Week 7: Traffic Models and QoS

Transcription

1 Week 7: Traffic Models and QoS Acknowledgement: Some slides are adapted from Computer Networking: A Top Down Approach Featuring the Internet, 2 nd edition, J.F Kurose and K.W. Ross All Rights Reserved, copyright Network Performance, 2010s1 7-1

2 Outline What is QoS? What does Internet traffic look like? Is it Poisson? What are the implications for network performance? Approaches to providing QoS: Laissez faire approach No change to network; Application-level bag of tricks Structured approach Change network to provide some performance guarantees QoS mechanisms: Packet classification and marking Packet policing/shaping Packet scheduling Resource allocation IETF IntServ and DiffServ frameworks Network Performance, 2010s1 7-2

3 What is QoS? Applications: Existing: , ftp, web; delay insensitive, loss sensitive Emerging: VoIP, multimedia; delay sensitive, loss insensitive Streaming stored/live audio and video Real-time interactive audio and video Integrated network to support existing and new apps Best-effort model adequate? QoS network provides application with level of performance needed for application to function. Network Performance, 2010s1 7-3

4 Internet Traffic: Need a model All performance techniques must make some assumptions regarding traffic Analytical models: arrival and service time distributions Simulation: traffic generators Experiments: traffic traces or real traffic Analysis of trace data A trace is captured from a live network Normally want to statistically characterize the trace to build a model, but may be able to use the trace directly How do we know our sample is typical or large enough? Network Performance, 2010s1 7-4

5 Markovian Models Poisson Process Appropriate if there is a large number of independent users and no source dominates? We know and love it! (good handle on M/M/1 queues) Variations: µ 01 rate λ rate λ 2 µ 10 Markov Modulated Poisson Process (MMPP) A Poisson arrival process with time-varying arrival rate λ(t) Process modulating the Poisson arrivals has a Markov chain Markov Modulated Fluid Process (MMFP) Embedded Markov models, Regression models, Network Performance, 2010s1 7-5

6 Traffic Burstiness Variability in traffic rate / volume Why is burstiness important? Peak traffic demands on buffer resources can lead to overflow and lost traffic Peak demands may create quality of service (QoS) problems in a network Need to characterize burstiness for traffic sources in a QoS environment Can be characterized in many ways: Ratio of peak rate to mean rate Coefficient of variation of traffic load over different intervals Index of dispersion of intervals (IDI) Index of sipersion of counts (IDC) Spectral (frequency) characteristics Stochastic process entropy rate Network Performance, 2010s1 7-6

7 Traffic Modeling Pre-1990 s: Traffic modeling in the world of telephony was the basis for initial network models Assumed Poisson arrival process Assumed exponential call duration Well established queuing literature based on these assumptions Enabled very successful engineering of telephone networks In 1989, Leland and Wilson begin taking high resolution traffic traces at Bellcore Ethernet traffic from a large research lab 100 µsec time stamps Packet length, status, 60 bytes of data Mostly IP traffic (a little NFS) Four data sets over three year period Over 100 million packets in traces Traces considered representative of normal use Network Performance, 2010s1 7-7

8 Measured Traffic Network Performance, 2010s1 7-8

9 Poisson Traffic Network Performance, 2010s1 7-9

10 Generated vs. Measured Traffic Network Performance, 2010s1 7-10

11 Fractals: Scaling property A Poisson process When observed on a fine time scale will appear bursty When aggregated on a coarse time scale will flatten (smooth) to white noise A Self-Similar (fractal) process A phenomenon that is self-similar looks or behaves the same when viewed at different degrees of magnification or different scales on a dimension (time or space) When aggregated over wide range of time scales will maintain its bursty characteristic Hurst parameter H (0.5 H 1) measures degree of self-similarity Network Performance, 2010s1 7-11

12 Why Do We Care? If network traffic is self-similar, there is significant amount of clustering at all time scales Requires more buffering Leads to higher queueing delays Mean waiting time Queue occupancy Network Performance, 2010s1 7-12

13 IP QoS History Inception: ToS byte in IP header 1986: TCP developed Early-1990s: QoS mechanisms for routers: Packet scheduling Packet dropping Traffic conditioning Resource allocation Mid-1990s: IntServ framework firm guarantees ( contract ) Late-1990s: DiffServ framework Classes that receive differential service 2000s: MPLS and Traffic Engineering Network Performance, 2010s1 7-13

14 QoS in Today s Internet TCP/UDP/IP: best-effort service no guarantees on delay, loss?????? But you said multimedia apps requires QoS and level of performance to be effective!????? Today s Internet multimedia applications use application-level techniques to mitigate (as best possible) effects of delay, loss Network Performance, 2010s1 7-14

