Greedy distributed optimization of multi-commodity flows
Proceedings of the twenty-sixth annual ACM symposium on Principles of distributed computing
| Hi-index | 0.00 |
We consider solutions for distributed unsplittable multicommodity flow problems, which are solved by multiple agents operating in a cooperative but uncoordinated manner. A requirement here is that each commodity routes its entire demand along a single path at any point in time. Assuming that the minimum capacity of any edge is at least polylogarithmic factor larger than the maximum demand of any commodity, we present a greedy distributed randomized algorithm, which with high probability, achieves 1+ε approximation, in O(H · logO(1)) m · (1/ε)0(1)) iterations where H ≤ n is an upper bound on the number edges allowed on any flow-path. No prior results exist for our distributed model. Our algorithm is based on simulating the splittable multicommodity flow solution of [1] and picking a path for routing the entire flow according to the probability distribution given by the splittable flow. We use Chernoff bounds to show that the real edge-congestions are sufficiently close to the edge-congestions in the simulation for the analysis of [1] to hold.