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Learning-based resource optimization in asynchronous transfer mode (ATM) networks
S Al-Sharhan1, F Karray, W Gueaieb
1Dept. of Syst. Design Eng., Univ. of Waterloo, Canada.
Summary
This study introduces computational intelligence for efficient bandwidth allocation in asynchronous transfer mode (ATM) networks. Soft computing methods offer a flexible solution to the accuracy-simplicity trade-off in ATM bandwidth management.
Area of Science:
- Computer Science
- Telecommunications Engineering
Background:
- Traditional fluid flow models for bandwidth allocation in asynchronous transfer mode (ATM) networks struggle with computational complexity.
- These conventional methods exhibit inefficiencies when handling diverse and competing bandwidth demands from various services.
- Approximation techniques, while simpler, can lead to inaccuracies in bandwidth estimation.
Purpose of the Study:
- To address the limitations of conventional bandwidth allocation techniques in ATM networks.
- To explore the application of computational intelligence, specifically soft computing, for improved bandwidth management.
- To investigate a flexible control mechanism that balances accuracy and simplicity in ATM bandwidth allocation.
Main Methods:
- Utilizing computational intelligence tools, including neural networks and neurofuzzy controllers.
- Applying soft computing-based controllers to learn from examples and manage indeterminate nonlinear input-output relations.
- Implementing these techniques to tackle the bandwidth allocation problem in asynchronous transfer mode networks.
Main Results:
- Soft computing-based controllers demonstrate effectiveness in managing complex and varying bandwidth requirements.
- The proposed approach offers a flexible control mechanism for asynchronous transfer mode networks.
- A fundamental trade-off between accuracy and simplicity in bandwidth allocation is addressed.
Conclusions:
- Computational intelligence provides a robust solution for efficient bandwidth allocation in asynchronous transfer mode networks.
- Soft computing techniques effectively overcome the limitations of traditional and approximation-based methods.
- The developed control mechanism offers a practical and flexible approach to ATM network resource utilization.
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