The Use of Rollback to Prevent Incorrect Operation of Features in Intelligent Network Based Systems

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Dave Marples

CITEL Technologies Ltd [email protected]

Evan Magill

University of Strathclyde [email protected]

2/18 Structure * Target System * Algorithms * Implementation * Results * Conclusions

Structure of Presentation

      Introduction Target System Algorithms System Implementation Results Conclusions

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3/18 Structure * Target System * Algorithms * Implementation * Results * Conclusions

Target System

  A pre-existent switching system that requires new features to be added, potentially by multiple independent agencies.

A system that is difficult or impossible to predict based on the information that is available.

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Do Not Disturb

4/18 Structure * Target System * Algorithms * Implementation * Results * Conclusions

The Overall System

Terminal Call Forward Uncond Basic Call Switch Hardware Terminal

Software Subsystem

Feature Manager Sw

Switch Software

Communications Link Terminal

Hardware Subsystem CITEL

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5/18 Structure * Target System * Algorithms * Implementation * Results * Conclusions

A Typical Feature

Existing Feature Feature Controller

To/From Feature Manager CITEL

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6/18 Structure * Target System * Algorithms * Implementation * Results * Conclusions

The Feature Controller

    Behaves like the switching system from the perspective of the feature.

Is capable of rolling back the feature to some previous defined state.

Performs other arbitrary management functions on behalf of the system.

Can be ‘retrofitted’ to legacy Features and Clumps.

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E1

* Implementation * Results * Conclusions Structure * Target System * Algorithms

Feature Operation

7/18

Commit Case Feature Logic Start Transaction Create Copy Copy of Feature Logic R1 E2 R2 Commit Original Process Exits Copy Process Continues and Becomes Orignal

R1 E2 E1

* Implementation * Results * Conclusions Structure * Target System * Algorithms

Feature Operation

8/18

Commit Case Feature Logic Abort Case Feature Logic Start Transaction Create Copy Copy of Feature Logic Start Transaction Create Copy Copy of Feature Logic E1 R1 E2 R2 Commit R2 Abort Copy Process Exits Original Process Exits Copy Process Continues and Becomes Orignal Original Process Resumes

9/18 Structure * Target System * Algorithms * Implementation * Results * Conclusions

Feature Manager

    Controls the rollback of the features and caches their responses.

Uses the responses to detect the interaction between features.

Applies appropriate resolutions if one can be found.

Alerts the system operator if no suitable resolution is available.

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10/18 Structure * Target System * Algorithms * Implementation * Results * Conclusions

Output

    Graph of transitions in response to initial triggering event. (Action Trace) Automated resolution by rules (Feature x takes precedence over Feature y).

Manual Resolution by Operator Intervention.

Automated ‘learned’ resolutions based on past experience (under development).

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11/18 Structure * Target System * Algorithms * Implementation * Results * Conclusions

Implementation of System

   Based around eight IN-style Features implemented as separate processes in a UNIX environment.

Whole system runs under DESK.

Runs on a Pentium II 233 system under Linux with the Hardware Subsystem in a DOS window.

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12/18 Structure * Target System * Algorithms * Implementation * Results * Conclusions

Sample Output

Basic Call together with Do Not Disturb.

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BCM with DND and CFU

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14/18 Structure * Target System * Algorithms * Implementation * Results * Conclusions

Sample Output

BCM with CFU Loop

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15/18 Structure * Target System * Algorithms * Implementation * Results * Conclusions CFU (B) DND (B) OCS (A) TL (A) CFBL (B) HL (A) TCS (B) CW (B) CFU (B) DND (B)

Detected Detected

Results

OCS (A) TL (A) CFBL (B)

Detected Detected

HL (A)

Detected

TCS (B)

Detected Detected

CW (B)

Detected Detected

16/18 Structure * Target System * Algorithms * Implementation * Results * Conclusions

Analysis of Results

    The technique does not generate false positive or negative results. It detects interaction system states.

* in every case when the interaction occurs between two stable It is not possible to automatically resolve intention interactions using this, or any other, automatic system.

Missed trigger points are not detected using the technique. The action trace is a useful facility for seeing what features acted on a call to allow further analysis.

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17/18 Structure * Target System * Algorithms * Implementation * Results * Conclusions

Conclusions

    The use of RollBack opens up new techniques for the co-operative existence of multiple features.

Information about the internal architecture of Features is not required for the correct operation of the technique.

The technique works with existing feature sets. Legacy Compatible.

The communication and signalling used by the features is arbitrary - it doesn’t need to be IN.

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18/18 Structure * Target System * Algorithms * Implementation * Results * Conclusions

Future Work

     Investigation of n-Feature Scenarios.

Investigation of long-lived Features.

Analysis of performance and scalability concerns.

Implementation of the technique in a real switching environment.

Exploration of hybrid approaches to FI using more formal specification and design-time techniques to supplement the runtime capabilities presented here.

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