Transcript Presentation Slide

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
Dials
Offered
Topology/
traffic
Configuration
measure
Knobs
Changes to
the network
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 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
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Problem #1: No Algorithm For Setting the Knobs
• Random Early Detection (RED)
Probability
– Several tunable parameters
– Min and max thresholds on queue length, max
probability, queue weight
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. 6
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, …
Probability
– Not easily measurable today on high-speed links
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
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j
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
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– Leads to an NP-hard optimization problem
– … forcing the use of local-search techniques
We need protocols designed with knob optimization in mind. 9
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
dst
NYC
SFO
ISP network
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Dallas
We need protocols that make small reactions to small changes.10
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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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
dst
NYC
SFO
ISP network
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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
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