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RFC 2884 - Performance Evaluation of Explicit Congestion Notific


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Network Working Group                                     J. Hadi Salim
Request for Comments: 2884                              Nortel Networks
Category: Informational                                        U. Ahmed
                                                    Carleton University
                                                              July 2000

   Performance Evaluation of Explicit Congestion Notification (ECN)
                             in IP Networks

Status of this Memo

   This memo provides information for the Internet community.  It does
   not specify an Internet standard of any kind.  Distribution of this
   memo is unlimited.

Copyright Notice

   Copyright (C) The Internet Society (2000).  All Rights Reserved.

Abstract

   This memo presents a performance study of the Explicit Congestion
   Notification (ECN) mechanism in the TCP/IP protocol using our
   implementation on the Linux Operating System. ECN is an end-to-end
   congestion avoidance mechanism proposed by [6] and incorporated into
   RFC 2481[7]. We study the behavior of ECN for both bulk and
   transactional transfers. Our experiments show that there is
   improvement in throughput over NON ECN (TCP employing any of Reno,
   SACK/FACK or NewReno congestion control) in the case of bulk
   transfers and substantial improvement for transactional transfers.

   A more complete pdf version of this document is available at:
   http://www7.nortel.com:8080/CTL/ecnperf.pdf

   This memo in its current revision is missing a lot of the visual
   representations and experimental results found in the pdf version.

1. Introduction

   In current IP networks, congestion management is left to the
   protocols running on top of IP. An IP router when congested simply
   drops packets.  TCP is the dominant transport protocol today [26].
   TCP infers that there is congestion in the network by detecting
   packet drops (RFC 2581). Congestion control algorithms [11] [15] [21]
   are then invoked to alleviate congestion.  TCP initially sends at a
   higher rate (slow start) until it detects a packet loss. A packet
   loss is inferred by the receipt of 3 duplicate ACKs or detected by a

   timeout. The sending TCP then moves into a congestion avoidance state
   where it carefully probes the network by sending at a slower rate
   (which goes up until another packet loss is detected).  Traditionally
   a router reacts to congestion by dropping a packet in the absence of
   buffer space. This is referred to as Tail Drop. This method has a
   number of drawbacks (outlined in Section 2). These drawbacks coupled
   with the limitations of end-to-end congestion control have led to
   interest in introducing smarter congestion control mechanisms in
   routers.  One such mechanism is Random Early Detection (RED) [9]
   which detects incipient congestion and implicitly signals the
   oversubscribing flow to slow down by dropping its packets. A RED-
   enabled router detects congestion before the buffer overflows, based
   on a running average queue size, and drops packets probabilistically
   before the queue actually fills up. The probability of dropping a new
   arriving packet increases as the average queue size increases above a
   low water mark minth, towards higher water mark maxth. When the
   average queue size exceeds maxth all arriving packets are dropped.

   An extension to RED is to mark the IP header instead of dropping
   packets (when the average queue size is between minth and maxth;
   above maxth arriving packets are dropped as before). Cooperating end
   systems would then use this as a signal that the network is congested
   and slow down. This is known as Explicit Congestion Notification
   (ECN).  In this paper we study an ECN implementation on Linux for
   both the router and the end systems in a live network.  The memo is
   organized as follows. In Section 2 we give an overview of queue
   management in routers. Section 3 gives an overview of ECN and the
   changes required at the router and the end hosts to support ECN.
   Section 4 defines the experimental testbed and the terminologies used
   throughout this memo. Section 5 introduces the experiments that are
   carried out, outlines the results and presents an analysis of the
   results obtained.  Section 6 concludes the paper.

2. Queue Management in routers

   TCP's congestion control and avoidance algorithms are necessary and
   powerful but are not enough to provide good service in all
   circumstances since they treat the network as a black box. Some sort
   of control is required from the routers to complement the end system
   congestion control mechanisms. More detailed analysis is contained in
   [19].  Queue management algorithms traditionally manage the length of
   packet queues in the router by dropping packets only when the buffer
   overflows.  A maximum length for each queue is configured. The router
   will accept packets till this maximum size is exceeded, at which
   point it will drop incoming packets. New packets are accepted when
   buffer space allows. This technique is known as Tail Drop. This
   method has served the Internet well for years, but has the several
   drawbacks.  Since all arriving packets (from all flows) are dropped

