Proportional Differentiation Model Research Articles

An IP cross-layer scheduler with closed-loop control for QoS provisioning in NGNs

Next-generation networks (NGNs) will support quality of service over a mixed wired and wireless IP-based infrastructure. A relative model of service differentiation in differentiated services architecture is a scalable solution for delivering multimedia traffic. However, considering the dynamic nature of radio channels specifically, it is difficult to achieve a given service provisioning working at the IP and lower layers separately as in the classical approach without a run-time adaptation of the system towards the target quality. This work describes an IP cross-layer scheduler able to support a Proportional Differentiation Model (PDM) for delay guarantees also over wireless. The key idea is to leverage feedbacks from the lower layers about the actual delays experienced by packets in order to tune at run-time the priority of the IP service classes in a closed-loop control with the objective of supporting a PDM at the interface on the whole, considering the cumulative latency across multiple layers, as relevant for the end-user. A simulation analysis demonstrates the prominent improvements in reliability and robustness of the proposal in the case of time-variant performance of the MAC and PHY layers with respect to the classical non-cross-layer approach and open-loop control. Furthermore, considerations on the required functionality and likely deployment scenarios highlight the scalability and backward compatibility of the designed solution, addressing a sustainable approach and smooth migration to NGNs.

Cross-layer Scheduling with Feedback for QoS Support

Next-Generation Networks (NGNs) will support Quality of Service (QoS) over a mixed wired and wireless IP-based infrastructure. A relative model of service differentiation in Differentiated Services architecture is a scalable solution for delivering multimedia traffic. However, considering the dynamic nature of radio channels typically, it is difficult to achieve a given service provisioning working at the IP and lower layers separately as in the classical approach, without a run-time adaptation of the system towards the target quality. This work describes an IP cross-layer scheduler able to support a Proportional Differentiation Model (PDM) for delay guarantees with content-awareness, also over wireless. The key idea is to leverage feedbacks from the lower layers about the actual delays experienced by packets in order to tune at run-time the priority of the IP service classes in a closed-loop control with the objective of supporting a PDM at the network node on the whole, considering the cumulative latency in crossing the first three layers of the protocol stack, as relevant for the end-user. A simulation analysis demonstrates the prominent improvements in reliability and robustness of the proposal in the case of time-variant performance of the MAC and PHY layers with respect to the classical non-cross-layer approach and the open- loop control. Furthermore, considerations on the required functionality and likely deployment scenarios highlight the scalability and backward compatibility of the designed solution in supporting the concept of network transparency for the delivering of critical applications, as of the e-health domain.

A new proportional differentiation scheme based on batch scheduling for optical burst switching networks

Optical burst switching (OBS) is a promising switching paradigm for building the next generation optical internet. The proportional differentiation model is very convenient for network operators to quantitatively adjust the quality differentiation among service classes. To provide proportional differentiated services for OBS networks, a proportional differentiation scheme based on batch scheduling is proposed in this article. The scheme adopts the batch scheduling idea to reserve data channel resources for a batch of data bursts. It helps to decrease burst dropping probability. When some data bursts are unsuccessfully scheduled, a preemption method is used to keep a proportional burst dropping probability among service classes according to the expected burst dropping probability equations given by the proportional differentiation model. The scheme has low computational complexity. Simulation results show that the scheme can provide proportional differentiated services and efficiently decrease the burst dropping probability.

Resource allocation optimization for quantitative service differentiation on server clusters

The goal of service differentiation is to provide different service quality levels to meet changing system configuration and resource availability and to satisfy different requirements and expectations of applications and users. In this paper, we investigate the problem of quantitative service differentiation on cluster-based delay-sensitive servers. The goal is to support a system-wide service quality optimization with respect to resource allocation on a computer system while provisioning proportionality fairness to clients. We first propose and promote a square-root proportional differentiation model. Interestingly, both popular delay factors, queueing delay and slowdown, are reciprocally proportional to the allocated resource usage. We formulate the problem of quantitative service differentiation as a generalized resource allocation optimization towards the minimization of system delay, defined as the sum of weighted delay of client requests. We prove that the optimization-based resource allocation scheme essentially provides square-root proportional service differentiation to clients. We then study the problem of service differentiation provisioning from an important relative performance metric, slowdown. We give a closed-form expression of the expected slowdown of a popular heavy-tailed workload model with respect to resource allocation on a server cluster. We design a two-tier resource management framework, which integrates a dispatcher-based node partitioning scheme and a server-based adaptive process allocation scheme. We evaluate the resource allocation framework with different models via extensive simulations. Results show that the square-root proportional model provides service differentiation at a minimum cost of system delay. The two-tier resource allocation framework can provide fine-grained and predictable service differentiation on cluster-based servers.

Achieving proportional loss rate differentiation in a wireless network with a multi-state link

Many algorithms for providing the proportional loss rate differentiation (PLD) model have been proposed under a wired network. However, these algorithms suffer from low performance in a wireless network, which has a multi-state link, a link with location-dependent and time-varying channel capacity. This paper proposes two channel-aware and debt-aware droppers, named no-debt reference (NDR) and random probability (RP), to provide PLD in a wireless network with a multi-state link. The two proposed droppers prefer dropping the packet destined to a poor channel to improve the performance, causing some loss debt, which will be compensated later to keep PLD. From simulation results, NDR and RP actually achieve similar accurate loss rate proportion, lower queueing delay and loss rate, and higher throughput, compared with other methods in the wireless environment.

