Lecture Notes in Computer Science Edited by G. Goos, J. Hartmanis and J. van Leeuwen

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1 Lecture Notes in Computer Science 2050 Edited by G. Goos, J. Hartmanis and J. van Leeuwen

2 3 Berlin Heidelberg New York Barcelona Hong Kong London Milan Paris Singapore Tokyo

3 Jean-Yves Le Boudec Patrick Thiran Network Calculus A Theory of Deterministic Queuing Systems for the Internet 13

4 Series Editors Gerhard Goos, Karlsruhe University, Germany Juris Hartmanis, Cornell University, NY, USA Jan van Leeuwen, Utrecht University, The Netherlands Authors Jean-Yves Le Boudec Patrick Thiran EPFL INN Ecublens 1015 Lausanne, Switzerland Cataloging-in-Publication Data applied for Die Deutsche Bibliothek - CIP-Einheitsaufnahme Le Boudec, Jean-Yves: Network calculus : a theory of deterministic queuing systems for the Internet / Jean-Yves Le Boudec ; Patrick Thiran. - Berlin ; Heidelberg ; New York ; Barcelona ; Hong Kong ; London ; Milan ; Paris ; Singapore ; Tokyo : Springer, 2001 (Lecture notes in computer science ; Vol. 2050) ISBN X CR Subject Classification (1998): C.2, D.4.4, D.4, E.4, H.3.5, H.4.3 ISSN ISBN X Springer-Verlag Berlin Heidelberg New York This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, re-use of illustrations, recitation, broadcasting, reproduction on microfilms or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer-Verlag. Violations are liable for prosecution under the German Copyright Law. Springer-Verlag Berlin Heidelberg New York a member of BertelsmannSpringer Science+Business Media GmbH Springer-Verlag Berlin Heidelberg 2001 Printed in Germany Typesetting: Camera-ready by author Printed on acid-free paper SPIN /

5 A Annelies A Joana, Maëlle, Audraine et Elias Amamère - JL A mes parents - PT Pour éviter les grumeaux Qui encombrent les réseaux Il fallait, c est compliqué, Maîtriser les seaux percés Branle-bas dans les campus On pourra dorénavant Calculer plus simplement Grâce à l algèbre Min-Plus Foin des obscures astuces Pour estimer les délais Et la gigue des paquets Place à Network Calculus - JL

6 Contents Introduction xiii I A First Course in Network Calculus 1 1 Network Calculus Models for Data Flows Cumulative Functions, Discrete Time versus Continuous Time Models Backlog and Virtual Delay Example: The Playout Buffer Arrival Curves Definition of an Arrival Curve Leaky Bucket and Generic Cell Rate Algorithm Sub-additivity and Arrival Curves Minimum Arrival Curve Service Curves Definition of Service Curve Classical Service Curve Examples Network Calculus Basics Three Bounds Are the Bounds Tight? Concatenation Improvement of Backlog Bounds Greedy Shapers Definitions Input-Output Characterization of Greedy Shapers Properties of Greedy Shapers Maximum Service Curve, Variable and Fixed Delay Maximum Service Curves Delay from Backlog Variable versus Fixed Delay Handling Variable Length Packets

7 viii CONTENTS An Example of Irregularity Introduced by Variable Length Packets The Packetizer A Relation between Greedy Shaper and Packetizer Packetized Greedy Shaper Lossless Effective Bandwidth and Equivalent Capacity Effective Bandwidth of a Flow Equivalent Capacity Example: Acceptance Region for a FIFO Multiplexer Proof of Theorem Bibliographic Notes Exercises Application of Network Calculus to the Internet GPS and Guaranteed Rate Schedulers Packet Scheduling GPS and a Practical Implementation (PGPS) Guaranteed Rate Schedulers The Integrated Services Model of the IETF The Guaranteed Service The Integrated Services Model for Internet Routers Reservation Setup with RSVP A Flow Setup Algorithm Multicast Flows Flow Setup with ATM Schedulability EDF Schedulers SCED Schedulers [65] Buffer Requirements Application to Differentiated Services Differentiated Services A Bounding Method for Aggregate Scheduling An Explicit Delay Bound for Differentiated Services Networks Bounds for Aggregate Scheduling with Dampers Exercises II Mathematical Background Basic Min-plus and Max-plus Calculus Min-plus Calculus Infimum and Minimum Dioid (R [f+1g; ^; +) A Catalog of Wide-sense Increasing Functions

