STAG-based Dynamic Two-commodity Maximum Flow Algorithm for Time-varying Networks

Abstract

The multi-commodity flow problem plays an important role in network optimization, routing and service scheduling. With the network partitioning and intermittent connectivity, the commodity flows in time-varying networks are different from that over the static networks. As an NP-hard problem, existing works can only obtain suboptimal results on maximizing the multi-commodity flow of dynamic networks, due to the time variant network characteristics and coupling commodity relationships. In this paper, we propose a graph-based flow algorithm to solve the maximum two-commodity flow problem over the time-varying networks. Specially, we exploit storage time aggregated graph (STAG) to model the time variant network topology, link contact and node buffer resources. And through analyzing the relationship between two different commodities, we simplify the coupling two-commodity flow problem as the two single-commodity flow ones. As such, a STAG-based dynamic combined flow algorithm is proposed to maximize the two-commodity flow. Finally, we demonstrate the performance of the proposed algorithm through simulations.