Buffer allocation optimization in ATM switching networks using ALOPEX algorithm
We describe the use of a stochastic algorithm, called ALOPEX, which could be implemented in VLSI for optimizing the buffer allocation process in ATM switching networks. We present the results of computer simulations for buffer allocation in ATM switching networks using the ALOPEX algorithm. The algorithm uses a scalar cost function which is a measure of global performance. The ALOPEX works by broadcasting the global cost function to all neural processors in the neural network. Since each neural processor solely depends on the global cost function no interaction is needed between the neural processors and the algorithm is more amenable to massively parallel implementation. The application of the ALOPEX algorithm for the buffer allocation optimization in ATM networks assumes limited buffer capacity. The proposed ALOPEX-based approach takes advantage of the favorable control characteristics of the algorithm such as high adaptability and high speed collective computing power for effective buffer utilization. The proposed model uses complete sharing buffer allocation strategy and enhances its performance for high traffic loads by regulating the buffer allocation process dynamically.
atm switching neural networks optimization algorithms buffer management