Approximate Fairness with Quantized Congestion Notification for Multi-tenanted Data Centers

Size: px
Start display at page:

Download "Approximate Fairness with Quantized Congestion Notification for Multi-tenanted Data Centers"

Transcription

1 Approximate Fairness with Quantized Congestion Notification for Multi-tenanted Data Centers Abdul Kabbani Stanford University Joint work with: Mohammad Alizadeh, Masato Yasuda, Rong Pan, and Balaji Prabhakar 1

2 Multi-tenant DCs Cloud computing with multiple tenants Need to share networking resources in a programmable way One tenant/class shouldn t adversely affect another Different versions of TCP, some more aggressive than others Different transport protocols (eg. UDP) Malicious flows Flow1 Flow2 Flow3 Flow4 Flow5 2

3 Flow isolation Classical approach: isolate packets at buffers Followed by FQ/WFQ, GPS, DRR But, isolating packets in buffers can be hard/expensive Need per-flow queues, per-packet schedulers Lots of datapath work 3

4 Our Approach Key observations for providing bandwidth slices It is not necessary to do per-packet scheduling; fairness over few RTTs works well Focus on the larger flows; small (mice) flows impose work but don t consume bandwidth Provide bandwidth slices by sending congestion signals differentially, rather than scheduling AFD (Pan, Breslau, Prabhakar, Shenker, 2003) Uses a single queue, slices bandwidth via differential dropping 4

5 Goal 1 F b =2 QCN Rate=1Gbs 2 Rate=9Gbs F b =2 Switch 1 Rate=1Gbs F b =1 AF-QCN 2 Rate=9Gbs F b =7 Switch 5

6 Rest of the talk Quick overview of AFD Overview of QCN: L2 congestion control Approximate Fair QCN (AF-QCN) algorithm 6

7 AFD Based on 3 simple mechanisms Estimate per flow/class arrival rate counting per class bytes over fixed intervals ( T s ) averaging over multiple intervals Estimate fair share rate: Fair share = C / #flows Perform differential probabilistic dropping to drive arrival rate to fair (or weighted fair) rate 7

8 AFD Algorithm D i = Drop Probability for Class i Arriving Packets Class i M i = Arrival estimate for Class i (Bytes over interval T s ) D i 1-D i Qlen Qref Mfair = Mfair a1(qlen Qref) + a2(qlen_old Qref) Fair Share If M i F(Mfair,Min i,max i,w i, ) : No Drop (D i = 0) If M i > F(Mfair,Mini,Maxi,Wi, ) : D i > 0 such that M i (1-D i ) = F(Mfair,Min i,max i,w i, )

9 The QCN control loop Feedback S 1 D 1 S N Congestion Point D N Reaction Points 9

10 Reflection Probability QCN Congestion Point Consider the single-source, single-switch loop below Q eq Source Congestion Point (Switch) Dynamics: Sample packets, compute feedback (Fb), send it to source Fb = (Q-Q eq + w. dq/dt ) = (queue offset + w.rate offset) Pmin P max Quantized to 6 bits Fb 10

11 Rate QCN: Reaction Point Source (reaction point): Transmit regular Ethernet frames. When congestion message arrives: Multiplicative Decrease: CR CR(1 G d F b ) Fast Recovery Active Probing TR CR Target Rate Fast Recovery Rd Rd/2 Current Rate Rd/4 Rd/8 Active Probing Congestion message recd Time

12 AF-QCN 12

13 How does it work? 1 F b =2 QCN Rate=1Gbs 2 Rate=9Gbs F b =2 Switch 1 Rate=1Gbs F b =1 AF-QCN 2 Rate=9Gbs F b =7 Switch 13

14 AF-QCN Algorithm Upon sampling a pkt at the QCN switch, F b is computed as F b (1 )F b QCN F b AF F b-qcn : the same value calculated by the QCN CP (flowindependent) F b-af : a fairness term calculated as in AFD (flowdependent) α is small (chosen to be 1/8), to ensure good stability of QCN is retained Utilization (stability) first, fairness is second Major difference with DRR-type schedulers 14

15 Evaluation 15

16 Setup Simulations Static flows: Service rate: 10Gbps -> 2Gbps -> 10Gbps Class priorities: uniform, and variable weights, rate caps. Parking lot topology Dynamic flows: Bursty on-off source together with backlogged sources Poisson arrivals with backlogged sources NetFPGA hardware evaluation 16