   when the buffer overflows, this interacts badly with the congestion
   control mechanism of TCP. A cycle is formed with a burst of drops
   after the maximum queue size is exceeded, followed by a period of
   underutilization at the router as end systems back off. End systems
   then increase their windows simultaneously up to a point where a
   burst of drops happens again. This phenomenon is called Global
   Synchronization. It leads to poor link utilization and lower overall
   throughput [19] Another problem with Tail Drop is that a single
   connection or a few flows could monopolize the queue space, in some
   circumstances. This results in a lock out phenomenon leading to
   synchronization or other timing effects [19].  Lastly, one of the
   major drawbacks of Tail Drop is that queues remain full for long
   periods of time. One of the major goals of queue management is to
   reduce the steady state queue size[19].  Other queue management
   techniques include random drop on full and drop front on full [13].

2.1. Active Queue Management

   Active queue management mechanisms detect congestion before the queue
   overflows and provide an indication of this congestion to the end
   nodes [7]. With this approach TCP does not have to rely only on
   buffer overflow as the indication of congestion since notification
   happens before serious congestion occurs. One such active management
   technique is RED.

2.1.1. Random Early Detection

   Random Early Detection (RED) [9] is a congestion avoidance mechanism
   implemented in routers which works on the basis of active queue
   management. RED addresses the shortcomings of Tail Drop.  A RED
   router signals incipient congestion to TCP by dropping packets
   probabilistically before the queue runs out of buffer space. This
   drop probability is dependent on a running average queue size to
   avoid any bias against bursty traffic. A RED router randomly drops
   arriving packets, with the result that the probability of dropping a
   packet belonging to a particular flow is approximately proportional
   to the flow's share of bandwidth. Thus, if the sender is using
   relatively more bandwidth it gets penalized by having more of its
   packets dropped.  RED operates by maintaining two levels of
   thresholds minimum (minth) and maximum (maxth). It drops a packet
   probabilistically if and only if the average queue size lies between
   the minth and maxth thresholds. If the average queue size is above
   the maximum threshold, the arriving packet is always dropped. When
   the average queue size is between the minimum and the maximum
   threshold, each arriving packet is dropped with probability pa, where
   pa is a function of the average queue size. As the average queue
   length varies between minth and maxth, pa increases linearly towards
   a configured maximum drop probability, maxp. Beyond maxth, the drop

   probability is 100%.  Dropping packets in this way ensures that when
   some subset of the source TCP packets get dropped and they invoke
   congestion avoidance algorithms that will ease the congestion at the
   gateway. Since the dropping is distributed across flows, the problem
   of global synchronization is avoided.

3. Explicit Congestion Notification

   Explicit Congestion Notification is an extension proposed to RED
   which marks a packet instead of dropping it when the average queue
   size is between minth and maxth [7]. Since ECN marks packets before
   congestion actually occurs, this is useful for protocols like TCP
   that are sensitive to even a single packet loss. Upon receipt of a
   congestion marked packet, the TCP receiver informs the sender (in the
   subsequent ACK) about incipient congestion which will in turn trigger
   the congestion avoidance algorithm at the sender.  ECN requires
   support from both the router as well as the end hosts, i.e.  the end
   hosts TCP stack needs to be modified. Packets from flows that are not
   ECN capable will continue to be dropped by RED (as was the case
   before ECN).

3.1. Changes at the router

   Router side support for ECN can be added by modifying current RED
   implementations. For packets from ECN capable hosts, the router marks
   the packets rather than dropping them (if the average queue size is
   between minth and maxth).  It is necessary that the router identifies
   that a packet is ECN capable, and should only mark packets that are
   from ECN capable hosts. This uses two bits in the IP header.  The ECN
   Capable Transport (ECT) bit is set by the sender end system if both
   the end systems are ECN capable (for a unicast transport, only if
   both end systems are ECN-capable). In TCP this is confirmed in the
   pre-negotiation during the connection setup phase (explained in
   Section 3.2).  Packets encountering congestion are marked by the
   router using the Congestion Experienced (CE) (if the average queue
   size is between minth and maxth) on their way to the receiver end
   system (from the sender end system), with a probability proportional
   to the average queue size following the procedure used in RED
   (RFC2309) routers.  Bits 10 and 11 in the IPV6 header are proposed
   respectively for the ECT and CE bits. Bits 6 and 7 of the IPV4 header
   DSCP field are also specified for experimental purposes for the ECT
   and CE bits respectively.

