Dynamic Flow Scheduling With Uncertain Flow Duration in Optical Data Centers

Optical switching based on wavelength division multiplexing has become a promising network technology to scale the performance of data centers. It provides high bisection bandwidth with low power consumption and low complexity of network wiring. However, it raises new challenges for the flow scheduling problem due to the dynamic arrival of traffic flows with unknown service duration combined with the circuit-switched nature of optical networks and wavelength continuity constraint. While the knowledge of flow service time helps to use resources in a better way to increase the revenue, in practice, the service time cannot be accurately specified. In this paper, we address the problem of flow scheduling in optical data centers considering the above challenges. We first develop an optimization formulation using Markov decision process that can estimate the flow termination time and revenue for cloud providers in a long run under the uncertainty in flow service time. Since solving the optimization formulation is mathematically intractable, we then develop heuristic scheduling algorithms for both scenarios: with known and with unknown flow service time. We use a probabilistic model to address the uncertainty due to unknown flow service time. We design a flow scheduling framework that integrates the proposed algorithms to perform flow scheduling in optical data center networks. We evaluate the proposed algorithms through comprehensive simulations and compare their performance against that of a baseline algorithm. The results show that the proposed algorithms achieve significant performance improvement compared with the baseline algorithm.