Approximation algorithms and hardness of integral concurrent flow

We study an integral counterpart of the classical Maximum Concurrent Flow problem, that we call Integral Concurrent Flow (ICF). In the basic version of this problem (basic-ICF), we are given an undirected n-vertex graph G with edge capacities c(e), a subset T of vertices called terminals, and a demand D(t,t′) for every pair (t,t′) of the terminals. The goal is to find a maximum value λ, and a collection P of paths, such that every pair (t,t′) of terminals is connected by ⌊ λ·D(t,t′) ⌋ paths in P, and the number of paths containing any edge e is at most c(e). We show an algorithm that achieves a poly log n-approximation for basic-ICF, while violating the edge capacities by only a constant factor. We complement this result by proving that no efficient algorithm can achieve a factor α-approximation with congestion c for any values α,c satisfying α·c=O(log log n/log log log n), unless NP ⊆ ZPTIME(n ^{poly log n}). We then turn to study the more general group version of the problem (group=ICF), in which we are given a collection (S ₁,T ₁),...,(S _k,T _k)} of pairs of vertex subsets, and for each 1 ≤ i ≤ k, a demand D _i is specified. The goal is to find a maximum value λ and a collection P of paths, such that for each i, at least ⌊ λ·D _i⌋ paths connect the vertices of S _i to the vertices of T _i, while respecting the edge capacities. We show that for any 1 ≤ c ≤ O(log log n), no efficient algorithm can achieve a factor O(n ^{1/(2 2c+3)})-approximation with congestion c for the problem, unless NP ⊆ DTIME(n ^{O(log log n)}). On the other hand, we show an efficient randomized algorithm that finds a poly log n-approximate solution with a constant congestion, if we are guaranteed that the optimal solution contains at least D ≥ k poly log n paths connecting every pair (S _i,T _i).