3.2. Changes at the TCP Host side

   The proposal to add ECN to TCP specifies two new flags in the
   reserved field of the TCP header. Bit 9 in the reserved field of the
   TCP header is designated as the ECN-Echo (ECE) flag and Bit 8 is

   designated as the Congestion Window Reduced (CWR) flag.  These two
   bits are used both for the initializing phase in which the sender and
   the receiver negotiate the capability and the desire to use ECN, as
   well as for the subsequent actions to be taken in case there is
   congestion experienced in the network during the established state.

   There are two main changes that need to be made to add ECN to TCP to
   an end system and one extension to a router running RED.

   1. In the connection setup phase, the source and destination TCPs
   have to exchange information about their desire and/or capability to
   use ECN. This is done by setting both the ECN-Echo flag and the CWR
   flag in the SYN packet of the initial connection phase by the sender;
   on receipt of this SYN packet, the receiver will set the ECN-Echo
   flag in the SYN-ACK response. Once this agreement has been reached,
   the sender will thereon set the ECT bit in the IP header of data
   packets for that flow, to indicate to the network that it is capable
   and willing to participate in ECN. The ECT bit is set on all packets
   other than pure ACK's.

   2. When a router has decided from its active queue management
   mechanism, to drop or mark a packet, it checks the IP-ECT bit in the
   packet header. It sets the CE bit in the IP header if the IP-ECT bit
   is set. When such a packet reaches the receiver, the receiver
   responds by setting the ECN-Echo flag (in the TCP header) in the next
   outgoing ACK for the flow. The receiver will continue to do this in
   subsequent ACKs until it receives from the sender an indication that
   it (the sender) has responded to the congestion notification.

   3. Upon receipt of this ACK, the sender triggers its congestion
   avoidance algorithm by halving its congestion window, cwnd, and
   updating its congestion window threshold value ssthresh. Once it has
   taken these appropriate steps, the sender sets the CWR bit on the
   next data outgoing packet to tell the receiver that it has reacted to
   the (receiver's) notification of congestion.  The receiver reacts to
   the CWR by halting the sending of the congestion notifications (ECE)
   to the sender if there is no new congestion in the network.

   Note that the sender reaction to the indication of congestion in the
   network (when it receives an ACK packet that has the ECN-Echo flag
   set) is equivalent to the Fast Retransmit/Recovery algorithm (when
   there is a congestion loss) in NON-ECN-capable TCP i.e. the sender
   halves the congestion window cwnd and reduces the slow start
   threshold ssthresh. Fast Retransmit/Recovery is still available for
   ECN capable stacks for responding to three duplicate acknowledgments.

4. Experimental setup

   For testing purposes we have added ECN to the Linux TCP/IP stack,
   kernels version 2.0.32. 2.2.5, 2.3.43 (there were also earlier
   revisions of 2.3 which were tested).  The 2.0.32 implementation
   conforms to RFC 2481 [7] for the end systems only. We have also
   modified the code in the 2.1,2.2 and 2.3 cases for the router portion
   as well as end system to conform to the RFC. An outdated version of
   the 2.0 code is available at [18].  Note Linux version 2.0.32
   implements TCP Reno congestion control while kernels >= 2.2.0 default
   to New Reno but will opt for a SACK/FACK combo when the remote end
   understands SACK.  Our initial tests were carried out with the 2.0
   kernel at the end system and 2.1 (pre 2.2) for the router part.  The
   majority of the test results here apply to the 2.0 tests. We  did
   repeat these tests on a different testbed (move from Pentium to
   Pentium-II class machines)with faster machines for the 2.2 and 2.3
   kernels, so the comparisons on the 2.0 and 2.2/3 are not relative.

   We have updated this memo release to reflect the tests against SACK
   and New Reno.

4.1. Testbed setup

                                             -----      ----
                                            | ECN |    | ECN |
                                            | ON  |    | OFF |
          data direction ---->>              -----      ----
                                              |          |
      server                                  |          |
       ----        ------        ------       |          |
      |    |      |  R1  |      |  R2  |      |          |
      |    | -----|      | ---- |      | ----------------------
       ----        ------ ^      ------             |
                          ^                         |
                          |                        -----
      congestion point ___|                       |  C  |
                                                  |     |
                                                   -----

   The figure above shows our test setup.

