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.<br><br>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. <br>
<br> 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. <br>
<br>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. <br><br> <br> <br><br> <br><br><br><br><div class="gmail_quote">2013/3/6 Jon Crowcroft <span dir="ltr"><<a href="mailto:Jon.Crowcroft@cl.cam.ac.uk" target="_blank">Jon.Crowcroft@cl.cam.ac.uk</a>></span><br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">there's no free lunch - if the queue keeps being filled, you'll get increasing<br>
packet loss - to cover the loss, you'll need more redundency in your erasure<br>
coding - this will cost you more delay at source (in constructing more<br>
redundency) and more overhead in the net, which is a positive feedback loop,<br>
just like retransmitting a whole window into a congested queue/path - a<br>
"vicious cycle" as opposed to a virtuous one...<br>
<br>
so all that erasure codes buy you is delaying the point when you go over the<br>
clif<br>
<br>
In missive <<a href="mailto:CANZhn5Gqyw6-XpaB2-XKmbahKidOap%2BZDqGAJ2NqdMBxmYAP-w@mail.gmail.com">CANZhn5Gqyw6-XpaB2-XKmbahKidOap+ZDqGAJ2NqdMBxmYAP-w@mail.gmail.com</a>>, shun cai<br>
typed:<br>
<br>
>>--f46d0444e84964e1a304d740bdcd<br>
>>Content-Type: text/plain; charset=ISO-8859-1<br>
<div class="im"> >><br>
>>As discussed in chapter 1 of your PhD thesis, when network is congested,<br>
>>retransmission dominate the traffic and effective throughput diminshes<br>
>>rapidly, leading to a deteriorating situation. This can be illustrated in<br>
>>the well known figure with two turning points Knee and Cliff.<br>
>><br>
>><br>
</div><div><div class="h5"> >> I wonder, however, does the situation the same if rateless erasure<br>
>>code (say fountain codes) is used? As with erasure code, no ACK and<br>
>>retransmission is needed except when the whole file is completed. So even<br>
>>heavy loaded, the network is still busy with effective data packet, right?<br>
>>Although queueing delay will increase, I believe that the network<br>
>>throughput will not suffer the plunge as un-coded network.<br>
>><br>
>><br>
>><br>
>>Kara<br>
>><br>
>><br>
>><br>
>><br>
>><br>
>><br>
>>2013/3/5 Srinivasan Keshav <<a href="mailto:keshav@uwaterloo.ca">keshav@uwaterloo.ca</a>><br>
>><br>
>>> To answer this question, I put together some slides for a presentation at<br>
>>> the IRTF ICCRG Workshop in 2007 [1]. In a nutshell, to save costs, we<br>
>>> always size a shared resource (such as a link or a router) smaller than the<br>
>>> sum of peak demands. This can result in transient or persistent overloads,<br>
>>> reducing user-perceived performance. Transient overloads are easily<br>
>>> relieved by a buffer, but persistent overload requires reductions of source<br>
>>> loads, which is the role of congestion control. Lacking congestion control,<br>
>>> or worse, with an inappropriate response to a performance problem (such as<br>
>>> by increasing the load), shared network resources are always overloaded<br>
>>> leading to delays, losses, and eventually collapse, where every packet that<br>
