A fully distributed traffic allocation algorithm for nonconcave utility maximization in connectionless communication networks. (November 2019)

• Record Type: Journal Article
• Authors: Wang, Jingyao; Ashour, Mahmoud; Lagoa, Constantino M.; Aybat, Necdet Serhat; Che, Hao
• Is Part Of: Automatica. Volume 109(2019)
• Publication Date: 2019-11
• Subjects: Traffic engineering -- Network utility maximization problem -- Nonconcave utility function -- Fully distributed traffic allocation algorithm -- Connectionless communication networks; Automatic control -- Periodicals; Automation -- Periodicals; 629.805
• DOI: 10.1016/j.automatica.2019.108506
• Languages: English
• ISSNs: 0005-1098

Abstract

Abstract: As IP video services have emerged to be the predominant Internet application, how to optimize the Internet resource allocation, while satisfying the quality of experience (QoE) for users of video services and other Internet applications becomes a challenge. This is because the QoE perceived by a user of video services can be characterized by a staircase function of the data rate, which is nonconcave and hence it is "hard" to find the optimal operating point. The work in this paper aims at tackling this challenge. It considers the packet routing problem among multiple end points in packet switching networks based on a connectionless, hop-by-hop forwarding paradigm. We model this traffic allocation problem using a fluid flow model and let the link bandwidth be the only resource to be shared. To maximize the utilization of resources and avoid congestion, we formulate the problem as a network utility maximization problem. More precisely, the objective of this paper is to design a Fully Distributed Traffic Allocation Algorithm (FDTAA) that is applicable to a large class of nonconcave utility functions. Moreover, FDTAA runs in a fully distributed way: it enables each router to independently address and route each data unit using immediate local information in parallel, without referring to any global information of the communication network. FDTAA requires minimum computation workload, since the routing decision made at each router is solely based on the destination information carried in each unit. In addition, the network utility values corresponding to the FDTAA iterate sequence converge to the optimal network utility value at the rate of O ( 1 ∕ K ), where K is the iteration counter. These theoretical results are exemplified by the simulation performed on an example communication network.