   All the physical links are 10Mbps ethernet.  Using Class Based
   Queuing (CBQ) [22], packets from the data server are constricted to a
   1.5Mbps pipe at the router R1. Data is always retrieved from the
   server towards the clients labelled , "ECN ON", "ECN OFF", and "C".
   Since the pipe from the server is 10Mbps, this creates congestion at
   the exit from the router towards the clients for competing flows. The
   machines labeled "ECN ON" and "ECN OFF"  are running the same version

   of Linux and have exactly the same hardware configuration. The server
   is always ECN capable (and can handle NON ECN flows as well using the
   standard congestion algorithms). The machine labeled "C" is used to
   create congestion in the network. Router R2 acts as a path-delay
   controller.  With it we adjust the RTT the clients see.  Router R1
   has RED implemented in it and has capability for supporting ECN
   flows.  The path-delay router is a PC running the Nistnet [16]
   package on a Linux platform. The latency of the link for the
   experiments was set to be 20 millisecs.

4.2. Validating the Implementation

   We spent time validating that the implementation was conformant to
   the specification in RFC 2481. To do this, the popular tcpdump
   sniffer [24] was modified to show the packets being marked. We
   visually inspected tcpdump traces to validate the conformance to the
   RFC under a lot of different scenarios.  We also modified tcptrace
   [25] in order to plot the marked packets for visualization and
   analysis.

   Both tcpdump and tcptrace revealed that the implementation was
   conformant to the RFC.

4.3. Terminology used

   This section presents background terminology used in the next few
   sections.

   * Congesting flows: These are TCP flows that are started in the
   background so as to create congestion from R1 towards R2. We use the
   laptop labeled "C" to introduce congesting flows. Note that "C" as is
   the case with the other clients retrieves data from the server.

   * Low, Moderate and High congestion: For the case of low congestion
   we start two congesting flows in the background, for moderate
   congestion we start five congesting flows and for the case of high
   congestion we start ten congesting flows in the background.

   * Competing flows: These are the flows that we are interested in.
   They are either ECN TCP flows from/to "ECN ON" or NON ECN TCP flows
   from/to "ECN OFF".

   * Maximum drop rate: This is the RED parameter that sets the maximum
   probability of a packet being marked at the router. This corresponds
   to maxp as explained in Section 2.1.

   Our tests were repeated for varying levels of congestion with varying
   maximum drop rates. The results are presented in the subsequent
   sections.

   * Low, Medium and High drop probability: We use the term low
   probability to mean a drop probability maxp of 0.02, medium
   probability for 0.2 and high probability for 0.5. We also
   experimented with drop probabilities of 0.05, 0.1 and 0.3.

   * Goodput: We define goodput as the effective data rate as observed
   by the user, i.e., if we transmitted 4 data packets in which two of
   them were retransmitted packets, the efficiency is 50% and the
   resulting goodput is 2*packet size/time taken to transmit.

   * RED Region: When the router's average queue size is between minth
   and maxth we denote that we are operating in the RED region.

4.4. RED parameter selection

   In our initial testing we noticed that as we increase the number of
   congesting flows the RED queue degenerates into a simple Tail Drop
   queue.  i.e. the average queue exceeds the maximum threshold most of
   the times.  Note that this phenomena has also been observed by [5]
   who proposes a dynamic solution to alleviate it by adjusting the
   packet dropping probability "maxp" based on the past history of the
   average queue size.  Hence, it is necessary that in the course of our
   experiments the router operate in the RED region, i.e., we have to
   make sure that the average queue is maintained between minth and
   maxth. If this is not maintained, then the queue acts like a Tail
   Drop queue and the advantages of ECN diminish. Our goal is to
   validate ECN's benefits when used with RED at the router.  To ensure
   that we were operating in the RED region we monitored the average
   queue size and the actual queue size in times of low, moderate and
   high congestion and fine-tuned the RED parameters such that the
   average queue zones around the RED region before running the
   experiment proper.  Our results are, therefore, not influenced by
   operating in the wrong RED region.

5. The Experiments

   We start by making sure that the background flows do not bias our
   results by computing the fairness index [12] in Section 5.1. We
   proceed to carry out the experiments for bulk transfer presenting the
   results and analysis in Section 5.2. In Section 5.3 the results for
   transactional transfers along with analysis is presented.  More
   details on the experimental results can be found in [27].

5.1. Fairness

   In the course of the experiments we wanted to make sure that our
   choice of the type of background flows does not bias the results that
   we collect.  Hence we carried out some tests initially with both ECN
   and NON ECN flows as the background flows. We repeated the
   experiments for different drop probabilities and calculated the
   fairness index [12].  We also noticed (when there were equal number
   of ECN and NON ECN flows) that the number of packets dropped for the
   NON ECN flows was equal to the number of packets marked for the ECN
   flows, showing thereby that the RED algorithm was fair to both kind
   of flows.

