Volker,

I believe the issue is independent of SIP, if the offered load at client "significantly" surges over a period of time period "much shorter" than the overloaded server feedback period, then applying % blocking at the client will not work "as well as" applying rate control.

Within the scope of our simulation models, that would be equivalent to the transients associated with step like offered load we looked at.  But if I remember well, we never looked at the controlled load out of the clients, we always focused on the overloaded element goodput and we all implemented percent blocking in the client.

Unfortunately, I have not run simulations to compare both throttling methods so I can only provide qualitative statements. 

Though, note that I always assumed the feedback period from the overloaded element to be short enough to nullify the issue, hence ignoring the issue.

Thanks,

Eric Noel 
LMTS
AT&T Labs, Inc. 
Rethink Possible

Network Design and Performance Analysis
200 South Laurel Avenue, D5-3D19
Middletown, NJ 07748
P: 732.420.4174
ecnoel@att.com


-----Original Message-----
From: Volker Hilt [mailto:volker.hilt@alcatel-lucent.com] 
Sent: Friday, April 29, 2011 12:43 PM
To: NOEL, ERIC C (ERIC C)
Cc: sip-overload@ietf.org
Subject: Re: [sip-overload] Proposal: Support for the restriction algorithms should be mandatory for clients (draft-ietf-soc-overload-control-02)

Eric,

what makes you think the problems described in this paper apply to SIP 
overload control? None of the simulations (including yours) did show 
problems with loss-based control. Do you have results that replicate 
these issues in a SIP network? What is missing?

