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Network Protocols Designed for Optimizability. Jennifer Rexford Princeton University http://www.cs.princeton.edu/~jrex. Measure, Model, and Control. Network Management. Models, tools, scripts, databases. Knobs. Dials. Offered traffic. Changes to the network. Topology/ Configuration.

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Network protocols designed for optimizability l.jpg

Network Protocols Designed for Optimizability

Jennifer Rexford

Princeton University

http://www.cs.princeton.edu/~jrex


Measure model and control l.jpg
Measure, Model, and Control

Network Management

Models, tools, scripts, databases

Knobs

Dials

Offered

traffic

Changes to

the network

Topology/

Configuration

measure

control

Operational network


Knobs and dials l.jpg
Knobs and Dials

  • Knobs: configurable parameters

    • Buffering: Random Early Detection parameters

    • Link scheduling: weighted fair queuing weights

    • Path selection: link weights and routing policies

  • Dials: measurement data

    • Traffic: link utilization, Netflow records, …

    • Performance: ping, download times, …

    • Routing: routing-protocol messages, tables, …

Network management: read the dials and tune the knobs


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Two Directions We Could Go

  • Algorithms for setting knobs based on dials

    • E.g., setting RED parameters based on link load

    • E.g., setting link weights based on traffic matrix

    • E.g., setting access-control lists to block attacks

  • Designing better knobs and dials

    • Maybe we can’t add all that much meaningful abstraction on top of what we’ve got underneath

    • Maybe we should design new protocols and mechanisms with optimization in mind

    • “Doing well in a class is much easier when you get to write the exam.” – Mung Chiang


Problem 1 no algorithm for setting the knobs l.jpg
Problem #1: No Algorithm For Setting the Knobs

  • Random Early Detection (RED)

    • Several tunable parameters

    • Min and max thresholds on queue length, max probability, queue weight

Probability

Average Queue Length


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Problem #1: RED Example Continued

  • Settings have a big influence on performance

    • Good settings can improve the network “goodput”

    • Bad settings may offer no improvement, or (in some cases), worse performance

  • No algorithm for optimizing the parameters

    • Settings based on general guidelines

    • Makes it difficult for operators to enable RED

We need mechanisms that have algorithms for setting knobs.


Problem 2 poor dials to guide knob settings l.jpg
Problem #2: Poor Dials to Guide Knob Settings

  • Example: Random Early Detection

    • Appropriate parameters depend on many factors

      • Number of active flows, flow durations, flow RTTs, …

    • Not easily measurable today on high-speed links

Probability

Average Queue Length

We need measurements that support network management.


Problem 2 poor dials to guide knob settings8 l.jpg

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Problem #2: Poor Dials to Guide Knob Settings

  • Example: Traffic engineering

    • Depends on knowing the traffic matrix Mij

    • Challenging to measure

      • Resorting to inference of the traffic matrix

      • Aggregating and joining lots of fine-grain data

We need measurements that support network management.


Problem 3 intractable optimization problems l.jpg

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Problem #3: Intractable Optimization Problems

  • Example: Traffic engineering

    • Tuning link weights to the prevailing traffic

    • Leads to an NP-hard optimization problem

    • … forcing the use of local-search techniques

We need protocols designed with knob optimization in mind.


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Problem #4: Non-Linearities in the System

  • Example: Hot-potato routing

    • Small change causes a big effect

      • Failure, planned maintenance, or traffic engineering

      • Routes to thousands of destinations shift at once

      • … causing large shifts in traffic and many BGP updates

NYC

SFO

ISP network

11

Dallas

We need protocols that make small reactions to small changes.


Design for optimizability l.jpg
Design for Optimizability

  • Creating protocols and mechanisms where

    • We know the algorithms for tuning the knobs

    • We have the measurements the algorithms need

    • The resulting optimization problems are tractable

    • The system does not have non-linearities

  • Example approaches

    • Randomization

    • Increasing the degrees of freedom

    • Logically centralized control


Randomization l.jpg
Randomization

  • Example: traffic engineering

    • Forward traffic in inverse proportion to path costs

    • … rather than using only the shortest paths

    • Leads to polynomial-time optimization problems

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Increasing degrees of freedom l.jpg

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Increasing Degrees of Freedom

  • Example: egress selection

    • Forward traffic to lowest ranked egress point

    • … as weighted sum of constant and path cost

    • E.g., keep using SFO even when cost goes to 11

    • Enables integer programming solutions for tuning

NYC

SFO

ISP network

11

Dallas


Logically centralized control l.jpg
Logically Centralized Control

  • Example: Routing Control Platform (RCP)

    • Separate topology discovery from path selection

    • Collect topology and traffic data at servers

    • Apply optimization techniques for selecting routes

    • … and tell routers what forwarding tables to use

RCP


Conclusions l.jpg
Conclusions

  • Protocols induce optimization problems

    • Read the dials and tune the knobs

    • Controls how the system performs

  • Yet, optimization problems are often hard

    • Lack of predictive models

    • Missing measurement data

    • Computational intractability

    • Non-linearities in the system

  • Design protocols with optimization in mind

    • Randomize, add degrees of freedom, decompose


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