ALN

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ALN: Applied Learning Networks

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Making TCP Smarter

ALN applies experience with previous network connections to help tune future network connections.

Current Internet hosts open new connections that are initialized with a number of default parameters. These defaults are intended to be conservative, such as 'start with one packet at a time' and 'assume you know nothing about the round trip time'.

Various experience with TCP has shown that it can be useful to apply past experience to help tune some of these parameters for future connections.

ALN assumes:

  • TCP does a good job converging on TCB state over time,
    but a lousy job of guessing initial conditions
  • TCP experiences stable net over the connection,
    and stable offered load

Does the following:

  • RECORDS TCP end-of-connection state, as well as 'kitchen sink' data (weather, endpoint loc, etc.)
  • TRAINS an adaptive learning module on the state data (TCB state:kitchen sink state)
  • APPLIES TCBs of new connections based on predictive lookup (i.e., lookup kitchen sink state and retrieve expected TCP initial state)

Parts of the ALN Project

Data Collection
Collect TCP state information with associated 'real-world' context and look for possible correlation axes. Also examine the start/end state and determine potential bounds of performance benefit.
Predictors
definition
 
Integration
definition
Evaluation
 
Includes integration of the system in a real OS, and measuring the performance benefit and overhead.

Effort sponsored by the Defense Advanced Research Projects Agency (DARPA) and Air Force Research Laboratory, Air Force Materiel Command, USAF, under agreement number FA8750-05-1-0051, order # T981. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the Defense Advanced Research Projects Agency (DARPA), the Air Force Research Laboratory, or the U.S. Government.

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