V1.0 - 20050426 Telecommunications Industry AssociationTR-30.3/09-03-004 Arlington, VA, March 30-31, 2009.

Slides:



Advertisements
Similar presentations
1 Network Telecommunication Group University of Pisa - Information Engineering department January Speaker: Raffaello Secchi Authors: Davide Adami.
Advertisements

10/11/2014Anue Systems, Inc. 1 v Telecommunications Industry AssociationTR-30.3/ Lake Buena Vista, FL December.
A Centralized Scheduling Algorithm based on Multi-path Routing in WiMax Mesh Network Yang Cao, Zhimin Liu and Yi Yang International Conference on Wireless.
1 CONGESTION CONTROL. 2 Congestion Control When one part of the subnet (e.g. one or more routers in an area) becomes overloaded, congestion results. Because.
Tiziana Ferrari Differentiated Services Test: Report1 Differentiated Service Test REPORT TF-TANT Tiziana Ferrari Frankfurt, 1 Oct.
CS640: Introduction to Computer Networks Aditya Akella Lecture 20 – QoS.
Telecommunications Industry AssociationTR-30.3/ By Teleconference April 26, 2010.
2005/12/06OPLAB, Dept. of IM, NTU1 Optimizing the ARQ Performance in Downlink Packet Data Systems With Scheduling Haitao Zheng, Member, IEEE Harish Viswanathan,
2014 Examples of Traffic. Video Video Traffic (High Definition) –30 frames per second –Frame format: 1920x1080 pixels –24 bits per pixel  Required rate:
Priority Scheduling and Buffer Management for ATM Traffic Shaping Authors: Todd Lizambri, Fernando Duran and Shukri Wakid Present: Hongming Wu.
SKELETON BASED PERFORMANCE PREDICTION ON SHARED NETWORKS Sukhdeep Sodhi Microsoft Corp Jaspal Subhlok University of Houston.
1 “Multiplexing Live Video Streams & Voice with Data over a High Capacity Packet Switched Wireless Network” Spyros Psychis, Polychronis Koutsakis and Michael.
1 Call Admission Control Carey Williamson Department of Computer Science University of Calgary.
Adaptive Sampling for Sensor Networks Ankur Jain ٭ and Edward Y. Chang University of California, Santa Barbara DMSN 2004.
Performance Evaluation of IP Telephony over University Network A project funded by University Fast Track By M. Kousa, M Sait, A. Shafi, A. Khan King Fahd.
802.11n MAC layer simulation Submitted by: Niv Tokman Aya Mire Oren Gur-Arie.
Multiple Sender Distributed Video Streaming Thinh Nguyen, Avideh Zakhor appears on “IEEE Transactions On Multimedia, vol. 6, no. 2, April, 2004”
A General approach to MPLS Path Protection using Segments Ashish Gupta Ashish Gupta.
CSE 401N Multimedia Networking-2 Lecture-19. Improving QOS in IP Networks Thus far: “making the best of best effort” Future: next generation Internet.
Differentiated Multimedia Web Services Using Quality Aware Transcoding S. Chandra, C.Schlatter Ellis and A.Vahdat InfoCom 2000, IEEE Journal on Selected.
MAC Layer Protocols for Sensor Networks Leonardo Leiria Fernandes.
Network Simulation Internet Technologies and Applications.
IETF 90: VNF PERFORMANCE BENCHMARKING METHODOLOGY Contributors: Sarah Muhammad Durrani: Mike Chen:
Traffic Modeling.
QoS Guarantees  introduction  call admission  traffic specification  link-level scheduling  call setup protocol  required reading: text, ,
Dynamic versus Static Traffic Policing: A New Approach for Videoconference Traffic over Wireless Cellular Networks Author: Polychronis Koutsakis Anukrati.
CSE QoS in IP. CSE Improving QOS in IP Networks Thus far: “making the best of best effort”
Tiziana Ferrari Diffserv deployment in the wide area: network design and testing1 Diffserv deployment in the wide area: network design and testing Tiziana.
Distributed Multimedia March 19, Distributed Multimedia What is Distributed Multimedia?  Large quantities of distributed data  Typically streamed.
جلسه دهم شبکه های کامپیوتری به نــــــــــــام خدا.
Univ. of TehranAdv. topics in Computer Network1 Advanced topics in Computer Networks University of Tehran Dept. of EE and Computer Engineering By: Dr.
Quality of Service Karrie Karahalios Spring 2007.
Network Instruments VoIP Analysis. VoIP Basics  What is VoIP?  Packetized voice traffic sent over an IP network  Competes with other traffic on the.
