rfc2963.txt

著名的RFC文档,其中有一些文档是已经翻译成中文的的.

   The customer routers are connected with 34 Mbps links to the backbone
   network which is, in our case, composed of a single bottleneck 70
   Mbps link between the edge routers ER1 and ER2.  The delay on all the
   customer-edge 34 Mbps links has been set to 2.5 msec to model a MAN
   or small WAN environment.  These links and the customer routers are
   not a bottleneck in our environment and no losses occurs inside the
   edge routers.  The customer routers are equipped with a trTCM
   [Heinanen2] and mark the incoming traffic.  The parameters of the
   trTCM are shown in table A.1.








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        Table A.1: configurations of the trTCMs

        Router          CIR               PIR             Line Rate
        C1              2 Mbps            4 Mbps          34 Mbps
        C2              4 Mbps            8 Mbps          34 Mbps
        C3              6 Mbps           12 Mbps          34 Mbps
        C4              8 Mbps           16 Mbps          34 Mbps
        C5             10 Mbps           20 Mbps          34 Mbps
        C6              2 Mbps            4 Mbps          34 Mbps
        C7              4 Mbps            8 Mbps          34 Mbps
        C8              6 Mbps           12 Mbps          34 Mbps
        C9              8 Mbps           16 Mbps          34 Mbps
        C10            10 Mbps           20 Mbps          34 Mbps

   All customer routers are equipped with a trTCM where the CIR are 2
   Mbps for router C1 and C6, 4 Mbps for C2 and C7, 6 Mbps for C3 and
   C8, 8 Mbps for C4 and C9 and 10 Mbps for C5 and C10.  Routers C6-C10
   also contain a trRAS in addition to the trTCM while routers C1-C5
   only contain a trTCM.  In all simulations, the PIR is always twice as
   large as the CIR.  Also the PBS is the double of the CBS.  The CBS
   will be varied in the different simulation runs.

   The edge routers, ER1 and ER2, are connected with a 70 Mbps link
   which is the bottleneck link in our environment.  These two routers
   implement the RIO algorithm [Clark] that we have extended to support
   three drop priorities instead of two.  The thresholds of the
   parameters are 100 and 200 packets (minimum and maximum threshold,
   respectively) for the red packets, 200 and 400 packets for the yellow
   packets and 400 and 800 for the green packets.  These thresholds are
   reasonable since there are 100 TCP connections crossing each edge
   router.  The parameter maxp of RIO for green, yellow and red are
   respectively set to 0.02, 0.05, and 0.1.  The weight to calculate the
   average queue length which is used by RED or RIO is set to 0.002
   [Floyd].

   The simulated time is set to 102 seconds where the first two seconds
   are not used to gather TCP statistics (the so-called warm-up time)
   such as goodput.

A.2 Simulation results for the trRAS

   For our first simulations, we consider that routers C1-C5 only
   utilize a trTCM while routers C6-C10 utilize a rate adaptive shaper
   in conjunction with a trTCM. All routers use a CBS of 3 KBytes.  In
   table A.2, we show the total throughput achieved by the workstations
   attached to each LAN as well as the total throughput for the green
   and the yellow packets as a function of the CIR of the trTCM used on
   the customer router attached to this LAN.  The throughput of the red



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   packets is equal to the difference between the total traffic and the
   green and the yellow traffic.  In table A.3, we show the total
   throughput achieved by the workstations attached to customer routers
   with a rate adaptive shaper.

        Table A.2: throughput in Mbps for the unshaped traffic.

                      green           yellow          total
        2Mbps [C1]    1.10            0.93            2.25
        4Mbps [C2]    2.57            1.80            4.55
        6Mbps [C3]    4.10            2.12            6.39
        8Mbps [C4]    5.88            2.32            8.33
        10Mbps [C5]   7.57            2.37            10.0

        Table A.3: throughput in Mbps for the adaptively shaped
        traffic.
                            green           yellow          total
        2Mbps [C6]    2.00            1.69            3.71
        4Mbps [C7]    3.97            2.34            6.33
        6Mbps [C8]    5.93            2.23            8.17
        8Mbps [C9]    7.84            2.28            10.1
        10Mbps [C10]  9.77            2.14            11.9

   This first simulation shows clearly that the workstations attached to
   an edge router with a rate adaptive shaper have a clear advantage,
   from a performance point of view, with respect to workstations
   attached to an edge router with only a trTCM.  The performance
   improvement is the result of the higher proportion of packets marked
   as green by the edge routers when the rate adaptive shaper is used.

   To evaluate the impact of the CBS on the TCP goodput, we did
   additional simulations were we varied the CBS of all customer
   routers.

