[NRG] [WING] Notification: NRG Meeting: Maple: Simplifying SDN Programming Using Alg... @ Tue Mar 17, 2015 11am - 12pm (NRG at BU)

Yuefeng Wang wyf at bu.edu
Thu Mar 12 11:50:57 EDT 2015


Hi everyone,
Just an update. Cody's talk will be at 3pm on Tuesday instead of 11am.

Regards,
Yuefeng

On Thu, Mar 12, 2015 at 11:00 AM, Google Calendar <
calendar-notification at google.com> wrote:

> more details »
> <https://www.google.com/calendar/event?action=VIEW&eid=dnNvbTVpaGsyaTBxZjcyYmIycTVyODRqNjggNTYwam42bnQ1aGo0b2YzcnNyaWNoZnB0aW9AZw>
> NRG Meeting: Maple: Simplifying SDN Programming Using Algorithmic Policies
> (Cody Doucette)
> Maple: Simplifying SDN Programming Using Algorithmic Policies
>
> Presenter: Cody Doucette
>
> Authors: Andreas Voellmy, Junchang Wang, Y. Richard Yang, Bryan Ford, Paul
> Hudak
>
> Abstract:
>
> Software-Defined Networking offers the appeal of a simple, centralized
> programming model for managing complex networks. However, challenges in
> managing low-level details, such as setting up and maintaining correct and
> efficient forwarding tables on distributed switches, often compromise this
> conceptual simplicity. In this talk, I will present Maple, a system that
> simplifies SDN programming by (1) allowing a programmer to use a standard
> programming language to design an arbitrary, centralized algorithm, which
> we call an algorithmic policy, to decide the behaviors of an entire
> network, and (2) providing an abstraction that the programmer-defined,
> centralized policy runs, conceptually, “afresh” on every packet entering a
> network, and hence is oblivious to the challenge of translating a
> high-level policy into sets of rules on distributed individual switches.
>
> To implement algorithmic policies efficiently, Maple includes not only a
> highly-efficient multicore scheduler that can scale efficiently to
> controllers with 40+ cores, but more importantly a novel tracing runtime
> optimizer that can automatically record reusable policy decisions, offload
> work to switches when possible, and keep switch flow tables up-to-date by
> dynamically tracing the dependency of policy decisions on packet contents
> as well as the environment (system state). Evaluations using real HP
> switches show that Maple optimizer reduces HTTP connection time by a factor
> of 100 at high load. During simulated benchmarking, Maple scheduler, when
> not running the optimizer, achieves a throughput of over 20 million new
> flow requests per second on a single machine, with 95-percentile latency
> under 10 ms.
>
> This paper was originally presented at SIGCOMM '13, and is available for
> download here:
> http://conferences.sigcomm.org/sigcomm/2013/papers/sigcomm/p87.pdf
> <https://www.google.com/url?q=http%3A%2F%2Fconferences.sigcomm.org%2Fsigcomm%2F2013%2Fpapers%2Fsigcomm%2Fp87.pdf&usd=2&usg=AFQjCNENx6c6I152pH86WOjPbU8TZLVF7w>
>
> *When*
> Tue Mar 17, 2015 11am – 12pm Eastern Time
> *Calendar*
> NRG at BU
> *Who*
>> Yuefeng Wang - creator
>
> Invitation from Google Calendar <https://www.google.com/calendar/>
>
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