ACN: RED paper1 Random Early Detection Gateways for Congestion Avoidance Sally Floyd and Van Jacobson, IEEE Transactions on Networking, Vol.1, No. 4, (Aug.
A T M (QoS).
ECS 152A 4. Communications Techniques. Asynchronous and Synchronous Transmission Timing problems require a mechanism to synchronize the transmitter and.
All Rights Reserved © Alcatel-Lucent | TIA 30.3 Contribution | August 2010 Telecommunications Industry AssociationTR-30.3/ Arlington, VA.
Methods for providing Quality of Service in WLANs W.Burakowski, A. Beben, J.Sliwinski Institute of Telecommunications, Warsaw University of Technology,
Anue Systems Inc1 v Telecommunications Industry AssociationTR-30.3/ Lake Buena Vista, FL December 8 - 9, 2008.
10/24/2015Anue Systems, Inc. 1 v Telecommunications Industry AssociationTR-30.3/ Lake Buena Vista, FL December.
Active Measurements on the AT&T IP Backbone Len Ciavattone, Al Morton, Gomathi Ramachandran AT&T Labs.
Multimedia networking: outline 7.1 multimedia networking applications 7.2 streaming stored video 7.3 voice-over-IP 7.4 protocols for real-time conversational.
June 10, 1999 Discrete Event Simulation - 3 What other subsystems do we need to simulate? Although Packets are responsible for the largest amount of events,
Department of Computer Science Aruna Balasubramanian, Brian Neil Levine, Arun Venkataramani DTN Routing as a Resource Allocation Problem.
Demystifying Quality of Service (QoS). Page 2 What Is Quality of Service?  Ability of a network to provide improved service to selected network traffic.
Chapter 10 Verification and Validation of Simulation Models
Overload Prediction Based on Delay in Wireless OFDMA Systems E. O. Lucena, F. R. M. Lima, W. C. Freitas Jr and F. R. P. Cavalcanti Federal University of.
1 OUTPUT ANALYSIS FOR SIMULATIONS. 2 Introduction Analysis of One System Terminating vs. Steady-State Simulations Analysis of Terminating Simulations.
MMAC: A Mobility- Adaptive, Collision-Free MAC Protocol for Wireless Sensor Networks Muneeb Ali, Tashfeen Suleman, and Zartash Afzal Uzmi IEEE Performance,
Service Level Monitoring. Measuring Network Delay, Jitter, and Packet-loss  Multi-media applications are sensitive to transmission characteristics of.
Modeling and Simulating Time- Sensitive Networking Harri Laine.
Analyze Assure Accelerate Network Model for Evaluating Multimedia Transmission Performance Over Internet Protocol PN Will become TIA/EIA-921 Jack.
An Efficient Gigabit Ethernet Switch Model for Large-Scale Simulation Dong (Kevin) Jin.
Doc.: IEEE /1407r0 SubmissionSlide 1 Simulation Based Study of QoE Date: Authors: Chao-Chun Wang, MediaTek Nov NameAffiliationsAddressPhone .
CONGESTION CONTROL.
Doc.: IEEE /0178r0 Submission March 2005 Fahd Pirzada - DellSlide 1 IEEE TGT Streaming Media Apps Notice: This document has been prepared.
Efficient Gigabit Ethernet Switch Models for Large-Scale Simulation Dong (Kevin) Jin David Nicol Matthew Caesar University of Illinois.
An Efficient Gigabit Ethernet Switch Model for Large-Scale Simulation Dong (Kevin) Jin.
Quality of Service Schemes for IEEE Wireless LANs-An Evaluation 主講人 : 黃政偉.
Model-Based Estimation of Streaming Performance draft-ko-ippm-streaming-performance Ken Ko IPPM – Orlando – IETF86 –
Performance Limitations of ADSL Users: A Case Study Matti Siekkinen, University of Oslo Denis Collange, France Télécom R&D Guillaume Urvoy-Keller, Ernst.
1 Traffic Management Benchmarking Framework IETF 89 London draft-constantine-bmwg-traffic-management-03 Barry Constantine Tim.
Providing QoS in IP Networks
3/18/2016Anue Systems, Inc. 1 v Telecommunications Industry AssociationTR-30.3/ Arlington, VA July ,
Performance Comparison of Ad Hoc Network Routing Protocols Presented by Venkata Suresh Tamminiedi Computer Science Department Georgia State University.
Performance Evaluation of Scheduling in IEEE based Wireless Mesh Networks Bo Han, Weijia Jia,and Lidong Lin Computer Communications, 2007 Mei-zhen.
QoS & Queuing Theory CS352.
Chapter 10 Verification and Validation of Simulation Models
CONGESTION CONTROL.
CprE 458/558: Real-Time Systems
Presentation transcript:

v Telecommunications Industry AssociationTR-30.3/ Arlington, VA, March 30-31, 2009