   Table A.4 shows the total goodput for workstations attached to,
   respectively, routers C1 (trTCM with 2 Mbps CIR, no adaptive
   shaping), C6 (trRAS with 2 Mbps CIR and adaptive shaping), C3 (trTCM
   with 6 Mbps CIR, no adaptive shaping), and C8 (trRAS with 6 Mbps CIR
   and adaptive shaping) for various values of the CBS.  From this
   table, it is clear that the rate adaptive shapers provide a
   performance benefit when the CBS is small.  With a very large CBS,
   the performance decreases when the shaper is in use.  However, a CBS
   of a few hundred KBytes is probably too large in many environments.








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      Table A.4: goodput in Mbps (link rate is 70 Mbps) versus CBS
      in KBytes.
      CBS  2_Mbps_unsh     2_Mbps_sh      6_Mbps_unsh    6_Mbps_sh
      3       1.88            3.49          5.91           7.77
      10      2.97            2.91          6.76           7.08
      25      3.14            2.78          7.07           6.73
      50      3.12            2.67          7.20           6.64
      75      3.18            2.56          7.08           6.58
      100     3.20            2.64          7.00           6.62
      150     3.21            2.54          7.11           6.52
      200     3.26            2.57          7.07           6.53
      300     3.19            2.53          7.13           6.49
      400     3.13            2.48          7.18           6.43

A.3 Simulation results for the Green trRAS

   We use the same scenario as in A.2 but now we use the Green trRAS
   (G-trRAS).

   Table A.5 and Table A.6 show the results of the same scenario as for
   Table A.2 and Table A.3 but the shaper is now the G-trRAS.  We see
   that the shaped traffic performs again much better, also compared to
   the previous case (i.e. where the trRAS was used).  This is because
   the amount of yellow traffic increases with the expense of a slight
   decrease in the amount of green traffic.  This can be explained by
   the fact that the G-trRAS introduces some burstiness.

      Table A.5: throughput in Mbps for the unshaped traffic.
                    green           yellow          total
      2Mbps [C1]    1.10            0.95            2.26
      4Mbps [C2]    2.41            1.66            4.24
      6Mbps [C3]    3.94            1.97            6.07
      8Mbps [C4]    5.72            2.13            7.96
      10Mbps [C5]   7.25            2.29            9.64

      Table A.6: throughput in Mbps for the adaptively shaped
      traffic.
                    green           yellow          total
      2Mbps [C6]    1.92            1.75            3.77
      4Mbps [C7]    3.79            3.24            7.05
      6Mbps [C8]    5.35            3.62            8.97
      8Mbps [C9]    6.96            3.48            10.4
      10Mbps [C10]  8.69            3.06            11.7

   The impact of the CBS is shown in Table A.7 which is the same
   scenario as Table A.4 with the only difference that the shaper is now
   the G-trRAS.  We see that the shaped traffic performs much better
   than the unshaped traffic when the CBS is small.  When the CBS is



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   large, the shaped and unshaped traffic performs more or less the
   same.  This is in contrast with the trRAS, where the performance of
   the shaped traffic was slightly worse in case of a large CBS.

   Table A.7: goodput in Mbps (link rate is 70 Mbps) versus CBS
   in KBytes.

      CBS  2_Mbps_unsh     2_Mbps_sh      6_Mbps_unsh    6_Mbps_sh
      3       1.90            3.44          5.62           8.44
      10      2.95            3.30          6.70           7.20
      25      2.98            3.01          7.03           6.93
      50      3.06            2.85          6.81           6.84
      75      3.08            2.80          6.87           6.96
      100     2.99            2.78          6.85           6.88
      150     2.98            2.70          6.80           6.81
      200     2.96            2.70          6.82           6.97
      300     2.94            2.70          6.83           6.86
      400     2.86            2.62          6.83           6.84

A.4 Conclusion simulations

   From these simulations, we see that the shaped traffic has much
   higher throughput compared to the unshaped traffic when the CBS was
   small.  When the CBS is large, the shaped traffic performs slightly
   less than the unshaped traffic due to the delay in the shaper.  The
   G-trRAS solves this problem.  Additional simulation results may be
   found in [Cnodder]
























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Authors' Addresses

   Olivier Bonaventure
   Infonet research group
   Institut d'Informatique (CS Dept)
   Facultes Universitaires Notre-Dame de la Paix
   Rue Grandgagnage 21, B-5000 Namur, Belgium.

   EMail: [e-mail]
   URL:   http://www.infonet.fundp.ac.be


   Stefaan De Cnodder
   Alcatel Network Strategy Group
   Fr. Wellesplein 1, B-2018 Antwerpen, Belgium.

   Phone:  [phone]
   Fax:    [phone]
   EMail:  [e-mail]
































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Acknowledgement

   Funding for the RFC Editor function is currently provided by the
   Internet Society.



















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