8 CONTENTS ix Pseudo-inverse of Wide-sense Increasing Functions Concave, Convex and Star-shaped Functions Min-plus Convolution Sub-additive Functions Sub-additive Closure Min-plus Deconvolution Representation of Min-plus Deconvolution by Time Inversion Vertical and Horizontal Deviations Max-plus Calculus Max-plus Convolution and Deconvolution Linearity of Min-plus Deconvolution in Max-plus Algebra Exercises Min-plus and Max-plus System Theory Min-plus and Max-plus Operators Vector Notations Operators A Catalog of Operators Upper and Lower Semi-continuous Operators Isotone Operators Linear Operators Causal Operators Shift-invariant Operators Idempotent Operators Closure of an Operator Fixed Point Equation (Space Method) Main Theorem Examples of Application Fixed Point Equation (Time Method) Conclusion III A Second Course in Network Calculus Optimal Multimedia Smoothing Problem Setting Constraints Imposed by Lossless Smoothing Minimal Requirements on Delays and Playback Buffer Optimal Smoothing Strategies Maximal Solution Minimal Solution Set of Optimal Solutions Optimal Constant Rate Smoothing Optimal Smoothing versus Greedy Shaping

9 x CONTENTS 5.7 Comparison with Delay Equalization Lossless Smoothing over Two Networks Minimal Requirements on the Delays and Buffer Sizes for Two Networks Optimal Constant Rate Smoothing over Two Networks Bibliographic Notes FIFO Systems and Aggregate Scheduling Introduction General Bounds for Aggregate Scheduling Stability of a Network with Aggregate Scheduling The Open Issue of Stability The Ring is Stable Bounds for a FIFO Service Curve Element Bounds for a Network of FIFO CBR Servers Closed Form Bounds for an ATM Network with Strong Source Rate Conditions Proof of Theorem Bibliographic Notes Exercises Adaptive and Packet Scale Rate Guarantees Introduction Adaptive Guarantee Limitations of the Service Curve Abstraction Definition of Adaptive Guarantee Properties of Adaptive Guarantees Application to the Internet: Packet Scale Rate Guarantee Definition of Packet Scale Rate Guarantee Practical Realization of Packet Scale Rate Guarantee Proof of Theorem Bibliographic Notes Exercises Time Varying Shapers Introduction Time Varying Shapers Time Invariant Shaper with Initial Conditions Shaper with Non-empty Initial Buffer Leaky Bucket Shapers with Non-zero Initial Bucket Level Time Varying Leaky-Bucket Shaper Bibliographic Notes

10 CONTENTS xi 9 Systems with Losses A Representation Formula for Losses Losses in a Finite Storage Element Losses in a Bounded Delay Element Application 1: Bound on Loss Rate Application 2: Bound on Losses in Complex Systems Bound on Losses by Segregation between Buffer and Policer Bound on Losses in a VBR Shaper Solution to Skohorkhod s Reflection Problem with Two Boundaries Bibliographic Notes Bibliography 267 Index 273

11 Introduction What this Book is About Network Calculus is a set of recent developments that provide deep insights into flow problems encountered in networking. The foundation of network calculus lies in the mathematical theory of dioids, and in particular, the Min-Plus dioid (also called Min-Plus algebra). With network calculus, we are able to understand some fundamental properties of integrated services networks, window flow control, scheduling and buffer or delay dimensioning. This book is organized in three parts. Part I (Chapters 1 and 2) is a self contained, first course on network calculus. It can be used at the undergraduate level or as an entry course at the graduate level. The prerequisite is a first undergraduate course on linear algebra and one on calculus. Chapter 1 provides the main set of results for a first course: arrival curves, service curves and the powerful concatenation results are introduced, explained and illustrated. Practical definitions such as leaky bucket and generic cell rate algorithms are cast in their appropriate framework, and their fundamental properties are derived. The physical properties of shapers are derived. Chapter 2 shows how the fundamental results of Chapter 1 are applied to the Internet. We explain, for example, why the Internet integrated services internet can abstract any router by a rate-latency service curve. We also give a theoretical foundation to some bounds used for differentiated services. Part II contains reference material that is used in various parts of the book. Chapter 3 contains all first level mathematical background. Concepts such as min-plus convolution and sub-additive closure are exposed in a simple way. Part I makes a number of references to Chapter 3, but is still self-contained. The role of Chapter 3 is to serve as a convenient reference for future use. Chapter 4 gives advanced min-plus algebraic results, which concern fixed point equations that are not used in Part I. Part III contains advanced material; it is appropriate for a graduate course. Chapter 5 shows the application of network calculus to the determination of optimal playback delays in guaranteed service networks; it explains how fundamental bounds for multimedia streaming can be determined. Chapter 6 considers systems with aggregate scheduling. While the bulk of network calculus in this book applies to systems where schedulers are used to separate flows, there are still some interesting results that can be derived for such systems. Chapter 7 goes beyond the service curve defini- J.-Y. Le Boudec and P. Thiran, Network Calculus, LNCS 2050, pp. xiii xix, c Springer-Verlag Berlin Heidelberg 2001

