Network Protocols Designed for Optimizability Jennifer Rexford Princeton University

2 Measure, Model, and Control Topology/ Configuration Offered traffic Changes to the network Operational network Models, tools, scripts, databases measure control Network Management Dials Knobs

3 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

4 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

5 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 Average Queue Length Probability

6 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.

7 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 We need measurements that support network management. Average Queue Length Probability

8 Problem #2: Poor Dials to Guide Knob Settings Example: Traffic engineering –Depends on knowing the traffic matrix M ij –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. i j

9 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 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 SFO Dallas NYC ISP network dst We need protocols that make small reactions to small changes.

11 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

12 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

13 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 SFO Dallas NYC ISP network dst

14 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

15 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