Abstract
We consider a high speed integrated services network,
and investigate compare and contrast the performance of
two algorithms for solving the Aggregated Bandwidth
Allocation (BA) problem. The algorithms we focus our
attention are: (1) a classical constrained optimisation
(CCO) algorithm and (2) a constrained optimisation
Genetic Algorithm (GA). We adopt the Virtual Path (VP)
concept for ATM (Asynchronous Transfer Mode) networks
as an example, but expect the findings to be equally
applicable for other aggregated bandwidth allocation
problems, such as in networks using MPLS (Multiprotocol
Label Switching). We define a fair multiobjective
criterion for maximisation of network throughput, based
on a probability (density) function for bandwidth
demand. We convert to single optimisation problem and
study network topologies involving varying number of
nodes. We compare throughput, fairness and time
complexity of GA-BAVP and CCO-BAVP. The results on
maximising the throughput obtained with GA-BAVP and
CCO-BAVP are in close agreement, however when
considering fairness GA-BAVP outperforms CCO-BAVP,
especially for more complex topologies. Convergence of
the two algorithms appears similar, with GA-BAVP
outperforming CCO-BAVP in initial stages, and
vice-versa for longer time scales. However as problem
complexity increases the solution time for the Genetic
Algorithm does not increase as fast as the classical
constrained optimisation algorithm. A hybrid scheme is
also introduced, combining the benefits of both
algorithms. It exhibited better overall convergence
rate but the same solution as CCO-BAVP. More work is
needed to investigate the usefulness of GAs in larger
network topologies, as well as to multiobjective
bandwidth allocation problems.
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