   Fairness index: The fairness index is a performance metric described
   in [12].  Jain [12] postulates that the network is a multi-user
   system, and derives a metric to see how fairly each user is treated.
   He defines fairness as a function of the variability of throughput
   across users. For a given set of user throughputs (x1, x2...xn), the
   fairness index to the set is defined as follows:

   f(x1,x2,.....,xn) = square((sum[i=1..n]xi))/(n*sum[i=1..n]square(xi))

   The fairness index always lies between 0 and 1. A value of 1
   indicates that all flows got exactly the same throughput.  Each of
   the tests was carried out 10 times to gain confidence in our results.
   To compute the fairness index we used FTP to generate traffic.

   Experiment details: At time t = 0 we start 2 NON ECN FTP sessions in
   the background to create congestion. At time t=20 seconds we start
   two competing flows. We note the throughput of all the flows in the
   network and calculate the fairness index. The experiment was carried
   out for various maximum drop probabilities and for various congestion
   levels.  The same procedure is repeated with the background flows as
   ECN. The fairness index was fairly constant in both the cases when
   the background flows were ECN and NON ECN indicating that there was
   no bias when the background flows were either ECN or NON ECN.

   Max     Fairness                Fairness
   Drop    With BG                 With BG
   Prob    flows ECN               flows NON ECN

   0.02    0.996888                0.991946
   0.05    0.995987                0.988286
   0.1     0.985403                0.989726
   0.2     0.979368                0.983342

   With the observation that the nature of background flows does not
   alter the results, we proceed by using the background flows as NON
   ECN for the rest of the experiments.

5.2. Bulk transfers

   The metric we chose for bulk transfer is end user throughput.

   Experiment Details: All TCP flows used are RENO TCP. For the case of
   low congestion we start 2 FTP flows in the background at time 0. Then
   after about 20 seconds we start the competing flows, one data
   transfer to the ECN machine and the second to the NON ECN machine.
   The size of the file used is 20MB. For the case of moderate
   congestion we start 5 FTP flows in the background and for the case of
   high congestion we start 10 FTP flows in the background. We repeat
   the experiments for various maximum drop rates each repeated for a
   number of sets.

   Observation and Analysis:

   We make three key observations:

   1) As the congestion level increases, the relative advantage for ECN
   increases but the absolute advantage decreases (expected, since there
   are more flows competing for the same link resource). ECN still does
   better than NON ECN even under high congestion.  Infering a sample
   from the collected results: at maximum drop probability of 0.1, for
   example, the relative advantage of ECN increases from 23% to 50% as
   the congestion level increases from low to high.

   2) Maintaining congestion levels and varying the maximum drop
   probability (MDP) reveals that the relative advantage of ECN
   increases with increasing MDP. As an example, for the case of high
   congestion as we vary the drop probability from 0.02 to 0.5 the
   relative advantage of ECN increases from 10% to 60%.

   3) There were hardly any retransmissions for ECN flows (except the
   occasional packet drop in a minority of the tests for the case of
   high congestion and low maximum drop probability).

   We analyzed tcpdump traces for NON ECN with the help of tcptrace and
   observed that there were hardly any retransmits due to timeouts.
   (Retransmit due to timeouts are inferred by counting the number of 3
   DUPACKS retransmit and subtracting them from the total recorded
   number of retransmits).  This means that over a long period of time
   (as is the case of long bulk transfers), the data-driven loss
   recovery mechanism of the Fast Retransmit/Recovery algorithm is very
   effective.  The algorithm for ECN on congestion notification from ECE

   is the same as that for a Fast Retransmit for NON ECN. Since both are
   operating in the RED region, ECN barely gets any advantage over NON
   ECN from the signaling (packet drop vs. marking).

   It is clear, however, from the results that ECN flows benefit in bulk
   transfers.  We believe that the main advantage of ECN for bulk
   transfers is that less time is spent recovering (whereas NON ECN
   spends time retransmitting), and timeouts are avoided altogether.
   [23] has shown that even with RED deployed, TCP RENO could suffer
   from multiple packet drops within the same window of data, likely to
   lead to multiple congestion reactions or timeouts (these problems are
   alleviated by ECN). However, while TCP Reno has performance problems
   with multiple packets dropped in a window of data, New Reno and SACK
   have no such problems.

