CS452 - Real-Time Programming - Spring 2012

Lecture 29 - Pathologies II

Public Service Annoucements

  1. Final exam date: 9.00 August 7 to 11.30 August 9


As we go down this list both pathology detection and the length of the edit-compile-test cycle grow without bound.

1. Deadlock

2. Livelock (Deadly Embrace)


Two or more tasks are READY. For each task, the state of the other tasks prevents progress being made regardless of which task is ACTIVE.

A higher level of coordination is required.

Two types of livelock exist

  1. Ones that are the result of bad coding
  2. Ones that are inherent in the application definition

Looking for solutions we prefer ones that avoid a central planner. Why?

Livelock usually occurs in the context of resource contention

Livelock that's Really Deadlock


  1. Make a single compound resourse, BUT
  2. Impose a global order on resource requests that all clients must follow.
  3. Create a mega-server that handles all resource requests

Real Livelock

Proprietor1 & proprietor2 fail the requests

Livelock that's Really a Critical Race

We could try to make the clients a little more considerate

   for ( Send( prop1, get-res1, result1 ) && Send( prop2, get-res2, result2 );
         !(result1 && result2) || !time-out( ); 
         Send( prop1, get-res1, result1 ) && Send( prop2, get-res2, result2 ) {
      if ( result2 ) Send( prop2, release-res2, ... );
      if ( result1 ) Send( prop1, release-res1, ... );
      Delay ( random( ) );

Inherent Livelock

Remember the example where two trains come face to face, each waiting for the other to move. They will wait facing each other until the demo is over, probably polling.

What's hard about solving this problem?

In real life,

What's most easy for you to do is to programme each driver with

  1. detection, e.g.,
  2. work around, e.g.,

Notice that the solution above doesn't work for the train-driver because he cannot release the track he is sitting on. His solution would be something like

   for ( Send( prop, get-res, result );
         !result && !time-out( ); 
         Send( prop, get-res, result ) Delay ( random( ) );

3. Critical Races


  1. Two tasks, A & B, at the same priority
  2. A is doing a lot of debugging IO
  3. B always reserves a section of track before A, and all is fine.
  4. Debugging IO is removed
  5. A reserves the section before B can get it, and execution collapses.
  6. Lower priority of A to the same level as C.
  7. Now C executes faster and gets a resource before D .
  8. You shuffle priorities forever, eventually reverting, to put back in the debugging IO.


The order in which computation is done is an important factor in determining whether or not it is successful.

Critical races, like Livelock can be


  1. Small changes in priorities change execution unpredictably, and drastically.
  2. Debugging output changes execution drastically.
  3. Changes in train speeds change execution drastically.

`Drastically' usually means chaos in both senses of the term

  1. Sense one: a small change in the initial conditions produces an exponentially growing divergence in the execution.
  2. Sense two: exercise for the reader.


  1. Explicit synchronization
  2. Gating is a technique of global synchronization

These solutions are hard to find because

  1. Scenario-oriented design is natural (possibly even inescapable) for humans
  2. Too much synchronization can kill performance because it introduces extra dependencies. (Check to see how many tasks are delaying.)

4. Performance

Changes in performance of one task with respect to another often give rise to critical races

The hardest problem to solve

In practice, how do you know you have performance problems? Problems I have seen


The hardest thing to get right

Problems with priority

  1. Priority inversion
  2. One resource, many clients
  3. Tasks try to do too much


  1. Too many tasks

Layered abstraction are costly

e.g. Notifier -> SerialServer -> InputAccumulater -> Parser -> TrackServer


  1. Too much terminal output interferes with train controller communication
  2. Requests to poll the sensors get backed up in the serial server, or whoever provides output buffering.


  1. Turn on optimization, but be careful
  2. Turn on caches

Size & align calibration tables by size & alignment of cache lines

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