17 Single Link, 4 Sources, 50us RTT QCN AF-QCN 17

18 AF-QCN with Different Weights w i = i Flow 4 is capped at 1Gbps at t=2sec 18

19 Parking Lot QCN AF-QCN 19

20 Dynamic Flows: Flow Completion Times (FCT) 8 RPs sharing one link - 4 RPs serving backlogged static flows - 4 RPs each serving 4 permanent connections (16 connections in total) Dynamics flows - Pareto size of mean 10KB - Poisson arrivals - 1Gbps total offered load Flow Size Bin (KB) FCT with QCN (usec) FCT with AF-QCN (usec) [1,10) [10,100) [100,1000) [1000, )

21 Hardware Implementation (1Gbps NetFPGA) Same Weight Different Weights 21

22 Summary AF-QCN Provides programmable bandwidth allocation via light-weight CP modifications Fair at the granularity of a few msecs Does not disturb the original QCN characteristics: stability/responsiveness/etc Improves flow completion times Similar approaches seem promising at L3 22

23 Back up 23

24 Single Link, 4 Sources, 400us RTT QCN AF-QCN 24

25 Dynamic Flows: Flow Completion Times (FCT) 8 RPs sharing one link - 4 RPs serving backlogged static flows - 4 RPs each serving 4 permanent connections (16 connections in total) Dynamics flows - Pareto size of mean 10KB - Poisson arrivals - 1Gbps total offered load Flow Size Bin (KB) FCT with QCN (usec) FCT with AF-QCN (usec) [1,10) 2.346, , 3.23 [10,100) 2.610, , 3.33 [100,1000) 5.037, , 3.04 [1000, ) 33.14, ,

26 Fairness achieved: 40 Sources, 50us RTT

27 1 Bursty Source + 3 Static Sources 10KB Bursts (totaling 1Gbps) 10KBBursts (totaling 10Gbps)

28 AF-QCN Algorithm Flow j s arrivals are estimated every T s (equals 1msec here) as where: m j (1-β)m j + βm j-new m j-new denotes the pkt arrivals within the last T s interval β is small enough to smooth down bursty arrivals (chosen to be 1/8)

Min Congestion Control for High- Speed Heterogeneous Networks. JetMax: Scalable Max-Min

Min Congestion Control for High- Speed Heterogeneous Networks. JetMax: Scalable Max-Min JetMax: Scalable Max-Min Min Congestion Control for High- Speed Heterogeneous Networks Yueping Zhang Joint work with Derek Leonard and Dmitri Loguinov Internet Research Lab Department of Computer Science

More information

Extended Ethernet Congestion Management (E 2 CM): Per Path ECM - A Hybrid Proposal

Extended Ethernet Congestion Management (E 2 CM): Per Path ECM - A Hybrid Proposal Extended Ethernet Congestion Management (E 2 CM): Per Path ECM - A Hybrid Proposal M. Gusat, C. Minkenberg and R. Luijten IBM Research GmbH, Zurich March 14 th 2007 Outline Status at 802.1 Critique Analysis

More information

These are special traffic patterns that create more stress on a switch

These are special traffic patterns that create more stress on a switch Myths about Microbursts What are Microbursts? Microbursts are traffic patterns where traffic arrives in small bursts. While almost all network traffic is bursty to some extent, storage traffic usually

More information

Processor Sharing Flows in the Internet

Processor Sharing Flows in the Internet STANFORD HPNG TECHNICAL REPORT TR4-HPNG4 Processor Sharing Flows in the Internet Nandita Dukkipati, Nick McKeown Computer Systems Laboratory Stanford University Stanford, CA 9434-93, USA nanditad, nickm

More information

CPU Scheduling Exercises

CPU Scheduling Exercises CPU Scheduling Exercises NOTE: All time in these exercises are in msec. Processes P 1, P 2, P 3 arrive at the same time, but enter the job queue in the order presented in the table. Time quantum = 3 msec

More information

NICTA Short Course. Network Analysis. Vijay Sivaraman. Day 1 Queueing Systems and Markov Chains. Network Analysis, 2008s2 1-1

NICTA Short Course. Network Analysis. Vijay Sivaraman. Day 1 Queueing Systems and Markov Chains. Network Analysis, 2008s2 1-1 NICTA Short Course Network Analysis Vijay Sivaraman Day 1 Queueing Systems and Markov Chains Network Analysis, 2008s2 1-1 Outline Why a short course on mathematical analysis? Limited current course offering

More information

Capturing Network Traffic Dynamics Small Scales. Rolf Riedi

Capturing Network Traffic Dynamics Small Scales. Rolf Riedi Capturing Network Traffic Dynamics Small Scales Rolf Riedi Dept of Statistics Stochastic Systems and Modelling in Networking and Finance Part II Dependable Adaptive Systems and Mathematical Modeling Kaiserslautern,

