An analytical model for jitter in IP networks

ABSTRACT: Traditionally, IP network planning and design is mostly based on the average delay or loss constraints which can often be easily calculated. Jitter, on the other hand, is much more difficult to evaluate, but it is particularly important to manage the QoS of real-time and interactive services such as VoIP and streaming video. In this paper, we present simple formulas for the jitter of Poisson traffic in a single queue that can be quickly calculated . It takes into account the packets delay correlation and also the correlation of tandem queues that have a significant impact on the end-to-end jitter. We then extend them to the end-to-end jitter of a tagged stream based on a tandem queueing network. The results given by the model are then compared with event-driven simulations. We find that they are very accurate for Poisson traffic over a wide range of traffic loads and more importantly that they yield conservative values for the jitter so that they can be used in network design procedures. We also find some very counter-intuitive results. We show that jitter actually decreases with increasing load and the total jitter on a path depends on the position of congested links on that path. We finally point out some consequences of these results for network design procedures

A utility based framework for optimal network measurement

Packet level measurement is now routinely used to evaluate the loss and delay performance of broadband networks. In active measurement, probe packets provide samples of the loss and delay and from these samples the performance of the traffic as a whole can be deduced. However this is prone to errors: inaccuracy due to taking insufficient samples, self-interference due to injecting too many probe packets, and possible sample-correlation induced bias. In this paper we consider the optimisation of probing rate by treating all measurements as numerical experiments which can be optimally designed by using the statistical principles of design of experiments. We develop an analytical technique that quantifies an overall utility function associated with: (i) the disruption caused per probe packet, (ii) the bias and (iii) the variance as a function of the probing (sampling) rate. Our numerical results show that the optimal probing rate depends strongly on what parameter the network engineer seeks to measure.</p

Performance enhancement of large scale networks with heterogeneous traffic.

Finally, these findings are applied towards improving the performance of the Differentiated Services architecture by developing a new Refined Assured Forwarding framework where heterogeneous traffic flows share the same aggregate class. The new framework requires minimal modification to the existing Diffserv routers. The efficiency of the new architecture in enhancing the performance of Diffserv is demonstrated by simulation results under different traffic scenarios.This dissertation builds on the notion that segregating traffic with disparate characteristics into separate channels generally results in a better performance. Through a quantitative analysis, it precisely defines the number of classes and the allocation of traffic into these classes that will lead to optimal performance from a latency standpoint. Additionally, it weakens the most generally used assumption of exponential or geometric distribution of traffic service time in the integration versus segregation studies to date by including self-similarity in network traffic.The dissertation also develops a pricing model based on resource usage in a system with segregated channels. Based on analytical results, this dissertation proposes a scheme whereby a service provider can develop compensatory and fair prices for customers with varying QoS requirements under a wide variety of ambient traffic scenarios.This dissertation provides novel techniques for improving the Quality of Service by enhancing the performance of queue management in large scale packet switched networks with a high volume of traffic. Networks combine traffic from multiple sources which have disparate characteristics. Multiplexing such heterogeneous traffic usually results in adverse effects on the overall performance of the network

RouteNet: leveraging graph neural networks for network modeling and optimization in SDN

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Network modeling is a key enabler to achieve efficient network operation in future self-driving Software-Defined Networks. However, we still lack functional network models able to produce accurate predictions of Key Performance Indicators (KPI) such as delay, jitter or loss at limited cost. In this paper we propose RouteNet, a novel network model based on Graph Neural Network (GNN) that is able to understand the complex relationship between topology, routing, and input traffic to produce accurate estimates of the per-source/destination per-packet delay distribution and loss. RouteNet leverages the ability of GNNs to learn and model graph-structured information and as a result, our model is able to generalize over arbitrary topologies, routing schemes and traffic intensity. In our evaluation, we show that RouteNet is able to predict accurately the delay distribution (mean delay and jitter) and loss even in topologies, routing and traffic unseen in the training (worst case MRE = 15.4%). Also, we present several use cases where we leverage the KPI predictions of our GNN model to achieve efficient routing optimization and network planning.This work was supported in part by the Polish Ministryof Science and Higher Education with the subvention funds of the Facultyof Computer Science, Electronics and Telecommunications, AGH University,in part by the Spanish MINECO under Contract TEC2017-90034-C2-1-R(ALLIANCE), in part by the Catalan Institution for Research and AdvancedStudies (ICREA) and the FI-AGAUR Grant by the Catalan Government, andin part by PL-Grid Infrastructure.Peer ReviewedPostprint (author's final draft

RouteNet-Erlang: A graph neural network for network performance evaluation

Network modeling is a fundamental tool in network research, design, and operation. Arguably the most popular method for modeling is Queuing Theory (QT). Its main limitation is that it imposes strong assumptions on the packet arrival process, which typically do not hold in real networks. In the field of Deep Learning, Graph Neural Networks (GNN) have emerged as a new technique to build data-driven models that can learn complex and non-linear behavior. In this paper, we present RouteNet-Erlang, a pioneering GNN architecture designed to model computer networks. RouteNet-Erlang supports complex traffic models, multi-queue scheduling policies, routing policies and can provide accurate estimates in networks not seen in the training phase. We benchmark RouteNet-Erlang against a state-of-the-art QT model, and our results show that it outperforms QT in all the network scenarios.This publication is part of the Spanish I+D+i project TRAINER-A (ref.PID2020-118011GB-C21), funded by MCIN/ AEI/10.13039/501100011033. This work is also partially funded by the Catalan Institution for Research and Advanced Studies (ICREA) and the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund.Peer ReviewedPostprint (author's final draft

Investigation of delay jitter of heterogeneous traffic in broadband networks

Scope and Methodology of Study: A critical challenge for both wired and wireless networking vendors and carrier companies is to be able to accurately estimate the quality of service (QoS) that will be provided based on the network architecture, router/switch topology, and protocol applied. As a result, this thesis focuses on the theoretical analysis of QoS parameters in term of inter-arrival jitter in differentiated services networks by deploying analytic/mathematical modeling technique and queueing theory, where the analytic model is expressed in terms of a set of equations that can be solved to yield the desired delay jitter parameter. In wireless networks with homogeneous traffic, the effects on the delay jitter in reference to the priority control scheme of the ARQ traffic for the two cases of: 1) the ARQ traffic has a priority over the original transmission traffic; and 2) the ARQ traffic has no priority over the original transmission traffic are evaluated. In wired broadband networks with heterogeneous traffic, the jitter analysis is conducted and the algorithm to control its effect is also developed.Findings and Conclusions: First, the results show that high priority packets always maintain the minimum inter-arrival jitter, which will not be affected even in heavy load situation. Second, the Gaussian traffic modeling is applied using the MVA approach to conduct the queue length analysis, and then the jitter analysis in heterogeneous broadband networks is investigated. While for wireless networks with homogeneous traffic, binomial distribution is used to conduct the queue length analysis, which is sufficient and relatively easy compared to heterogeneous traffic. Third, develop a service discipline called the tagged stream adaptive distortion-reducing peak output-rate enforcing to control and avoid the delay jitter increases without bound in heterogeneous broadband networks. Finally, through the analysis provided, the differential services, was proved not only viable, but also effective to control delay jitter. The analytic models that serve as guidelines to assist network system designers in controlling the QoS requested by customer in term of delay jitter