FlowTrace: Maximizing the Service Payoff of Heterogeneous Communications Networks

Abstract

Massive Internet of Things devices with rapidly growing traffic make heterogeneous communications network a commercial infrastructure. As a critical network metric that represents the value created by the network, the service payoff of the network is positively correlated with the network throughput only when the quality of service (QoS) delivered to data-flows is guaranteed. To maximize the service payoff of the network, we propose a so-called FlowTrace as an QoS guarantee scheme. We first model the upper bound of the service payoff in a network as the maximum posterior service payoff (MPSP). To facilitate online bandwidth allocation and approximate the upper bound of the service payoff, we formulate a dynamic weight-based online bandwidth allocation (DWOBA) problem by designing dynamic weight as the flow metric. To relieve the computational pressure in the centralized network controller and the following bottleneck effect, we then decompose the DWOBA problem to a two-step DWOBA problem. Moreover, we propose a semi-centralized bandwidth allocation algorithm based on parallel edge computing for the two-step DWOBA problem. Simulation results demonstrate that the proposed FlowTrace outperforms prevalent online methods with a 3 $\sim$ 10x service payoff improvement under heavy traffic load and the optimization result reaches 70 $\%$ of the MPSP.