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Distributed Watchpoints: Debugging Very Large Ensembles of Robots De Rosa, Goldstein, Lee, Campbell, Pillai. Aug 19, 2006. Motivation. Distributed errors are hard to find with traditional debugging tools Centralized snapshot algorithms Expensive Geared towards detecting one error at a time

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Distributed Watchpoints: Debugging Very Large Ensembles of RobotsDe Rosa, Goldstein, Lee, Campbell, Pillai

Aug 19, 2006


Motivation
Motivation

  • Distributed errors are hard to find with traditional debugging tools

  • Centralized snapshot algorithms

    • Expensive

    • Geared towards detecting one error at a time

  • Special-purpose debugging code is difficult to write, may itself contain errors

Distributed Watchpoints


Expressing and detecting distributed conditions
Expressing and Detecting Distributed Conditions

“How can we represent, detect, and trigger on distributed conditions in very large multi-robot systems?”

  • Generic detection framework, well suited to debugging

  • Detect conditions that are not observable via the local state of one robot

  • Support algorithm-level debugging (not code/HW debugging)

  • Trigger arbitrary actions when condition is met

  • Asynchronous, bandwidth/CPU-limited systems

Distributed Watchpoints


Distributed parallel debugging state of the art
Distributed/Parallel Debugging:State of the Art

Modes:

  • Parallel: powerful nodes, regular (static) topology, shared memory

  • Distributed: weak, mobile nodes

    Tools:

  • GDB

  • printf()

  • Race detectors

  • Declarative network systems with debugging support (ala P2)

Distributed Watchpoints


Example errors leader election
Example Errors: Leader Election

Scenario: One Leader Per Two-Hop Radius

Distributed Watchpoints


Example errors token passing
Example Errors: Token Passing

Scenario: If a node has the token, exactly one of it’s neighbors must have had it last timestep

Distributed Watchpoints


Example errors gradient field
Example Errors: Gradient Field

Scenario: Gradient Values Must Be Smooth

Distributed Watchpoints


Expressing distributed error conditions
Expressing Distributed Error Conditions

Requirements:

  • Ability to specify shape of trigger groups

  • Temporal operators

  • Simple syntax (reduce programmer effort/learning curve)

    A Solution:

  • Inspired by Linear Temporal Logic (LTL)

    • A simple extension to first-order logic

    • Proven technique for single-robot debugging [Lamine01]

  • Assumption: Trigger groups must be connected

    • For practical/efficiency reasons

Distributed Watchpoints


Watchpoint primitives
Watchpoint Primitives

  • nodes(a,b,c);

  • n(b,c)

  • &

  • (a.var > b.var)

  • & (c.prev.var != 2)

  • Modules (implicitly quantified over all connected sub-ensembles)

  • Topological restrictions (pairwise neighbor relations)

  • Boolean connectives

  • State variable comparisons (distributed)

  • Temporal operators

Distributed Watchpoints


Distributed errors example watchpoints
Distributed Errors: Example Watchpoints

nodes(a,b,c);n(a.b) & n(b,c) & (a.isLeader == 1) & (c.isLeader == 1)

nodes(a,b,c);n(a,b) & n(a,c) & (a.token == 1) & (b.prev.token == 1) & (c.prev.token == 1)

nodes(a,b);(a.state - b.state > 1)

Distributed Watchpoints


Watchpoint execution

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Watchpoint Execution

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Distributed Watchpoints


Performance watchpoint size
Performance: Watchpoint Size

  • 1000 modules, running for 100 timesteps

  • Simulator overhead excluded

  • Application: data aggregation with landmark routing

  • Watchpoint: are the first and last robots in the watchpoint in the same state?

Distributed Watchpoints


Performance number of matchers
Performance: Number of Matchers

  • This particular watchpoint never terminates early

  • Number of matchers increases exponentially

  • Time per matcher remains within factor of 2

  • Details of the watchpoint expression more important than size

Distributed Watchpoints



Future work
Future Work

  • Distributed implementation

  • More optimization

  • User validation

  • Additional predicates

Distributed Watchpoints


Conclusions
Conclusions

  • Simple, yet highly descriptive syntax

  • Able to detect errors missed by more conventional techniques

  • Low simulation overhead

Distributed Watchpoints



Backup slides
Backup Slides

Distributed Watchpoints


Optimizations
Optimizations

  • Temporal span

  • Early termination

  • Neighbor culling

  • (one slide per)

Distributed Watchpoints


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