Network Protocols Designed for Optimizability

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Network Protocols Designed for Optimizability

• Jennifer Rexford
• Princeton University
• http//www.cs.princeton.edu/jrex

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Measure, Model, and Control
Network Management
Models, tools, scripts, databases
Knobs
Dials
Offered traffic
Topology/ Configuration
Changes to the network
measure
control
Operational network
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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 cant add all that much meaningful
  abstraction on top of what weve 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

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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.
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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.
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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.
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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
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Dallas
We need protocols that make small reactions to
small changes.
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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

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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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1
3
1
3
2
1
5
4
3
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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
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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
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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