More information

cs/ee/ids 143 Communication Networks

cs/ee/ids 143 Communication Networks cs/ee/ids 143 Communication Networks Chapter 4 Transport Text: Walrand & Parakh, 2010 Steven Low CMS, EE, Caltech Agenda Internetworking n Routing across LANs, layer2-layer3 n DHCP n NAT Transport layer

More information

Impact of Cross Traffic Burstiness on the Packet-scale Paradigm An Extended Analysis

Impact of Cross Traffic Burstiness on the Packet-scale Paradigm An Extended Analysis Impact of ross Traffic Burstiness on the Packet-scale Paradigm An Extended Analysis Rebecca Lovewell and Jasleen Kaur Technical Report # TR11-007 Department of omputer Science University of North arolina

More information

Computer Networks ( Classroom Practice Booklet Solutions)

Computer Networks ( Classroom Practice Booklet Solutions) Computer Networks ( Classroom Practice Booklet Solutions). Concept Of Layering 0. Ans: (b) Sol: Data Link Layer is responsible for decoding bit stream into frames. 0. Ans: (c) Sol: Network Layer has the

More information

PIQI-RCP: Design and Analysis of Rate-Based Explicit Congestion Control

PIQI-RCP: Design and Analysis of Rate-Based Explicit Congestion Control PIQI-RCP: Design and Analysis of Rate-Based Explicit Congestion Control Saurabh Jain Joint work with Dr. Dmitri Loguinov June 21, 2007 1 Agenda Introduction Analysis of RCP QI-RCP PIQI-RCP Comparison Wrap

More information

Performance Analysis of Priority Queueing Schemes in Internet Routers

Performance Analysis of Priority Queueing Schemes in Internet Routers Conference on Information Sciences and Systems, The Johns Hopkins University, March 8, Performance Analysis of Priority Queueing Schemes in Internet Routers Ashvin Lakshmikantha Coordinated Science Lab

More information

Efficient Nonlinear Optimizations of Queuing Systems

Efficient Nonlinear Optimizations of Queuing Systems Efficient Nonlinear Optimizations of Queuing Systems Mung Chiang, Arak Sutivong, and Stephen Boyd Electrical Engineering Department, Stanford University, CA 9435 Abstract We present a systematic treatment

More information

Congestion Control. Need to understand: What is congestion? How do we prevent or manage it?

Congestion Control. Need to understand: What is congestion? How do we prevent or manage it? Congestion Control Phenomenon: when too much traffic enters into system, performance degrades excessive traffic can cause congestion Problem: regulate traffic influx such that congestion does not occur

More information

Internet Traffic Modeling and Its Implications to Network Performance and Control

Internet Traffic Modeling and Its Implications to Network Performance and Control Internet Traffic Modeling and Its Implications to Network Performance and Control Kihong Park Department of Computer Sciences Purdue University park@cs.purdue.edu Outline! Motivation! Traffic modeling!

More information

requests/sec. The total channel load is requests/sec. Using slot as the time unit, the total channel load is 50 ( ) = 1

requests/sec. The total channel load is requests/sec. Using slot as the time unit, the total channel load is 50 ( ) = 1 Prof. X. Shen E&CE 70 : Examples #2 Problem Consider the following Aloha systems. (a) A group of N users share a 56 kbps pure Aloha channel. Each user generates at a Passion rate of one 000-bit packet

More information

An Admission Control Mechanism for Providing Service Differentiation in Optical Burst-Switching Networks

An Admission Control Mechanism for Providing Service Differentiation in Optical Burst-Switching Networks An Admission Control Mechanism for Providing Service Differentiation in Optical Burst-Switching Networks Igor M. Moraes, Daniel de O. Cunha, Marco D. D. Bicudo, Rafael P. Laufer, and Otto Carlos M. B.

More information

Internet Congestion Control: Equilibrium and Dynamics

Internet Congestion Control: Equilibrium and Dynamics Internet Congestion Control: Equilibrium and Dynamics A. Kevin Tang Cornell University ISS Seminar, Princeton University, February 21, 2008 Networks and Corresponding Theories Power networks (Maxwell Theory)

More information

Latency and Backlog Bounds in Time- Sensitive Networking with Credit Based Shapers and Asynchronous Traffic Shaping

Latency and Backlog Bounds in Time- Sensitive Networking with Credit Based Shapers and Asynchronous Traffic Shaping Latency and Backlog Bounds in Time- Sensitive Networking with Credit Based Shapers and Asynchronous Traffic Shaping Ehsan Mohammadpour, Eleni Stai, Maaz Mohuiddin, Jean-Yves Le Boudec September 7 th 2018,

More information

Amr Rizk TU Darmstadt

Amr Rizk TU Darmstadt Saving Resources on Wireless Uplinks: Models of Queue-aware Scheduling 1 Amr Rizk TU Darmstadt - joint work with Markus Fidler 6. April 2016 KOM TUD Amr Rizk 1 Cellular Uplink Scheduling freq. time 6.

