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Network Protocols Designed for OptimizabilityPowerPoint Presentation

Network Protocols Designed for Optimizability

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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

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

measure

control

Operational network

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

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

- Random Early Detection (RED)
- Several tunable parameters
- Min and max thresholds on queue length, max probability, queue weight

Probability

Average Queue Length

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

- 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

- Appropriate parameters depend on many factors

Probability

Average Queue Length

We need measurements that support network management.

j

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.

1

3

1

3

2

1

5

4

3

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.

10

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

- Small change causes a big effect

NYC

SFO

ISP network

11

Dallas

We need protocols that make small reactions to small changes.

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

- Example: traffic engineering
- Forward traffic in inverse proportion to path costs
- … rather than using only the shortest paths
- Leads to polynomial-time optimization problems

2

1

3

1

3

2

1

5

4

3

10

9

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

- 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

- 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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