>>> is sent is a retransmission and no source makes progress. A more detailed<br>
>>> description can also be found in chapter 1 of my PhD thesis [2].<br>
>>><br>
>>> Incidentally, the distributed optimization approach that Jon mentioned is<br>
>>> described beautifully in [3].<br>
>>><br>
>>> hope this helps,<br>
>>> keshav<br>
>>><br>
>>> [1] Congestion and Congestion Control, Presentation at IRTF ICCRG<br>
>>> Workshop, PFLDnet, 2007, Los Angeles (California), USA, February 2007.<br>
>>> <a href="http://blizzard.cs.uwaterloo.ca/keshav/home/Papers/data/07/congestion.pdf" target="_blank">http://blizzard.cs.uwaterloo.ca/keshav/home/Papers/data/07/congestion.pdf</a><br>
>>><br>
>>> [2] S. Keshav, Congestion Control in Computer Networks PhD Thesis,<br>
>>> published as UC Berkeley TR-654, September 1991<br>
>>> <a href="http://blizzard.cs.uwaterloo.ca/keshav/home/Papers/data/91/thesis/ch1.pdf" target="_blank">http://blizzard.cs.uwaterloo.ca/keshav/home/Papers/data/91/thesis/ch1.pdf</a><br>
>>><br>
>>> [3] Palomar, Daniel P., and Mung Chiang. "A tutorial on decomposition<br>
>>> methods for network utility maximization." Selected Areas in<br>
>>> Communications, IEEE Journal on 24.8 (2006): 1439-1451.<br>
>>> <a href="http://www.princeton.edu/~chiangm/decomptutorial.pdf" target="_blank">http://www.princeton.edu/~chiangm/decomptutorial.pdf</a><br>
>>><br>
>>><br>
>>><br>
>><br>
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>>QlVcfGlyW0Mzs+8HVUjCTr9OnTcRwH8rWmpqawsLCoqKi0tLSoqOjVq1elpaU9PT0sFqugoCAiI=<br>
>>iI1NbW2tra/v5/BYKSlpXl4eISFhRUUFCQmJsbGxnZ3d3d1dcXHx5NIpOjo6KCgoIKCAqBavOt2=<br>
>>Tz5G5SuNRiMIQoLtymE95rHd+ZU2FfGGidQjRUUmHZsfrF5z96elt/653Giu6v01mqS9ak9+0/c=<br>
>>7Hfo8lMakfTDEjorXyVeIkYZtsKFFEATwBCAEJ3kQBDE4OAgj+IaGhnDkVFscx9Go1D8fZV/H1+=<br>
>>vXr0s0OPVUWHMa7g22W7bmnKO6aTxOOZquFrxLyn3z7JuzFjz4xx6yhpyH/GG/ww6JDuXN5aN2+=<br>
>>gcQBCExGsbzQ/z17nyvY/mfGyO7cdu2bf+Vr+XW9PbHPfUOtBdWNbV3mAnGKZrr95NKSWH1Ida5=<br>
>>9w6Faxgnnfg1UMkixSqrPquzv/Ndt0V8MWG+foAQRnbj5s2b/8vXZF963n1esX1/jkN/qOVQ1G3=<br>
>>89u9P5n7xxZGNR112hz+7kRF7vSTfpSnc4nkqOSOFkuLzxMdrBLw/4ANej5GEGSMmGUYmA7i7u7=<br>
>>u6uoaFhbW0tBCjxsd+wAeILT7w9QPeJ3zg6we8T/jA1w94n/D/AWQx0gRPTdK4AAAAAElFTkSuQ=<br>
>>mCC" alt=3D""><br><br>
>><br>=A0=A0=A0=A0 I wonder, however, does the situation the same if rateless=<br>
>> erasure code (say fountain codes) is used?=A0 As with erasure code, no ACK=<br>
>> and retransmission is needed except when the whole file is completed. So e=<br>
>>ven heavy loaded, the network is still busy with effective data packet, rig=<br>
>>ht?=A0 Although queueing delay will increase, I believe that=A0 the network=<br>
>> throughput=A0 will not=A0 suffer the plunge as un-coded network. <br><br>
>><br>=A0<br><br>Kara<br><br><br><br><br><br><br><div class=3D"gmail_quote">2=<br>
>>013/3/5 Srinivasan Keshav <span dir=3D"ltr">&lt;<a href=3D"mailto:<a href="mailto:keshav@uw">keshav@uw</a>=<br>
>><a href="http://aterloo.ca" target="_blank">aterloo.ca</a>" target=3D"_blank"><a href="mailto:keshav@uwaterloo.ca">keshav@uwaterloo.ca</a></a>&gt;</span><br><blockq=<br>