Volker




On 4/29/2011 8:56 AM, NOEL, ERIC C (ERIC C) wrote:
> Volker,
>
> Wrt Phil's comment on vulnerability to sudden increases on the
> offered load at client sources, I believe the paper from Arthur
> Berger in IEEE Transactions on Automatic Control Vol 36, No2,
> February 1991 titled "Overload Control Using Rate Control: Selecting
> Token Bank Capacity for Robustness to Arrival Rates" supports that
> observation.
>
> In that paper, Figure 3 compares rate control to call gapping and to
> percent blocking. As offered load increases, rate control is the only
> algorithm that controls load close to the target. Basically Phil's
> point.
>
> Because it is a copyrighted document, I do not think I can broadcast
> on distribution list. However, if you cannot get a copy of the paper
> let me know and I will assist you.
>
> Thanks,
>
> Eric Noel LMTS AT&T Labs, Inc. Rethink Possible
>
> Network Design and Performance Analysis 200 South Laurel Avenue,
> D5-3D19 Middletown, NJ 07748 P: 732.420.4174 ecnoel@att.com
>
>
> -----Original Message----- From: sip-overload-bounces@ietf.org
> [mailto:sip-overload-bounces@ietf.org] On Behalf Of Volker Hilt Sent:
> Thursday, April 28, 2011 10:06 PM To: sip-overload@ietf.org Subject:
> Re: [sip-overload] Proposal: Support for the restriction algorithms
> should be mandatory for clients (draft-ietf-soc-overload-control-02)
>
> Phil,
>
>> SIP support for 3 restriction algorithms at client sources defined
>> in soc-overload-control-02 is beneficial for the flexibility that
>> it provides to potentially support a wide range of server overload
>> controls and network applications. However, making proportional
>> restriction, termed a 'loss-based' algorithm, mandatory for clients
>> would severely limit the usefulness of this approach. I will
>> summarise the reasons for this and why this algorithm has limited
>> use in future generations of networks below. Practical issues
>> ----------------------- If client system suppliers are only
>> required to support proportional restriction, then realistically it
>> is likely that that is the only algorithm that will be provided by
>> default, and the other algorithms will be seen as chargeable
>> enhancements, making it difficult or expensive to deploy them. But,
>> of the two functional ends of a closed adaptive overload control,
>> it is the algorithm on the overloaded server that derives the
>> control parameter that is not standardised and has dependency on
>> node architecture, whereas the 3 client restriction algorithms are
>> less subject to design variation, have been around for a long time,
>> and are already widely deployed. Therefore we would expect it to be
>> less contentious to standardise these methods within SIP, but in
>> any case we would expect less sensitivity to design variations. It
>> is not realistic to envisage a situation whereby an overloaded
>> server supports several different client restriction algorithms
>> simultaneously, because of the complexity in design of the
>> algorithm, and because even if a unique solution is guaranteed,
>> there will be issues of both speed of convergence to that solution,
>> stability, and the need for the server capacity amongst upstream
>> clients to be allocated in a predictable and precise way. This has
>> implications for capacity guarantees, particularly at network
>> boundaries (see below). If support for the 3 different restriction
>> algorithms (or at least the 2 in which we are interested) is
>> mandated by the IETF rather than being optional, then a server
>> subject to overload can guarantee that the sending entities will
>> all use the same restriction algorithms, and can behave as
>> predicted.
>
> So you are arguing that a client should always implement the full set
> of restriction algorithms (e.g., loss, rate and window)?
>
> If we would mandate that, we would loose the possibility to extend
> the feedback types at a later point. And, of course, we add the
> complexity of requiring clients to implement multiple algorithms that
> to the same.
>
>> Performance imitations ---------------------------------- The main
>> performance limitation of proportional restriction is that it is
>> vulnerable to sudden increases on the offered load at client
>> sources, since the mean admitted rate after control is proportional
>> to the mean arrival rate before control until an adaptation of the
>> control parameter is made. But there will always be a delay to this
>> control adaptation (at least in the closed loop method implied by
>> the current specification), both because of the need for waiting
>> sufficiently long at the overloaded server in order to obtain
>> statistically accurate estimates before an adaptation is made, and
>> the time and number of received messages required to distribute
>> control to the clients.
>
> Can you please point us to results that show this behavior?
>
> I'd also recommend to look at the results of the SIP overload
> control design team that has investigated this problem.
>
>> This vulnerability is worse when the number of clients is 'large'
>> with a high capacity relative to the server. In contrast, a maximum
>> rate-based control is generally not so vulnerable to short term
>> surges in load.
>
> A rate based mechanism needs to split its capacity across upstream
> neighbors. If new clients arrive, the split has to be adjusted. In
> particular if you are dealing with a large set of clients some of
> which may be inactive for some time, this requires a quick
> readjustment of the allocated capacity. This topic has been
> discussion in the design considerations draft.
>
>> The need to support precise capacity guarantees
>> ------------------------------------------------------------------------
>>
>>
It is common practice for agreements concerning capacity to be provided
>> at network operator boundaries [from now on I'll use SP or Service
>> Provider as a generic term encompassing Operators etc], and in
>> many realistic applications this is essential (I recall that his
>> requirement is absent from the original list of SIP overload
>> control requirements?). It is also possible to want to provide
>> guarantees to sub-streams of SP traffic. These guarantees must be
>> practically useful, so that they can form the basis of a service
>> level agreement between SPs. I.e. they must be simple enough to be
>> easily understood by all parties, and above all they must be clear
>> and precise in the sense that the behaviour of the capacity is
>> predictable/deterministic (in a stochastic sense). SPs are not
>> interested in technicalities of restriction algorithms, but will
>> want the policies to be defined in terms of traffic characteristics
>> that are straightforward to interpret and agree. Clearly the
>> policies must also be efficient in the sense that imply that the
>> available capacity can be fully utilised. I suggest that these are
>> most easily expressed as a guaranteed minimum rate and a precise
>> way in which 'spare' capacity not being used by a client originated
>> stream is distributed over the other SPs, e.g. in terms of maximum
>> rates determined by agreed proportions of the available unused
>> allowances. Of course other policies are possible (but they may be
>> less precise or more complex). Whatever policies are chosen,
>> realising this is an inherent part of the server overload control,
>> but the difficulty and complexity is dependent upon the method of
>> call restriction is the clients. With proportional restriction,
>> note that the percentages have nothing directly to do with the
>> proportions of server capacity allocated to different clients. So
>> there is no natural and simple way to map between the parameters of
>> the agreement and the control parameters. If the same control level
>> were applied to all client traffic, then the changes in the offered
>> traffic from one client will always imply changes to the traffic
>> admitted by another, (and in particular this applies to sudden
>> large increases). To apply maximum rate-based guarantees would
>> require monitoring of the received rate from each source separately
>> in order that the offered traffic can be derived implicitly and
>> thereby percentages derived for each specific source. In contrast,
>> with a rate-based restriction, it is much simpler to implement
>> policies defined in terms of maximum rates, even though these are
>> adapted according to minimum guarantees and use of unused
>> allowances in a precise and predictable way.
>
> I don't think overload control is the right tool to police SLAs.
>
> Let's assume for a second you are in fact using overload control for
> this purpose and are configuring your overload control rates to
> match your SLAs. Say you have two servers A and B (each with capacity
> 500 req/s) and four upstream neighbors. The SLA with each of them is
> that you accept 250 req/s.
>
> If one of your servers goes down, your capacity is cut in half. If
> you have configured overload control to honor you SLAs, your
> remaining server will melt down within ms. Your only choice to
> survive this situation is to use overload control and cut the rates
> below the SLA.
>
> Thanks,
>
> Volker (as individual)
>
>
>
>
>> Comments please! Phil Williams
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