An algorithm for achieving proportional delay differentiation

We consider the implementation of the proportional delay differentiation model with adaptive waiting time priority scheduler and we address the problem of finding the appropriate scheduler differentiation parameters that achieve this model. In this paper, we propose an algorithm that can compute an analytical solution of this problem.

A combined delay and throughput proportional scheduling scheme for differentiated services

The proportional differentiation model is a service differentiation approach for differentiated services networks that includes several desirable features such as controllability and predictability. This paper proposes and evaluates a scheduling mechanism for the combined control of delay and throughput metrics, within the context of the proportional differentiation model. The scheme is based on the well known Little's Law, and always exercises average delay differentiation, while it implements throughput differentiation only during overload periods, and if certain conditions as satisfied. A moving window averaging mechanism and an active queue management scheme are simultaneously, and, respectively, used to achieve control over the relative throughput values as well as the relative delay values between classes. The scheme does away with measurement of the actual packet delays, and state information is minimized. Some feasibility bounds are presented under the assumption of Poisson arrivals, and a simulation study shows the effectiveness of this scheme.

Weighted proportional loss rate differentiation of TCP traffic

AbstractRelative service differentiation refers to service models that provide assurances for the relative quality ordering between classes, rather than for the absolute service level in each class. An example is the proportional differentiation model which provides a way to control the quality spacing between classes locally at each hop. In this model, certain forwarding metrics are ratioed proportional to the class differentiation parameters that the network operator chooses. In this paper, we propose a new proportional loss rate differentiation mechanism that integrates relative loss rate differentiation directly into active queue management using random early detection. Copyright © 2004 John Wiley &Sons, Ltd.

Scaled time priority: an efficient approximation to waiting time priority

Under the framework of DiffServ, Dovrolis presented the proportional differentiation model (PDM) [IEEE Network, October 1999] and found the waiting time priority (WTP) scheme [Proc. ACM SIGCOMM, September 1999] to be a suitable scheduling algorithm to achieve proportional delay differentiation. In this paper, we propose scaled time priority (STP), which is an efficient approximation to waiting time priority (WTP). In particular, STP is able to provide near proportional delay at a complexity of O(1), which is lower than WTP's O( N) complexity, where N is the number of service classes in the system. Simulation results show that STP is able to emulate the performance of WTP.

Achieving proportional delay differentiation efficiently

In this paper, we focus on efficiently achieving Proportional Delay Differentiation (PDD), an instance of the Proportional Differentiation Model first proposed under the DiffServ framework [IEEE Network (1999)]. PDD can be achieved using a few algorithms, one of the most well-known being Waiting Time Priority(WTP). Using WTP [Proc. ACM SIGCOMM (1999)] as a reference, we show that our proposed Scaled Time Priority (STP) algorithm is able to provide near proportional delay at a complexity of O(1), which is lower than the O( N) complexity of WTP, where N is the number of service classes in the system. Simulation results also show that STP is able to emulate the performance of WTP.

High-performance scheduler to achieveproportional delay differentiation

The proportional delay differentiation model provides a consistent packet delay differentiation between various classes of service. The waiting time priority (WTP) scheduler is a priority scheduler in which the priority of a packet increases in proportion to its waiting time, and it is known to be the best scheduler to achieve the proportional delay differentiation model. The author proposes an advanced WTP (AWTP) scheduler, modified from WTP, that accounts for the packet transmission time. Simulation results reveal that when the link utilisation is moderate (60%-90%), this scheduler not only obtains a more accurate delay proportion than the WTP scheduler, no matter whether in short or long timescales, but also reduces the average queueing delay (waiting time). The effects of traffic load distribution, and the mean packet size and its coefficient of variation on both schedulers' performance are also examined. AWTP always outperforms WTP in these cases. However, AWTP may not maintain stable delay ratios under some traffic load distributions. A modification named AWTP/sup +/ is proposed and proved to overcome this problem.

Proportional differentiated services: delay differentiation and packet scheduling

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

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 adaptively choose 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.

A case for relative differentiated services and the proportional differentiation model

Internet applications and users have very diverse quality of service expectations, making the same-service-to-all model of the current Internet inadequate and limiting. There is a widespread consensus today that the Internet architecture has to extended with service differentiation mechanisms so that certain users and applications can get better service than others at a higher cost. One approach, referred to as absolute differentiated services, is based on sophisticated admission control and resource reservation mechanisms in order to provide guarantees or statistical assurances for absolute performance measures, such as a minimum service rate or maximum end-to-end delay. Another approach, which is simpler in terms of implementation, deployment, and network manageability, is to offer relative differentiated services between a small number of service classes. These classes are ordered based on their packet forwarding quality, in terms of per-hop metrics for the queuing delays and packet losses, giving the assurance that higher classes are better than lower classes. The applications and users, in this context, can dynamically select the class that best meets their quality and pricing constraints, without a priori guarantees for the actual performance level of each class. The relative differentiation approach can be further refined and quantified using the proportional differentiation model. This model aims to provide the network operator with the "tuning knobs" for adjusting the quality spacing between classes, independent of the class loads. When this spacing is feasible in short timescales, it can lead to predictable and controllable class differentiation, which ore two important features for any relative differentiation model. The proportional differentiation model can be approximated in practice with simple forwarding mechanisms (packet scheduling and buffer management) that we describe.