12 xiv INTRODUCTION tion of Chapter 1 and analyzes adaptive guarantees, as they are used by the Internet differentiated services. Chapter 8 analyzes time varying shapers; it is an extension of the fundamental results in Chapter 1 that considers the effect of changes in system parameters due to adaptive methods. An application is to renegotiable reserved services. Lastly, Chapter 9 tackles systems with losses. The fundamental result is a novel representation of losses in flow systems. This can be used to bound loss or congestion probabilities in complex systems. Network calculus belongs to what is sometimes called exotic algebras or topical algebras. This is a set of mathematical results, often with high description complexity, that give insights into man-made systems such as concurrent programs, digital circuits and, of course, communication networks. Petri nets fall into this family as well. For a general discussion of this promising area, see the overview paper [30] and the book [24]. We hope to convince many readers that there is a whole set of largely unexplored, fundamental relations that can be obtained with the methods used in this book. Results such as shapers keep arrival constraints or pay bursts only once, derived in Chapter 1 have physical interpretations and are of practical importance to network engineers. All results here are deterministic. Beyond this book, an advanced book on network calculus would explore the many relations between stochastic systems and the deterministic relations derived in this book. The interested reader will certainly enjoy the pioneering work in [24] and [10]. The appendix contains an index of the terms defined in this book. Network Calculus, a System Theory for Computer Networks In the rest of this introduction we highlight the analogy between network calculus and what is called system theory. You may safely skip it if you are not familiar with system theory. Network calculus is a theory of deterministic queuing systems found in computer networks. It can also be viewed as the system theory that applies to computer networks. The main difference with traditional system theory, as the one that was so successfully applied to design electronic circuits, is that here we consider another algebra, where the operations are changed as follows: addition becomes computation of the minimum, multiplication becomes addition. Before entering the subject of the book itself, let us briefly illustrate some of the analogies and differences between min-plus system theory, as applied in this book to communication networks, and traditional system theory, applied to electronic circuits. Let us begin with a very simple circuit, such as the RC cell represented in Figure 1. If the input signal is the voltage x(t) 2 R, then the output y(t) 2 R of this simple circuit is the convolution of x by the impulse response of this circuit, which

13 INTRODUCTION xv is here h(t) =exp( t=rc)=rc for t 0: y(t) =(h Ω x)(t) = Z t 0 h(t s)x(s)ds: Consider now a node of a communication network, which is idealized as a (greedy) shaper. A (greedy) shaper is a device that forces an input flow x(t) to have an output y(t) that conforms to a given set of rates according to a traffic envelope ff (the shaping curve), at the expense of possibly delaying bits in the buffer. Here the input and output signals are cumulative flow, defined as the number of bits seen on the data flow in time interval [0;t]. These functions are non-decreasing with time t. Parameter t can be continuous or discrete. We will see in this book that x and y are linked by the relation y(t) =(ff Ω x)(t) = inf fff(t s) +x(s)g : s2r such that 0»s»t This relation defines the min-plus convolution between ff and x. 5 & \W (a) σ \W (b) Figure 1: An RC circuit (a) and a greedy shaper (b), which are two elementary linear systems in their respective algebraic structures. Convolution in traditional system theory is both commutative and associative, and this property allows to easily extend the analysis from small to large scale circuits. For example, the impulse response of the circuit of Figure 2(a) is the convolution of the impulse responses of each of the elementary cells: h(t) =(h 1 Ω h 2 )(t) = Z t 0 h 1 (t s)h 2 (s)ds: The same property applies to greedy shapers, as we will see in Chapter 1. The output of the second shaper of Figure 2(b) is indeed equal to y(t) = (ff Ω x)(t), where

14 xvi INTRODUCTION ff(t) =(ff 1 Ω ff 2 )(t) = inf fff 1(t s) +ff 2 (s)g : s2r such that 0»s»t This will lead us to understand the phenomenon known as pay burst only once already mentioned earlier in this introduction. K K \W (a) σ1 σ2 \W (b) Figure 2: The impulse response of the concatenation of two linear circuit is the convolution of the individual impulse responses (a), the shaping curve of the concatenation of two shapers is the convolution of the individual shaping curves (b). There are thus clear analogies between conventional circuit and system theory, and network calculus. There are however important differences too. A first one is the response of a linear system to the sum of the inputs. This is a very common situation, in both electronic circuits (take the example of a linear low-pass filter used to clean a signal x(t) from additive noise n(t), as shown in Figure 3(a)), and in computer networks (take the example a link of a buffered node with output link capacity C, where one flow of interest x(t) is multiplexed with other background traffic n(t), as shown in Figure 3(b)). Since the electronic circuit of Figure 3(a) is a linear system, the response to the sum of two inputs is the sum of the individual responses to each signal. Call y(t) the response of the system to the pure signal x(t), y n (t) the response to the noise n(t), and y tot (t) the response to the input signal corrupted by noise x(t) +n(t). Then y tot (t) =y(t) +y n (t). This useful property is indeed exploited to design the optimal linear system that will filter out noise as much as possible. If traffic is served on the outgoing link as soon as possible in the FIFO order, the node of Figure 3(b) is equivalent to a greedy shaper, with shaping curve ff(t) =Ct for t 0. It is therefore also a linear system, but this time in min-plus algebra. This means that the response to the minimum of two inputs is the minimum of the responses of the system to each input taken separately. However, this also mean that the response to the sum of two inputs is no longer the sum of the responses of