   Thus, for scenarios with very high levels of congestion, the
   advantages of ECN for TCP Reno flows could be more dramatic than the
   advantages of ECN for NewReno or SACK flows.  An important
   observation to make from our results is that we do not notice
   multiple drops within a single window of data. Thus, we would expect
   that our results are not heavily influenced by Reno's performance
   problems with multiple packets dropped from a window of data.  We
   repeated these tests with ECN patched newer Linux kernels. As
   mentioned earlier these kernels would use a SACK/FACK combo with a
   fallback to New Reno.  SACK can be selectively turned off (defaulting
   to New Reno).  Our results indicate that ECN still improves
   performance for the bulk transfers. More results are available in the
   pdf version[27]. As in 1) above, maintaining a maximum drop
   probability of 0.1 and increasing the congestion level, it is
   observed that ECN-SACK improves performance from about 5% at low
   congestion to about 15% at high congestion. In the scenario where
   high congestion is maintained and the maximum drop probability is
   moved from 0.02 to 0.5, the relative advantage of ECN-SACK improves
   from 10% to 40%.  Although this numbers are lower than the ones
   exhibited by Reno, they do reflect the improvement that ECN offers
   even in the presence of robust recovery mechanisms such as SACK.

5.3. Transactional transfers

   We model transactional transfers by sending a small request and
   getting a response from a server before sending the next request. To
   generate transactional transfer traffic we use Netperf [17] with the
   CRR (Connect Request Response) option.  As an example let us assume
   that we are retrieving a small file of say 5 - 20 KB, then in effect
   we send a small request to the server and the server responds by
   sending us the file. The transaction is complete when we receive the
   complete file. To gain confidence in our results we carry the
   simulation for about one hour. For each test there are a few thousand

   of these requests and responses taking place.  Although not exactly
   modeling HTTP 1.0 traffic, where several concurrent sessions are
   opened, Netperf-CRR is nevertheless a close approximation.  Since
   Netperf-CRR waits for one connection to complete before opening the
   next one (0 think time), that single connection could be viewed as
   the slowest response in the set of the opened concurrent sessions (in
   HTTP).  The transactional data sizes were selected based on [2] which
   indicates that the average web transaction was around 8 - 10 KB; The
   smaller (5KB) size was selected to guestimate the size of
   transactional processing that may become prevalent with policy
   management schemes in the diffserv [4] context.  Using Netperf we are
   able to initiate these kind of transactional transfers for a variable
   length of time. The main metric of interest in this case is the
   transaction rate, which is recorded by Netperf.

   * Define Transaction rate as: The number of requests and complete
   responses for a particular requested size that we are able to do per
   second. For example if our request is of 1KB and the response is 5KB
   then we define the transaction rate as the number of such complete
   transactions that we can accomplish per second.

   Experiment Details: Similar to the case of bulk transfers we start
   the background FTP flows to introduce the congestion in the network
   at time 0. About 20 seconds later we start the transactional
   transfers and run each test for three minutes. We record the
   transactions per second that are complete. We repeat the test for
   about an hour and plot the various transactions per second, averaged
   out over the runs. The experiment is repeated for various maximum
   drop probabilities, file sizes and various levels of congestion.

   Observation and Analysis

   There are three key observations:

   1) As congestion increases (with fixed drop probability) the relative
   advantage for ECN increases (again the absolute advantage does not
   increase since more flows are sharing the same bandwidth). For
   example, from the results, if we consider the 5KB transactional flow,
   as we increase the congestion from medium congestion (5 congesting
   flows) to high congestion (10 congesting flows) for a maximum drop
   probability of 0.1 the relative gain for ECN increases from 42% to
   62%.

   2) Maintaining the congestion level while adjusting the maximum drop
   probability indicates that the relative advantage for ECN flows
   increase.  From the case of high congestion for the 5KB flow we

   observe that the number of transactions per second increases from 0.8
   to 2.2 which corresponds to an increase in relative gain for ECN of
   20% to 140%.

