[e2e] Why do we need congestion control?

Jon Crowcroft Jon.Crowcroft at cl.cam.ac.uk
Wed Mar 6 05:43:56 PST 2013


there's no free lunch - if the queue keeps being filled, you'll get increasing
packet loss - to cover the loss, you'll need more redundency in your erasure
coding - this will cost you more delay at source (in constructing more
redundency) and more overhead in the net, which is a positive feedback loop,
just like retransmitting a whole window into a congested queue/path - a 
"vicious cycle" as opposed to a virtuous one...

so all that erasure codes buy you is delaying the point when you go over the
clif

In missive <CANZhn5Gqyw6-XpaB2-XKmbahKidOap+ZDqGAJ2NqdMBxmYAP-w at mail.gmail.com>, shun cai
 typed:

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 >>As discussed in chapter 1 of your PhD thesis,  when network is congested,
 >>retransmission dominate the traffic and effective throughput diminshes
 >>rapidly, leading to a deteriorating situation. This can be illustrated in
 >>the well known figure with two turning points Knee and Cliff.
 >>
 >>
 >>     I wonder, however, does the situation the same if rateless erasure
 >>code (say fountain codes) is used?  As with erasure code, no ACK and
 >>retransmission is needed except when the whole file is completed. So even
 >>heavy loaded, the network is still busy with effective data packet, right?
 >>Although queueing delay will increase, I believe that  the network
 >>throughput  will not  suffer the plunge as un-coded network.
 >>
 >>
 >>
 >>Kara
 >>
 >>
 >>
 >>
 >>
 >>
 >>2013/3/5 Srinivasan Keshav <keshav at uwaterloo.ca>
 >>
 >>> To answer this question, I put together some slides for a presentation at
 >>> the IRTF ICCRG Workshop in 2007 [1]. In a nutshell, to save costs, we
 >>> always size a shared resource (such as a link or a router) smaller than the
 >>> sum of peak demands. This can result in transient or persistent overloads,
 >>> reducing user-perceived performance. Transient overloads are easily
 >>> relieved by a buffer, but persistent overload requires reductions of source
 >>> loads, which is the role of congestion control. Lacking congestion control,
 >>> or worse, with an inappropriate response to a performance problem (such as
 >>> by increasing the load), shared network resources are always overloaded
 >>> leading to delays, losses, and eventually collapse, where every packet that
 >>> is sent is a retransmission and no source makes progress. A more detailed
 >>> description can also be found in chapter 1 of my PhD thesis [2].
 >>>
 >>> Incidentally, the distributed optimization approach that Jon mentioned is
 >>> described beautifully in [3].
 >>>
 >>> hope this helps,
 >>> keshav
 >>>
 >>> [1] Congestion and Congestion Control, Presentation at IRTF ICCRG
 >>> Workshop, PFLDnet, 2007, Los Angeles (California), USA, February 2007.
 >>> http://blizzard.cs.uwaterloo.ca/keshav/home/Papers/data/07/congestion.pdf
 >>>
 >>> [2] S. Keshav, Congestion Control in Computer Networks PhD Thesis,
 >>> published as UC Berkeley TR-654, September 1991
 >>> http://blizzard.cs.uwaterloo.ca/keshav/home/Papers/data/91/thesis/ch1.pdf
 >>>
 >>> [3] Palomar, Daniel P., and Mung Chiang. "A tutorial on decomposition
 >>> methods for network utility maximization." Selected Areas in
 >>> Communications, IEEE Journal on 24.8 (2006): 1439-1451.
 >>> http://www.princeton.edu/~chiangm/decomptutorial.pdf
 >>>
 >>>
 >>>
 >>
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 >>mCC" alt=3D""><br>
 >><br>=A0=A0=A0=A0 I wonder, however, does the situation the same if rateless=
 >> erasure code (say fountain codes) is used?=A0 As with erasure code, no ACK=
 >> and retransmission is needed except when the whole file is completed. So e=
 >>ven heavy loaded, the network is still busy with effective data packet, rig=
 >>ht?=A0 Although queueing delay will increase, I believe that=A0 the network=
 >> throughput=A0 will not=A0 suffer the plunge as un-coded network. <br>
 >><br>=A0<br><br>Kara<br><br><br><br><br><br><br><div class=3D"gmail_quote">2=
 >>013/3/5 Srinivasan Keshav <span dir=3D"ltr">&lt;<a href=3D"mailto:keshav at uw=
 >>aterloo.ca" target=3D"_blank">keshav at uwaterloo.ca</a>&gt;</span><br><blockq=
 >>uote class=3D"gmail_quote" style=3D"margin:0 0 0 .8ex;border-left:1px #ccc =
 >>solid;padding-left:1ex">
 >>
 >>To answer this question, I put together some slides for a presentation at t=
 >>he IRTF ICCRG Workshop in 2007 [1]. In a nutshell, to save costs, we always=
 >> size a shared resource (such as a link or a router) smaller than the sum o=
 >>f peak demands. This can result in transient or persistent overloads, reduc=
 >>ing user-perceived performance. Transient overloads are easily relieved by =
 >>a buffer, but persistent overload requires reductions of source loads, whic=
 >>h is the role of congestion control. Lacking congestion control, or worse, =
 >>with an inappropriate response to a performance problem (such as by increas=
 >>ing the load), shared network resources are always overloaded leading to de=
 >>lays, losses, and eventually collapse, where every packet that is sent is a=
 >> retransmission and no source makes progress. A more detailed description c=
 >>an also be found in chapter 1 of my PhD thesis [2].<br>
 >>
 >>
 >><br>
 >>Incidentally, the distributed optimization approach that Jon mentioned is d=
 >>escribed beautifully in [3].<br>
 >><br>
 >>hope this helps,<br>
 >>keshav<br>
 >><br>
 >>[1] Congestion and Congestion Control, Presentation at IRTF ICCRG Workshop,=
 >> PFLDnet, 2007, Los Angeles (California), USA, February 2007.<br>
 >><a href=3D"http://blizzard.cs.uwaterloo.ca/keshav/home/Papers/data/07/conge=
 >>stion.pdf" target=3D"_blank">http://blizzard.cs.uwaterloo.ca/keshav/home/Pa=
 >>pers/data/07/congestion.pdf</a><br>
 >><br>
 >>[2] S. Keshav, Congestion Control in Computer Networks PhD Thesis, publishe=
 >>d as UC Berkeley TR-654, September 1991<br>
 >><a href=3D"http://blizzard.cs.uwaterloo.ca/keshav/home/Papers/data/91/thesi=
 >>s/ch1.pdf" target=3D"_blank">http://blizzard.cs.uwaterloo.ca/keshav/home/Pa=
 >>pers/data/91/thesis/ch1.pdf</a><br>
 >><br>
 >>[3] Palomar, Daniel P., and Mung Chiang. &quot;A tutorial on decomposition =
 >>methods for network utility maximization.&quot; Selected Areas in Communica=
 >>tions, IEEE Journal on 24.8 (2006): 1439-1451.<br>
 >><a href=3D"http://www.princeton.edu/~chiangm/decomptutorial.pdf" target=3D"=
 >>_blank">http://www.princeton.edu/~chiangm/decomptutorial.pdf</a><br>
 >><br>
 >><br>
 >></blockquote></div><br>
 >>
 >>--f46d0444e84964e1a304d740bdcd--

 cheers

   jon



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