[e2e] Why do we need congestion control?
shun cai
vitacaishun at gmail.com
Wed Mar 6 06:49:09 PST 2013
1. With fountain codes, for k original packets, infinite number of encoded
packets can be generated and the receiver can recover the k packets with
any k(1+ e) coded packets.
2. Suppose all flows are coded, and the short term packet arrival rate of
some flows exceeds the service rate of the network. Overflow and packet
loss could happen wheather the packet coded or not.
My point is, I do not think the effective throughput will plunge with
coded flows , although the buffers are full. The network is in full swing
and the worst delay of a flow is the sum of the maximum queue time of the
routers along the path.
I think there will not be a cliff in the throughput-load figure, insdead,
the curve will go beyond the Knee and reach a constant throughput.
2013/3/6 Jon Crowcroft <Jon.Crowcroft at cl.cam.ac.uk>
> 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:
>
> >>--f46d0444e84964e1a304d740bdcd
> >>Content-Type: text/plain; charset=ISO-8859-1
> >>
> >>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
> >>>
> >>>
> >>>
> >>
> >>--f46d0444e84964e1a304d740bdcd
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> >>As discussed in chapter 1 of your PhD thesis,=A0 when network is
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> >> retransmission dominate the traffic and effective throughput diminshes
> rap=
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> the =
> >>well known figure with two turning points Knee and Cliff. <br>
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> >>+fLmuru5dN1okGIuvVY6VbZG0ZI9c4+N3oq0LHl5Lengj8ICx4qnr23dd3SizZ/6JaysCXtmmNc=
>
> >>QlVcfGlyW0Mzs+8HVUjCTr9OnTcRwH8rWmpqawsLCoqKi0tLSoqOjVq1elpaU9PT0sFqugoCAiI=
>
> >>iI1NbW2tra/v5/BYKSlpXl4eISFhRUUFCQmJsbGxnZ3d3d1dcXHx5NIpOjo6KCgoIKCAqBavOt2=
>
> >>Tz5G5SuNRiMIQoLtymE95rHd+ZU2FfGGidQjRUUmHZsfrF5z96elt/653Giu6v01mqS9ak9+0/c=
>
> >>7Hfo8lMakfTDEjorXyVeIkYZtsKFFEATwBCAEJ3kQBDE4OAgj+IaGhnDkVFscx9Go1D8fZV/H1+=
>
> >>vXr0s0OPVUWHMa7g22W7bmnKO6aTxOOZquFrxLyn3z7JuzFjz4xx6yhpyH/GG/ww6JDuXN5aN2+=
>
> >>gcQBCExGsbzQ/z17nyvY/mfGyO7cdu2bf+Vr+XW9PbHPfUOtBdWNbV3mAnGKZrr95NKSWH1Ida5=
>
> >>9w6Faxgnnfg1UMkixSqrPquzv/Ndt0V8MWG+foAQRnbj5s2b/8vXZF963n1esX1/jkN/qOVQ1G3=
>
> >>89u9P5n7xxZGNR112hz+7kRF7vSTfpSnc4nkqOSOFkuLzxMdrBLw/4ANej5GEGSMmGUYmA7i7u7=
>
> >>u6uoaFhbW0tBCjxsd+wAeILT7w9QPeJ3zg6we8T/jA1w94n/D/AWQx0gRPTdK4AAAAAElFTkSuQ=
> >>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"><<a href=3D"mailto:
> keshav at uw=
> >>aterloo.ca" target=3D"_blank">keshav at uwaterloo.ca
> </a>></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. "A tutorial on
> decomposition =
> >>methods for network utility maximization." 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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