15 Ad-hoc approach: Application-level solutions Example: Internet Phone UDP: avoid TCP congestion control (delays) for timesensitive traffic Delay compensation: adaptive play-out delay at client Loss compensation: FEC, interleaving, retransmissions Bandwidth estimation: server side matches stream bandwidth to available client-to-server path bandwidth chose among pre-encoded stream rates dynamic server encoding rate Network Performance, 2010s1 7-15

16 Structured approach: Principles Example: 1Mbps IP phone, FTP share 1.5 Mbps link. bursts of FTP can congest router, cause audio loss want to give priority to audio over FTP Principle 1 packet classification and marking needed for router to distinguish traffic Network Performance, 2010s1 7-16

17 Principles for QOS (contd.) what if applications misbehave (audio sends higher than declared rate) Policing/shaping: force source to adherence to bandwidth allocations Principle 2 provide protection (isolation) for one class from others Network Performance, 2010s1 7-17

18 Principles for QOS (contd.) Allocating fixed (non-sharable) bandwidth to flow: inefficient use of bandwidth if flows doesn t use its allocation Principle 3 While providing isolation, it is desirable to use resources as efficiently as possible Network Performance, 2010s1 7-18

19 Principles for QOS (contd.) Basic fact of life: can not support traffic demands beyond link capacity Principle 4 Resource allocation and Call Admission: network may block call (e.g., busy signal) if it cannot meet needs Network Performance, 2010s1 7-19

20 Summary of QoS Principles Let s next look at mechanisms for achieving this. Network Performance, 2010s1 7-20

21 Packet Classification and Marking Classification can be based on: 5-tuple: <src-ip, dst-ip, proto, src-port, dst-port> Ethernet MAC addresses Packet content: e.g. URL Marking done in IP ToS byte, now renamed the Diff-Serv Code Point (DSCP) byte Core routers can use marking, need not reclassify Network Performance, 2010s1 7-21

22 Policing and Shaping Goal: limit traffic to not exceed declared parameters Three common-used criteria: (Long term) Average Rate Peak Rate (Maximum) Burst Size Policing/Shaping Mechanism: Token Bucket: limit input to specified Burst Size and Average Rate. bucket can hold b tokens tokens generated at r token/sec unless bucket full over interval of length t: number of packets/bytes admitted no more than (r t + b). Network Performance, 2010s1 7-22

23 Packet Scheduling scheduling: choose next packet to send on link FIFO (first in first out) scheduling: send in order of arrival to queue example: supermarket check-out Network Performance, 2010s1 7-23

24 Scheduling Policies (contd.) Priority scheduling: transmit highest priority queued packet multiple classes, with different priorities class may depend on marking or other header info, e.g. IP source/dest, port numbers, etc.. example: airline check-in? Network Performance, 2010s1 7-24

25 Scheduling Policies (contd.) Weighted Fair Queuing: generalized Round Robin each class gets weighted amount of service in each cycle real-world example? Network Performance, 2010s1 7-25

26 Resource Reservation RSVP (ReSerVation Protocol) call setup, signaling traffic, QoS declaration per-element admission control QoS-sensitive scheduling (e.g., WFQ) request/ reply Network Performance, 2010s1 7-26

27 IETF Integrated Services (IntServ) Objective: QOS guarantees for individual application sessions resource reservation: routers maintain state info of allocated resources, QoS req s admit/deny new call setup requests: Question: can newly arriving flow be admitted with performance guarantees while not violated QoS guarantees made to already admitted flows? Network Performance, 2010s1 7-27

28 IntServ Architecture Policing/shaping: token bucket Per-flow classification and queueing WFQ scheduling Resource reservation / call admission All the above combine to provide guaranteed upper bound on delay, i.e., QoS guarantee! arriving traffic token rate, r bucket size, b WFQ per-flow rate, R D = b/r max Network Performance, 2010s1 7-28

29 IETF Differentiated Services (DiffServ) Concerns with Intserv: Scalability: signaling, maintaining per-flow router state difficult with large number of flows Flexible Service Models: Intserv has only two classes. Also want qualitative service classes behaves like a wire relative service distinction: Platinum, Gold, Silver Diffserv approach: simple functions in network core, relatively complex functions at edge routers (or hosts) Do t define define service classes, provide functional components to build service classes Network Performance, 2010s1 7-29

30 Diffserv Architecture Edge router: - per-flow traffic management - marks packets as in-profile and out-profile b marking r scheduling... Core router: - per class traffic management - buffering and scheduling based on marking at edge - preference given to in-profile packets Network Performance, 2010s1 7-30

31 How should the Internet evolve to better support multimedia? Integrated services philosophy: Fundamental changes in Internet so that apps can reserve end-to-end bandwidth Requires new, complex software in hosts & routers Laissez-faire no major changes more bandwidth when needed content distribution, application-layer multicast application layer Differentiated services philosophy: Fewer changes to Internet infrastructure, yet provide 1st and 2nd class service. What s your opinion? Network Performance, 2010s1 7-31