Title :
A CNN model for ATM cells scheduling in MIN switches
Author :
Guo, Donghui ; Parr, Gerard
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., California Univ., Berkeley, CA, USA
Abstract :
The multistage interconnection network (MIN) with self-routing is one of the best structures for implementation of IP/ATM switches, however, its internal cell blocking introduces more complications in the design of optimal scheduling algorithms for IP/ATM switches with quality of service (QoS) guarantees. A new model for cellular neural networks (CNN) is introduced to control traffic cell scheduling in MIN switches. the CNN scheduling algorithm will not only avoid cell blocking in MIN switches and therefore provide the global optimal solutions for traffic cell scheduling with QoS guarantees, but can also be programmed for different QoS reassignment. Our model is also resilient in the presence of switch element failure. The flexibility of our CNN model makes it very useful for applications in unreliable environments where the opportunity for link failure and node relocation are high.
Keywords :
cellular neural nets; multistage interconnection networks; optimisation; packet switching; quality of service; telecommunication network routing; telecommunication traffic; ATM cells scheduling; CNN model; CNN scheduling algorithm; IP/ATM switches; MIN switches; QoS guarantees; QoS reassignment; cellular neural networks; global optimal solutions; internal cell blocking; link failure; multistage interconnection network; node relocation; optimal scheduling algorithms; quality of service; self-routing; switch element failure; traffic cell scheduling control; unreliable environments; Algorithm design and analysis; Asynchronous transfer mode; Cellular neural networks; Communication system traffic control; Multiprocessor interconnection networks; Optimal scheduling; Quality of service; Scheduling algorithm; Switches; Traffic control;
Conference_Titel :
MILCOM 2002. Proceedings
Print_ISBN :
0-7803-7625-0
DOI :
10.1109/MILCOM.2002.1180419