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Thanks guys.

joel

On 11/3/14 3:30 PM, Vijay K. Gurbani wrote:
> On Oct 2, 2014 Tom Taylor wrote:
> 
>> Summary: Ready with minor issues and nits. I did not check out
>> the correctness of the code in Appendix A.
> 
> Tom: Thank you for your detailed review and your time on our
> draft.
> 
> Please see inline for resolutions.
> 
>> Minor issues: ------------
>> 
>> (1) Sec. 4.10 paragraphs 3 and 4: I believe these are about
>> setting the initial value for the attempt rate. The first
>> paragraph offers the alternative of using a vendor-provided value
>> R as a starting point or using r=R as a starting point. Doesn't
>> seem like two alternatives.
>> 
>> The second paragraph ignores all this and sets r=100 as the
>> starting point. But then it repeats text about using a
>> vendor-supplied value.
>> 
>> The two paragraphs need cleaning up.
> 
> This is a very good comment.  Here is what I propose:
> 
> OLD:
> 
> If the DUT vendor provides a value for R, the tester can use this 
> value.  Alternatively, in cases where the DUT vendor does not
> provide a value for R, or in cases where the tester wants to
> ascertain a vendor provided value using local media
> characteristics, the algorithm could be run by setting "r = R" and
> observing the value at convergence.
> 
> The algorithm proceeds by initializing "r = 100"; "r" is the
> session attempt rate.  The algorithm dynamically increases and
> decreases "r" as it converges to the a maximum sps value for R. The
> dynamic increase and decrease rate is controlled by the weights "w"
> and "d", respectively.  If the DUT vendor provides a value for R,
> the tester can use that value; however, because the requirements
> and media characteristics are a function of the test environment,
> it is best that the tester reflect these requirements during
> testing and allow the algorithm to converge to R.
> 
> NEW:
> 
> If the DUT vendor provides a value for R, the tester can use this 
> value to initialize "r = R", and proceed with the algorithm.  In 
> cases where the DUT vendor does not provide a value for R, or in 
> cases where the tester wants to ascertain a vendor provided value 
> using local media characteristics, it is best that the tester 
> reflect these requirements during testing and allow the algorithm
> to converge to R.  In such cases, the algorithm proceeds by
> initializing "r = 100";  "r" is the session attempt rate.  The
> algorithm dynamically increases and decreases "r" as it converges
> to the a maximum sps value for R. The dynamic increase and decrease
> rate is controlled by the weights "w" and "d", respectively.
> 
>> (2) The algorithm in 4.10 seems fairly unsophisticated, and
>> amounts to ten iterations of constrained binary search. Was
>> application of Newton's Method
>> 
>> http://www.ima.umn.edu/%7Emiller/Newton-MVT.pdf
>> 
>> to give you faster convergence and shorter test runs considered?
> 
> Very pertinent feedback (thanks!).  Pursuant to our offline 
> conversations, I believe that the algorithm as specified in
> bmwg-sip- bench-meth-11 does not need to be modified.  In the end,
> the algorithm in the draft is a version of gradient descent, or in
> our case, ascent. Such algorithms may not be fast, but they do find
> the maxima (minima) on a bounded number of iterations.
> 
> An important reason why the algorithm in bmwg-sip-bench-meth-11
> appears in the form that is does is for the simple reason that the
> tester may not know R a-priori.  Recall that R is a SIP session
> attempt rate, which the DUT can process with zero errors over a
> defined, extended period.
> 
> Our draft and the algorithm takes the conservative approach that
> the tester may not be given the value of R by the vendor a-priori,
> or the tester may independently want to approximate R to
> double-check vendor claim using locally pertinent media
> characteristics.
> 
> The binary search method depends on knowing the value of R, since
> that will become a ceiling to the process.  The Newton (N-R) method
> also depends on knowing the value of R, since to get N-R going, we
> would start the first run at the claimed steady-state (R) capacity.
> I have no doubt that we can make binary search or N-R work such
> that R is not known a-priori. But by doing so I suspect that we'd
> be close to the gradient descent that we have now.
> 
> Based on the simulation results for a binary search I shared with 
> you, I suggest adding a sentence in the revision to bench-meth as 
> follows:
> 
> OLD: The pseudo-code corresponding to the description above
> follows, and a simulation written in the R statistical language is
> provided in Appendix A.
> 
> NEW: The pseudo-code corresponding to the description above
> follows, and a simulation written in the R statistical language is
> provided in Appendix A.  We note that it is possible to have other
> algorithms that converge faster to R than the one shown below
> (e.g., an algorithm based on a binary search), however, such
> algorithms may not produce an optimal value for R, that is, they
> may sacrifice some accuracy for speed.  Such algorithms are for
> further study.
> 
>> Nits/editorial comments: -----------------------
>> 
>> IDNits requests that the code in Appendix A be preceded by a
>> <CODE BEGINS> line and terminated by a <CODE ENDS> line.
>> 
>> Sec. 3 para 2 last line: s/arrangements/arrangement/
> 
> Got it.  Thanks!
> 
>> Next para: spell out EA as presumably "Emulated Agents" on first 
>> use?Term is from the terminology document.
> 
> Done.
> 
>> Add <CODE BEGINS> and <CODE ENDS> around the pseudo-code in
>> 4.10.
>> 
>> Reference [I-D.sip-bench-term] is now dated July, 2014 (version
>> -11).
> 
> Good catch.  Updated.
> 
> I will skip the <CODE BEGINS> and <CODE ENDS> nits, if it is okay
> with you.
> 
> The results will show up in -12 that will be released shortly.
> Thanks a lot, Tom, for your insights and feedback on this work.
> 
> Cheers,
> 
> - vijay

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