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Dynamic overload control for distributed call processors using the neural network method.

S Wu¹, K M Wong

¹Laboratory for Information Synthesis, RIKEN Brain Science Institute, Hirosawa 2-1, Wako-shi, Saitama 351-01, Japan.

IEEE Transactions on Neural Networks

|February 8, 2008

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We developed a neural network algorithm for overload control in telecom networks. This advanced method improves network performance and reduces decision-making time, outperforming existing local and centralized control techniques.

Area of Science:

Telecommunications Engineering
Artificial Intelligence
Control Systems

Background:

Call processors in telecom networks face overload during peak traffic, necessitating robust control mechanisms.
Existing overload control methods often rely on predictive control with limited local information.

Purpose of the Study:

To propose a novel neural-network-based algorithm for overload control in telecom call processors.
To evaluate the performance of the proposed neural control against existing algorithms.

Main Methods:

A neural-network algorithm utilizing a group of neural controllers.
Controllers trained using examples generated by a globally optimal control method.
Simulations to compare performance metrics like throughput and response to traffic upsurges.

Related Experiment Videos

Main Results:

Neural controllers demonstrated superior performance in throughput and response to traffic upsurges compared to local control algorithms.
Neural control significantly reduced computational time for decision-making.
The proposed method is suitable for real-time implementation.

Conclusions:

The proposed neural-network algorithm offers an effective solution for overload control in telecom networks.
This approach enhances network resilience and efficiency during high traffic periods.
Neural control provides a computationally efficient and real-time implementable alternative to centralized methods.