Optimal Rate-Reliability-Delay Tradeoff in Networks with Composite Links

Abstract

Abstract-Networks need to accommodate diverse applications with different Quality-of-Service (QoS) requirements. New ideas at the physical layer are being developed for this purpose, such as diversity embedded coding, which is a technique that combines high rates with high reliability. We address the problem of how to fully utilize different rate-reliability characteristics at the physical layer to support different types of traffic over a network and to jointly maximize their utilities.We set up a new framework based on utility maximization for networks with composite links, meaning that each link consists of sub-links that can attain different rate-reliability characteristics simultaneously. We incorporate delay, in addition to rate and reliability, into the utility functions. To accommodate different types of traffic, we propose distributed algorithms converging to the optimal rate-reliability-delay tradeoff based on capacity division and priority queueing. Numerical results show that compared with traditional codes, the new codes can provide higher network utilities for all traffic types simultaneously. The results also show that priority queueing achieves higher network utility than capacity division.

A distributed congestion and power control algorithm to achieve bounded average queuing delay in wireless networks

“…Consider the requirement of the average queuing delay on a link to be less than a specific amount, i.e., E(T ) ≤ d , where d is the local average delay allowed on link and T is the queuing delay on link . Let us assume a queuing delay model based on the assumption of general packet length distribution (with mean 1/μ and variance σ 2 on every link) and each link modeled as an M/G/1 queue [7,12,22,23]. Therefore the average queuing delay on each link is equal to (see [7] or [22])…”

“…In [10], a fixed symbol rate is assumed and different bit rates are achieved by choosing the transmitted symbols from the appropriate signal constellation (adaptive modulation); and in [11], several extensions of the NUM problem including queuing delay are outlined. In [12], the authors incorporate the delay in addition to rate and reliability in the NUM problem. However, they assume fixed capacity links that consist of sub-links with different ratereliability characteristics.…”

Rate control-based framework and algorithm for optimal provisioning

“…There are many applications of NUM in network engineering, such as optimal network resource allocation, Internet congestion control and protocol stack design [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. For example, in [2], the authors show that even with simple solutions that can only provide suboptimal utility maximization, one can enhance network throughput and reduce link saturation.…”

“…Congestion control in a network with delaysensitive traffic is studied in [4], which is modeled by adding explicit delay terms to the utility function measuring user's satisfaction of the Quality of Service (QoS). A new framework based on utility maximization for networks with composite links is set up in [5]. An optimal scheduling algorithm to maximize network utility for mesh network is developed in [6], and joint end-to-end congestion and contention control method based on network utility maximization for ad hoc networks is proposed in [7].…”

“…In this paper, we study the network resource allocation problem by extending the basic network utility maximization (NUM) framework developed in [5]. The basic NUM problem can be formulated as in (1).…”