>>uote class=3D"gmail_quote" style=3D"margin:0 0 0 .8ex;border-left:1px #ccc =<br>
>>solid;padding-left:1ex"><br>
>><br>
>>To answer this question, I put together some slides for a presentation at t=<br>
>>he IRTF ICCRG Workshop in 2007 [1]. In a nutshell, to save costs, we always=<br>
>> size a shared resource (such as a link or a router) smaller than the sum o=<br>
>>f peak demands. This can result in transient or persistent overloads, reduc=<br>
>>ing user-perceived performance. Transient overloads are easily relieved by =<br>
>>a buffer, but persistent overload requires reductions of source loads, whic=<br>
>>h is the role of congestion control. Lacking congestion control, or worse, =<br>
>>with an inappropriate response to a performance problem (such as by increas=<br>
>>ing the load), shared network resources are always overloaded leading to de=<br>
>>lays, losses, and eventually collapse, where every packet that is sent is a=<br>
>> retransmission and no source makes progress. A more detailed description c=<br>
>>an also be found in chapter 1 of my PhD thesis [2].<br><br>
>><br>
>><br>
>><br><br>
>>Incidentally, the distributed optimization approach that Jon mentioned is d=<br>
>>escribed beautifully in [3].<br><br>
>><br><br>
>>hope this helps,<br><br>
>>keshav<br><br>
>><br><br>
>>[1] Congestion and Congestion Control, Presentation at IRTF ICCRG Workshop,=<br>
>> PFLDnet, 2007, Los Angeles (California), USA, February 2007.<br><br>
>><a href=3D"<a href="http://blizzard.cs.uwaterloo.ca/keshav/home/Papers/data/07/conge=" target="_blank">http://blizzard.cs.uwaterloo.ca/keshav/home/Papers/data/07/conge=</a><br>
>>stion.pdf" target=3D"_blank"><a href="http://blizzard.cs.uwaterloo.ca/keshav/home/Pa=" target="_blank">http://blizzard.cs.uwaterloo.ca/keshav/home/Pa=</a><br>
>>pers/data/07/congestion.pdf</a><br><br>
>><br><br>
>>[2] S. Keshav, Congestion Control in Computer Networks PhD Thesis, publishe=<br>
>>d as UC Berkeley TR-654, September 1991<br><br>
>><a href=3D"<a href="http://blizzard.cs.uwaterloo.ca/keshav/home/Papers/data/91/thesi=" target="_blank">http://blizzard.cs.uwaterloo.ca/keshav/home/Papers/data/91/thesi=</a><br>
>>s/ch1.pdf" target=3D"_blank"><a href="http://blizzard.cs.uwaterloo.ca/keshav/home/Pa=" target="_blank">http://blizzard.cs.uwaterloo.ca/keshav/home/Pa=</a><br>
>>pers/data/91/thesis/ch1.pdf</a><br><br>
>><br><br>
>>[3] Palomar, Daniel P., and Mung Chiang. &quot;A tutorial on decomposition =<br>
>>methods for network utility maximization.&quot; Selected Areas in Communica=<br>
>>tions, IEEE Journal on 24.8 (2006): 1439-1451.<br><br>
>><a href=3D"<a href="http://www.princeton.edu/~chiangm/decomptutorial.pdf" target="_blank">http://www.princeton.edu/~chiangm/decomptutorial.pdf</a>" target=3D"=<br>
>>_blank"><a href="http://www.princeton.edu/~chiangm/decomptutorial.pdf" target="_blank">http://www.princeton.edu/~chiangm/decomptutorial.pdf</a></a><br><br>
>><br><br>
>><br><br>
>></blockquote></div><br><br>
>><br>
>>--f46d0444e84964e1a304d740bdcd--<br>
<br>
cheers<br>
<span class="HOEnZb"><font color="#888888"><br>
jon<br>
<br>
</font></span></blockquote></div><br>