Data networks
Fair end-to-end window-based congestion control
IEEE/ACM Transactions on Networking (TON)
Stability of end-to-end algorithms for joint routing and rate control
ACM SIGCOMM Computer Communication Review
Reducing Congestion Effects in Wireless Networks by Multipath Routing
ICNP '06 Proceedings of the Proceedings of the 2006 IEEE International Conference on Network Protocols
Evolutionary Approach to Maxmin-Fair Network-Resource Allocation
SAINT '08 Proceedings of the 2008 International Symposium on Applications and the Internet
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This paper proposes an evolutionary approach to the network traffic optimization under the constraint of congestion avoidance. The individuals of the evolving population directly represents a set of paths in a network, and corresponding cross-over and mutation operators are provided. The optimization is a global one, i.e. it will not optimize the paths independently but also taking link sharing into account. To avoid the situation that the optimization will result in no traffic for some of the senders (which is also an element of the feasible space in congestion avoidance), we use the user fairness concept. A general approach to user fairness is also provided. The fitness of an individual (path set) is computed from the total traffic in the maxmin fairness state. Experiments on certain graph structures were performed. The results were compared with a path selection strategy based on single path evaluation only. The experiments for networks in the dimension 10 to 80 nodes demonstrate that an increase in performance of around 10% can be achieved in many cases, even with rather small population sizes and number of generations.