   3) As the transactional data size increases, ECN's advantage
   diminishes because the probability of recovering from a Fast
   Retransmit increases for NON ECN. ECN, therefore, has a huge
   advantage as the transactional data size gets smaller as is observed
   in the results.  This can be explained by looking at TCP recovery
   mechanisms.  NON ECN in the short flows depends, for recovery, on
   congestion signaling via receiving 3 duplicate ACKs, or worse by a
   retransmit timer expiration, whereas ECN depends mostly on the TCP-
   ECE flag. This is by design in our experimental setup.  [3] shows
   that most of the TCP loss recovery in fact happens in timeouts for
   short flows. The effectiveness of the Fast Retransmit/Recovery
   algorithm is limited by the fact that there might not be enough data
   in the pipe to elicit 3 duplicate ACKs.  TCP RENO needs at least 4
   outstanding packets to recover from losses without going into a
   timeout. For 5KB (4 packets for MTU of 1500Bytes) a NON ECN flow will
   always have to wait for a retransmit timeout if any of its packets
   are lost. ( This timeout could only have been avoided if the flow had
   used an initial window of four packets, and the first of the four
   packets was the packet dropped).  We repeated these experiments with
   the kernels implementing SACK/FACK and New Reno algorithms. Our
   observation was that there was hardly any difference with what we saw
   with Reno. For example in the case of SACK-ECN enabling: maintaining
   the maximum drop probability to 0.1 and increasing the congestion
   level for the 5KB transaction we noticed that the relative gain for
   the ECN enabled flows increases from 47-80%.  If we maintain the
   congestion level for the 5KB transactions and increase the maximum
   drop probabilities instead, we notice that SACKs performance
   increases from 15%-120%.  It is fair to comment that the difference
   in the testbeds (different machines, same topology) might have
   contributed to the results; however, it is worth noting that the
   relative advantage of the SACK-ECN is obvious.

6. Conclusion

   ECN enhancements improve on both bulk and transactional TCP traffic.
   The improvement is more obvious in short transactional type of flows
   (popularly referred to as mice).

   * Because less retransmits happen with ECN, it means less traffic on
   the network. Although the relative amount of data retransmitted in
   our case is small, the effect could be higher when there are more
   contributing end systems. The absence of retransmits also implies an
   improvement in the goodput. This becomes very important for scenarios

   where bandwidth is expensive such as in low bandwidth links.  This
   implies also that ECN lends itself well to applications that require
   reliability but would prefer to avoid unnecessary retransmissions.

   * The fact that ECN avoids timeouts by getting faster notification
   (as opposed to traditional packet dropping inference from 3 duplicate
   ACKs or, even worse, timeouts) implies less time is spent during
   error recovery - this also improves goodput.

   * ECN could be used to help in service differentiation where the end
   user is able to "probe" for their target rate faster. Assured
   forwarding [1] in the diffserv working group at the IETF proposes
   using RED with varying drop probabilities as a service
   differentiation mechanism.  It is possible that multiple packets
   within a single window in TCP RENO could be dropped even in the
   presence of RED, likely leading into timeouts [23]. ECN end systems
   ignore multiple notifications, which help in countering this scenario
   resulting in improved goodput. The ECN end system also ends up
   probing the network faster (to reach an optimal bandwidth). [23] also
   notes that RENO is the most widely deployed TCP implementation today.

   It is clear that the advent of policy management schemes introduces
   new requirements for transactional type of applications, which
   constitute a very short query and a response in the order of a few
   packets. ECN provides advantages to transactional traffic as we have
   shown in the experiments.

7. Acknowledgements

   We would like to thank Alan Chapman, Ioannis Lambadaris, Thomas Kunz,
   Biswajit Nandy, Nabil Seddigh, Sally Floyd, and Rupinder Makkar for
   their helpful feedback and valuable suggestions.

8. Security Considerations

   Security considerations are as discussed in section 9 of RFC 2481.

9. References

   [1]  Heinanen, J., Finland, T., Baker, F., Weiss, W. and J.
        Wroclawski, "Assured Forwarding PHB Group", RFC 2597, June 1999.

   [2]  B.A. Mat. "An empirical model of HTTP network traffic."  In
        proceedings INFOCOMM'97.

   [3]  Balakrishnan H., Padmanabhan V., Seshan S., Stemn M. and Randy
        H. Katz, "TCP Behavior of a busy Internet Server: Analysis and
        Improvements", Proceedings of IEEE Infocom, San Francisco, CA,
        USA, March '98
        http://nms.lcs.mit.edu/~hari/papers/infocom98.ps.gz

   [4]  Blake, S., Black, D., Carlson, M., Davies, E., Wang, Z. and W.
        Weiss, "An Architecture for Differentiated Services", RFC 2475,
        December 1998.

