed that an approprate packet droppng functon can drect the congeston control scheme to ts targets. Based on ths dea, t proposed a far packet droppng dscplne whch dstrbutes dropped packets unformly among varous sources consderng ther RTT. We mplemented the proposed model n ns-2 envronment by a modfcaton over RED module. Smulaton results showed that the proposed algorthm not only s farer than RED algorthm but also outperforms RED n terms of throughput, utlzaton, number of dropped packets, queue sze and even stablty. REFERENCES [1]. Wang, J. (2005), A Theoretcal Study of Internet Congeston Control: Equlbrum and Dynamcs, PhD thess, unversty of Caltech. [2]. Guo, S. Lao, X. L, C. (2008), stablty and hopf bfurcaton analyss n a novel congeston control model wth communcaton delay. 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Shahram Jamal s an assstant professor leadng the Autonomc Networkng Group at the Department of Engneerng, Unversty of Mohaghegh Ardabl. He teaches on computer networks, network securty, computer archtecture and computer syetems performance evaluaton. Dr. Jamal receved hs M.Sc. and Ph.D. degree from the Dept. of Computer Engneerng, Iran Unversty of Scence and Technology n 2001 and 2007, respectvely. Hs research nterests are computer networks and communcaton systems. Had Motarez receved hs M.SC from Islamc Azad Unversty of Tabrz n Hs research nterests are computer network and grd computng.