Packet Delay Modeling From a network synchronization perspective TIA TR 30.3 Arlington, VA March 2009

Outline Introduction Status Need common test suites Issues Burst Definition Simple way to understand PDV Matlab approximation to help demystify Synthetic test cases Some example results Interesting 3D surface plots Recap

State of Sync Deployment of CES/TDMoIP/PTP has begun Strong financial incentive to do so Trials are ongoing with good success But is the technology really ready? Not yet. Examples: SLA metrics are insufficient to ensure success with Sync Need common conformance and interoperability test suites Allocate end-to-end PDV budget to individual NEs Drives future equipment development Anue Systems, Inc.

Need common test suites Three different approaches Collect real world traces from actual networks Good because it is realistic But no way to know if such traces are really worst case Service providers are sensitive about publishing such measurements G.8261 Appendix VI test bed Measure delays in a test bed under controlled conditions Realism and repeatability depends on many factors Synthetic test cases Create test cases that introduce controlled amounts of impairment Can be unrealistic but still very useful E.g: sinusoidal tolerance tests for SONET/SDH Can be realistic E.g: modem testsd Anue Systems, Inc.

Real world measurements Actual measurements are samples of network behavior. Good for research and initial development Not so good for ensuring interoperability or performance margin in a real network. What if conditions change? Network loads & characteristics increase Technologies change (e.g. DOCSIS3.0, WiMAX, xDSL, PON) And: Harder to control e.g. no control over results (e.g. minTDEV)

Real World Measurements Anue Systems, Inc. GEM RNC Capture CES or PTP and save to PDV Bidi & Multi stream PDV #1 PDV #2 1 2

Real world measurements Anue Systems, Inc.

G.8261 App VI Test case: Seems straightforward Anue Systems, Inc.

Build & Measure a G.8261 App VI Test bed Test case descriptions in Appendix VI are incomplete It probably seemed clear enough when written. And it wasn’t important enough to be normative anyway. Now, everyone is doing it. Differently. Top three issues 1.Load Definition 2.Burst Definition 3.Burst Definition Anue Systems, Inc.

A test bed Measure delay under G.8261 Appendix VI test conditions…..

Definition of Load Percentage Load percentage is A ratio: number of bits / max # bits possible Measured over a specified time interval Longest possible time is to measure over a whole test case (one value/average) Shortest possible time is to measure from one frame to the next (many values) Why does this matter? Because we need to know what 80% load means If 80% is the peak load Then 80% is max and applies only during the burst. During gap, assume that 64 and 576 byte generators stay same If 80% is an average over the whole test case (nominal), Then load measured during a single burst must be greater than 80%. The amount by which it exceeds 80% determines the burst duty cycle. Anue Systems, Inc.

Bursts not fully specified “Maximum size packets will occur in bursts lasting between 0.1s and 3s.” Two related questions: How long between bursts? What is the burst density? If “Load” means peak load Then there’s no guidance on the gaps between bursts Could pick fixed or random value, or just assume 50% duty cycle If “Load” means average load Then gap time will depend on burst density proportional to the amount by which the load during a burst exceeds the nominal load Anue Systems, Inc.

Burst Parameters Anue Systems, Inc. time Load Percentage Burst Length Burst Load Gap Length Nominal Load

Burst Examples (30 sec. each: 20%, 50%, 80%) Anue Systems, Inc.

Burst Examples (30 sec. each: 20%, 50%, 80%) Anue Systems, Inc.

Burst Examples (30 sec. each: 20%, 50%, 80%) Anue Systems, Inc.

Are there limits on Burst Density? Yes For two reasons TM1 has 64-byte disturbance packets at a constant rate (CBR). Bandwidth limit (1G) of the disturbance load generators. Though you could have separate generators for each packet size ($) Max BurstLoad = 100% - NomLoad*(F 64 +F 576 ) Anue Systems, Inc.