   [5]  W. Feng, D. Kandlur, D. Saha, K. Shin, "Techniques for
        Eliminating Packet Loss in Congested TCP/IP Networks", U.
        Michigan CSE-TR-349-97, November 1997.

   [6]  S. Floyd. "TCP and Explicit Congestion Notification." ACM
        Computer Communications Review, 24, October 1994.

   [7]  Ramakrishnan, K. and S. Floyd, "A Proposal to add Explicit
        Congestion Notification (ECN) to IP", RFC 2481, January 1999.

   [8]  Kevin Fall, Sally Floyd, "Comparisons of Tahoe, RENO and Sack
        TCP", Computer  Communications Review, V. 26 N. 3, July 1996,
        pp. 5-21

   [9]  S. Floyd and V. Jacobson. "Random Early Detection Gateways for
        Congestion Avoidance". IEEE/ACM Transactions on Networking,
        3(1), August 1993.

   [10] E. Hashem. "Analysis of random drop for gateway congestion
        control." Rep. Lcs tr-465, Lav. Fot Comput. Sci., M.I.T., 1989.

   [11] V. Jacobson. "Congestion Avoidance and Control." In Proceedings
        of SIGCOMM '88, Stanford, CA, August 1988.

   [12] Raj Jain, "The art of computer systems performance analysis",
        John Wiley and sons QA76.9.E94J32, 1991.

   [13] T. V. Lakshman, Arnie Neidhardt, Teunis Ott, "The Drop From
        Front Strategy in TCP Over ATM and Its Interworking with Other
        Control Features", Infocom 96, MA28.1.

   [14] P. Mishra and H. Kanakia. "A hop by hop rate based congestion
        control scheme." Proc. SIGCOMM '92, pp. 112-123, August 1992.

   [15] Floyd, S. and T. Henderson, "The NewReno Modification to TCP's
        Fast Recovery Algorithm", RFC 2582, April 1999.

   [16] The NIST Network Emulation Tool
        http://www.antd.nist.gov/itg/nistnet/

   [17] The network performance tool
        http://www.netperf.org/netperf/NetperfPage.html

   [18] ftp://ftp.ee.lbl.gov/ECN/ECN-package.tgz

   [19] Braden, B., Clark, D., Crowcroft, J., Davie, B., Deering, S.,
        Estrin, D., Floyd, S., Jacobson, V., Minshall, G., Partridge,
        C., Peterson, L., Ramakrishnan, K., Shenker, S., Wroclawski, J.
        and L. Zhang, "Recommendations on Queue Management and
        Congestion Avoidance in the Internet", RFC 2309, April 1998.

   [20] K. K. Ramakrishnan and R. Jain. "A Binary feedback scheme for
        congestion avoidance in computer networks." ACM Trans. Comput.
        Syst.,8(2):158-181, 1990.

   [21] Mathis, M., Mahdavi, J., Floyd, S. and A. Romanow, "TCP
        Selective Acknowledgement Options", RFC 2018, October 1996.

   [22] S. Floyd and V. Jacobson, "Link sharing and Resource Management
        Models for packet  Networks", IEEE/ACM Transactions on
        Networking, Vol. 3 No.4, August 1995.

   [23] Prasad Bagal, Shivkumar Kalyanaraman, Bob Packer, "Comparative
        study of RED, ECN and TCP Rate Control".
        http://www.packeteer.com/technology/Pdf/packeteer-final.pdf

   [24] tcpdump, the protocol packet capture & dumper program.
        ftp://ftp.ee.lbl.gov/tcpdump.tar.Z

   [25] TCP dump file analysis tool:
        http://jarok.cs.ohiou.edu/software/tcptrace/tcptrace.html

   [26] Thompson K., Miller, G.J., Wilder R., "Wide-Area Internet
        Traffic Patterns and Characteristics". IEEE Networks Magazine,
        November/December 1997.

   [27] http://www7.nortel.com:8080/CTL/ecnperf.pdf

10. Authors' Addresses

   Jamal Hadi Salim
   Nortel Networks
   3500 Carling Ave
   Ottawa, ON, K2H 8E9
   Canada

   EMail: hadi@nortelnetworks.com

   Uvaiz Ahmed
   Dept. of Systems and Computer Engineering
   Carleton University
   Ottawa
   Canada

   EMail: ahmed@sce.carleton.ca

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Acknowledgement

   Funding for the RFC Editor function is currently provided by the
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