15 INTRODUCTION xvii QW K \WRWW \QW \W QW (a) & \WRWW (b) Figure 3: The response y tot (t) of a linear circuit to the sum of two inputs x + n is the sum of the individual responses (a), but the response y tot (t) of a greedy shaper to the aggregate of two input flows x + n is not the sum of the individual responses (b). the system to each input taken separately, because now x(t) +n(t) is a nonlinear operation between the two inputs x(t) and n(t): it plays the role of a multiplication in conventional system theory. Therefore the linearity property does unfortunately not apply to the aggregate x(t) +n(t). As a result, little is known on the aggregate of multiplexed flows. Chapter 6 will learn us some new results and problems that appear simple but are still open today. In both electronics and computer networks, nonlinear systems are also frequently encountered. They are however handled quite differently in circuit theory and in network calculus. Consider an elementary nonlinear circuit, such as the BJT amplifier circuit with only one transistor, shown in Figure 4(a). Electronics engineers will analyze this nonlinear circuit by first computing a static operating point y? for the circuit, when the input x? is a fixed constant voltage (this is the DC analysis). Next they will linearize the nonlinear element (i.e the transistor) around the operating point, to obtain a so-called small signal model, which a linear model of impulse response h(t) (this is the AC analysis). Now x lin (t) =x(t) x? is a time varying function of time within a small range around x?, so that y lin (t) =y(t) y? is indeed approximately given by y lin (t) ß (h Ω x lin )(t). Such a model is shown on Figure 4(b). The difficulty of a thorough nonlinear analysis is thus bypassed by restricting the input signal in a small range around the operating point. This allows to use a linearized model whose accuracy is sufficient to evaluate performance measures of interest, such as the gain of the amplifier. In network calculus, we do not decompose inputs in a small range time-varying part and another large constant part. We do however replace nonlinear elements by linear systems, but the latter ones are now a lower bound of the nonlinear system. We

16 xviii INTRODUCTION 9 \W [OLQW \OLQW (a) (b) Buffered window flow Controller Network β Π \W β \OLQW (c) (d) Figure 4: An elementary nonlinear circuit (a) replaced by a (simplified) linear model for small signals (b), and a nonlinear network with window flow control (c) replaced by a (worst-case) linear system (d). will see such an example with the notion of service curve, in Chapter 1: a nonlinear system y(t) =Π(x)(t) is replaced by a linear system y lin (t) =(fi Ωx)(t), where fi denotes this service curve. This model is such that y lin (t)» y(t) for all t 0, and all possible inputs x(t). This will also allow us to compute performance measures, such as delays and backlogs in nonlinear systems. An example is the window flow controller illustrated in Figure 4(c), which we will analyze in Chapter 4. A flow x is fed via a window flow controller in a network that realizes some mapping y =Π(x). The window flow controller limits the amount of data admitted in the network in such a way that the total amount of data in transit in the network is always less than some positive number (the window size). We do not know the exact mapping Π, we assume that we know one service curve fi for this flow, so that we can replace the nonlinear system of Figure 4(c) by the linear system of Figure 4(d), to obtain deterministic bounds on the end-to-end delay or the amount of data in transit. The reader familiar with traditional circuit and system theory will discover many other analogies and differences between the two system theories, while reading this book. We should insist however that no prerequisite in system theory is needed to discover network calculus as it is exposed in this book.

17 INTRODUCTION xix Acknowledgement We gratefully acknowledge the pioneering work of Cheng-Shang Chang and René Cruz; our discussions with them have influenced this text. We thank Anna Charny, Silvia Giordano, Olivier Verscheure, Frédéric Worm, Jon Bennett, Kent Benson, Vicente Cholvi, William Courtney, Juan Echaguë, Felix Farkas, Gérard Hébuterne and Milan Vojnović for the fruitful collaboration. The interaction with Rajeev Agrawal, François Baccelli, Guillaume Urvoy and Lothar Thiele is acknowledged with thanks. We are grateful to Holly Cogliati for helping with the preparation of the manuscript.