The proportional differentiation model provides the network operator with the 'tuning knobs' for adjusting the per-hop quality-of-service (QoS) ratios between classes, independent of the class loads. This paper applies the proportional model in the differentiation of queueing delays, and investigates appropriate packet scheduling mechanisms. Starting from the proportional delay differentiation (PDD) model, we derive the average queueing delay in each class, show the dynamics of the class delays under the PDD constraints, and state the conditions in which the PDD model is feasible. The feasibility model of the model can be determined from the average delays that result with the strict priorities scheduler. We then focus on scheduling mechanisms that can implement the PDD model, when it is feasible to do so. The proportional average delay (PAD) scheduler meets the PDD constraints, when they are feasible, but it exhibits a pathological behavior in short timescales. The waiting time priority (WTP) scheduler, on the other hand, approximates the PDD model closely, even in the short timescales of a few packet departures, but only in heavy load conditions. PAD and WTP serve as motivation for the third scheduler, called hybrid proportional delay (HPD). HPD approximates the PDD model closely, when the model is feasible, independent of the class load distribution. Also, HPD provides predictable delay differentiation even in short timescales.

Proportional differentiated services: delay differentiation and packet scheduling

Internet applications and users have very diverse service expectations, making the current same-service-to-all model inadequate and limiting. In the relative differentiated services approach, the network traffic is grouped in a small number of service classes which are ordered based on their packet forwarding quality, in terms of per-hop metrics for the queueing delays and packet losses. The users and applications, in this context, can adaptivelychoose the class that best meets their quality and pricing constraints, based on the assurance that higher classes will be better, or at least no worse, than lower classes. In this work, we propose the proportional differentiation model as a way to refine and quantify this basic premise of relative differentiated services. The proportional differentiation model aims to provide the network operator with the 'tuning knobs' for adjusting the quality spacing between classes, independent of the class loads; this cannot be achieved with other relative differentiation models, such as strict prioritization or capacity differentiation. We apply the proportional model on queueing-delay differentiation only, leaving the problem of coupled delay and loss differentiation for future work. We discuss the dynamics of the proportional delay differentiation model and state the conditions under which it is feasible. Then, we identify and evaluate (using simulations) two packet schedulers that approximate the proportional differentiation model in heavy-load conditions, even in short timescales. Finally, we demonstrate that such per-hop and class-based mechanisms can provide consistent end-to-end differentiation to individual flows from different classes, independently of the network path and flow characteristics.

http://www.cs.uccs.edu/~xzhou/teaching/CS522/Projects/GIT_Dovroli_TON02.pdf

The proportional service model has gained attention as an effective solution for quantitative service differentiation in IP networks. In particular, this differentiation scheme is controllable, consistent, and scalable. Thus, it gives network operators convenient management of services and resources even in a large-scale network. We give an overview of research efforts on this QoS model. Details about implementation strategies for various QoS metrics are provided. We also discuss how to achieve absolute service bounds in this relative differentiation model with different approaches. Several problems such as feasibility in differentiation are mentioned as open research issues at the end.

Proportional differentiation: a scalable QoS approach

The need for improved network traffic and bandwidth management becomes urgent as increasing amount of real time applications begin to coexist within current shared infrastructure. Recent developments within the research community and within commercial products provide numerous technologies that may be applied as solutions. The most popular are differentiated services (DiffServ) with numerous different scheduling disciplines. Weighted fair queuing (WFQ) offers fair queuing that divides the available bandwidth across queues of traffic based on weights. Each flow is associated with an independent queue assigned with a weight to ensure that important traffic gets higher priority over less important traffic. It is established, that in times of congestion WFQ is not capable to implement differentiation service, so in this paper we combine weighted fair queueing (WFQ) and LLQ (low latency queue) scheduling disciplines to ensure quality of service (QoS) for high priority bursty video conferencing, voice and data services at the same time

Analysis of qos assurance using weighted fair queueing (WQF) scheduling discipline with low latency queue (LLQ)

The issue of bandwidth provisioning for Per Hop Behavior (PHB) aggregates in Differentiated Services (DiffServ) networks has received a lot of attention from researchers. However, most proposed methods need to determine the amount of bandwidth to provision at the time of connection admission. This assumes that traffic in admitted flows always conforms to predefined specifications, which would need some form of traffic shaping or admission control before reaching the ingress of the domain. This paper proposes an adaptive provisioning mechanism based on reinforcement-learning principles, which determines at regular intervals the amount of bandwidth to provision to each PHB aggregate. The mechanism adjusts to maximize the amount of revenue earned from a usage-based pricing model. The novel use of a continuous-space, gradient-based learning algorithm, enables the mechanism to require neither accurate traffic specifications nor rigid admission control. Using ns-2 simulations, we demonstrate using Weighted Fair Queuing, how our mechanism can be implemented in a DiffServ network.

/pdf/adaptive-provisioning-of-differentiated-services-networks-1zjck1ydrr.pdf

Adaptive provisioning of differentiated services networks based on reinforcement learning

Differentiation service (Diffserv) is regarded as one of the practical architectures to realize quality of service (QoS) on the Internet Relative differentiated service, which achieves relative QoS differentiation between traffic classes, is a simple and easily-deployed service model Based on the concept of relative differentiated service, Dovrolis et al (ACM SIGMETRICS Performance Evaluation Review vol27, no1, pp204-5, 1999; IEEE Network, September 1999; ACM SIGCOMM-99, September 1999) proposed a proportional differentiation service model which guarantees the ratios of service differences between classes They claimed that weighted fair queuing (WFQ) is not suitable for implementing relative differentiation service and employed priority-based scheduling algorithms in their model In this paper, we extend WFQ and apply it to proportional delay differentiation service The extended WFQ algorithm adjusts the weighting of each class dynamically so that the delay differences between classes can be well controlled Simulations show that the proposed methods can realize proportional delay differentiation service effectively and efficiently

Meng Cheng Chen

Papers

Proportional delay differentiation service based on weighted fair queuing