G.8261 App VI Test cases: What matters most? Primary factor: Configuration of disturbance load generator Bursts (and gaps) Disturbance packet size(s) Secondary factors Type of switch (L2 or L2/L3) CBR (almost or exactly … or not) Other important factors Non-ethernet links (especially asymmetric access technologies) DSL, Cable Modem, PON (beating effects w.r.t. packet schedulers) Uplink versus downlink Store & Forward (traditional ethernet) vs. cut-through (MPLS) Anue Systems, Inc.

What to expect PDV to look like? Why? function V = PDV(NumPkt, PktSz, DistLd, TM, NumSw, MinDelSw, UncDelSw) MinDly = NumSw * (PktSz + MinDelSw); % Store-fwd delay MaxLen = max(TM(:,1)); X = zeros(1,MaxLen); % X is PDV for one switch for d=1:size(TM,1) len = TM(d,1); frac = TM(d,2); X = X + (frac/len)*[ones(1,len), zeros(1,MaxLen-len)]; end V = zeros(1,MaxLen*(NumSw-1)); % V is end-to end PDV V(1) = binopdf(0,NumSw-1,max(1e-4,DistLd)); for j=1:NumSw-1 tmp = X; for k=2:j tmp = conv(tmp,X); end weight = binopdf(j,NumSw-1,max(1e-4,DistLd)); V = V + weight*[tmp,zeros(1,MaxLen*(NumSw-1)-size(tmp,2))]; end V = [zeros(1,MinDly) NumPkt*V]; V = conv(V,ClkUncer(NumSw,UncDelSw)); Anue Systems, Inc.

What to expect PDV to look like? Anue Systems, Inc.

Model-based impairment profiles Build a bottom-up model of each network element Construct test scenarios by connecting together various model elements and run a simulation Validate end-to-end results Challenges As network changes, model params must also change New technologies may require new model elements Harder to make a general model than to measure one sample

Discrete event simulation of test bed.. One way to model is to use a discrete event simulator Develop models for the switches and dummy traffic generators. Anue has developed one such model This is just the beginning of modeling Further refinements are possible Plots shown are for G.8261 test beds configured per Appendix VI TM2 bursts assume Bursts that have constant load (70%-60%) 10 store/fwd GigE switches, 20 disturbance load generators (fwd/rev) Clock uncertainty (each switch is asynchronous) No priority or congestion avoidance (QoS, RED/WFQ, VOQ)

Example model results (20% load TM2) Reference (test bed) S/W Model Results

G.8261 Test Case 2 (TM2) Load Steps Anue Systems, Inc.

G.8261 Test Case 3 (TM2) Slow Load Ramp 24hr Anue Systems, Inc.

G.8261 TC3: variations showing 3D PDV Anue Systems, Inc. Delay 50us 150us 20% 80% Load

G.8261 TC3: variations showing 3D PDV Anue Systems, Inc. Delay 50us 150us 20% 80% Load

G.8261 TC7 (TM2 E1 16ppb) - Beating Anue Systems, Inc.

G.8261 TC8 partA (Blocking) Anue Systems, Inc. Bursts of max len pkts: (this dimension is governed by the burst density and max packet size) Gap between bursts: (this dimension is governed by the min and medium size packets)

3D Surface plot for TC8 ( blocking ) Anue Systems, Inc. Delay 50us 150us 0% 50% Load

3D Surface plot for TC8 ( blocking ) Anue Systems, Inc. Delay 50us 150us 0% 50% Load

G.8261 TC13 TM2 (fwd/rev steps) Anue Systems, Inc.

Recap Deployment has started Need metrics for measuring PDV Need common, repeatable test cases Focus on conformance and interoperability Burst parameters need better specification Simple matlab code can predict first order PDV behavior Helps understand measurements from lab Synthetic model Results are quite realistic

Thank You!

Questions? Contact me! Chip Webb Anue Systems 9111 Jollyville Rd Suite 100 ( preferred ) +1 (512) x102

Backup Slides Anue Systems, Inc.

Effect of reverse traffic: For L2/L3 enterprise switch DUMMY LOAD DUMMY LOAD Capture Packets Capture Packets DUT

Effect of reverse traffic: For L2/L3 enterprise switch Anue Systems, Inc.

Effect of reverse traffic: minTDEV Anue Systems, Inc.