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Closes #16. Use a single shared queue between all threads in each workload mapper.#19

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xkrogen merged 1 commit into
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xkrogen:ekrogen-workload-shared-queue
Apr 5, 2018
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Closes #16. Use a single shared queue between all threads in each workload mapper.#19
xkrogen merged 1 commit into
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xkrogen:ekrogen-workload-shared-queue

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@xkrogen

@xkrogen xkrogen commented Mar 28, 2018

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On a single mapper in the workload replay, currently tasks are distributed to threads in a path-partitioned manner. This results in bad skew issues, where one thread can fall very far behind because it is overloaded. Thus, although all of the issues on a specific path get executed in order (though late), the ordering with other paths is very far out of sync. It is better to let some operations on the same path occur out of order so that (a) overall everything stays more in sync and (b) no thread falls far behind (which can delay the entire mapper from completing).

…ared queue between all threads in a single workload mapper.
@chliang71

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One question, why is this IP based partitioning? To me, it looks like every command gets put into the same queue, and the threads pick up the commands regardless of IP. Am I missing something?

@xkrogen xkrogen changed the title Closes #16. Support IP based partitioning and use a single shared queue between all threads in a single workload mapper. Closes #16. Use a single shared queue between all threads in each workload mapper. Mar 30, 2018
@xkrogen

xkrogen commented Mar 30, 2018

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Sorry, the commit message was a bit incorrect. I have updated the title & the description to more fully explain the issue we are solving here.

@chliang71

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LGTM, +1

@xkrogen xkrogen merged commit 8f73360 into linkedin:master Apr 5, 2018
@xkrogen xkrogen deleted the ekrogen-workload-shared-queue branch April 5, 2018 17:25
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2 participants