More information

A Mathematical Model of the Skype VoIP Congestion Control Algorithm

A Mathematical Model of the Skype VoIP Congestion Control Algorithm A Mathematical Model of the Skype VoIP Congestion Control Algorithm Luca De Cicco, S. Mascolo, V. Palmisano Dipartimento di Elettrotecnica ed Elettronica, Politecnico di Bari 47th IEEE Conference on Decision

More information

TCP over Cognitive Radio Channels

TCP over Cognitive Radio Channels 1/43 TCP over Cognitive Radio Channels Sudheer Poojary Department of ECE, Indian Institute of Science, Bangalore IEEE-IISc I-YES seminar 19 May 2016 2/43 Acknowledgments The work presented here was done

More information

Analysis of Rate-distortion Functions and Congestion Control in Scalable Internet Video Streaming

Analysis of Rate-distortion Functions and Congestion Control in Scalable Internet Video Streaming Analysis of Rate-distortion Functions and Congestion Control in Scalable Internet Video Streaming Min Dai Electrical Engineering, Texas A&M University Dmitri Loguinov Computer Science, Texas A&M University

More information

Reliable Data Transport: Sliding Windows

Reliable Data Transport: Sliding Windows Reliable Data Transport: Sliding Windows 6.02 Fall 2013 Lecture 23 Exclusive! A Brief History of the Internet guest lecture by Prof. Hari Balakrishnan Wenesday December 4, 2013, usual 6.02 lecture time

More information

Solutions to COMP9334 Week 8 Sample Problems

Solutions to COMP9334 Week 8 Sample Problems Solutions to COMP9334 Week 8 Sample Problems Problem 1: Customers arrive at a grocery store s checkout counter according to a Poisson process with rate 1 per minute. Each customer carries a number of items

More information

A Different Kind of Flow Analysis. David M Nicol University of Illinois at Urbana-Champaign

A Different Kind of Flow Analysis. David M Nicol University of Illinois at Urbana-Champaign A Different Kind of Flow Analysis David M Nicol University of Illinois at Urbana-Champaign 2 What Am I Doing Here??? Invite for ICASE Reunion Did research on Peformance Analysis Supporting Supercomputing

More information

DIMENSIONING BANDWIDTH FOR ELASTIC TRAFFIC IN HIGH-SPEED DATA NETWORKS

DIMENSIONING BANDWIDTH FOR ELASTIC TRAFFIC IN HIGH-SPEED DATA NETWORKS Submitted to IEEE/ACM Transactions on etworking DIMESIOIG BADWIDTH FOR ELASTIC TRAFFIC I HIGH-SPEED DATA ETWORKS Arthur W. Berger and Yaakov Kogan AT&T Labs 0 Crawfords Corner Rd. Holmdel J, 07733 U.S.A.

More information

Socket Programming. Daniel Zappala. CS 360 Internet Programming Brigham Young University

Socket Programming. Daniel Zappala. CS 360 Internet Programming Brigham Young University Socket Programming Daniel Zappala CS 360 Internet Programming Brigham Young University Sockets, Addresses, Ports Clients and Servers 3/33 clients request a service from a server using a protocol need an

More information

Network Traffic Characteristic

Network Traffic Characteristic Network Traffic Characteristic Hojun Lee hlee02@purros.poly.edu 5/24/2002 EL938-Project 1 Outline Motivation What is self-similarity? Behavior of Ethernet traffic Behavior of WAN traffic Behavior of WWW

More information

Controlling Burstiness in Fair Queueing Scheduling

Controlling Burstiness in Fair Queueing Scheduling TEL-AVIV UNIVERSITY RAYMOND AND BEVERLY SACKLER FACULTY OF EXACT SCIENCES SCHOOL OF COMPUTER SCIENCES Controlling Burstiness in Fair Queueing Scheduling Thesis submitted in partial fulfillment of the requirements

More information

Window Size. Window Size. Window Size. Time. Time. Time

Window Size. Window Size. Window Size. Time. Time. Time A Spectrum of TCP-friendly Window-based Congestion Control Algorithms Λ Shudong Jin Liang Guo Ibrahim Matta Azer Bestavros Computer Science Department Boston University Boston, MA 5 fjins, guol, matta,

More information

The Analysis of Microburst (Burstiness) on Virtual Switch

The Analysis of Microburst (Burstiness) on Virtual Switch The Analysis of Microburst (Burstiness) on Virtual Switch Chunghan Lee Fujitsu Laboratories 09.19.2016 Copyright 2016 FUJITSU LABORATORIES LIMITED Background What is Network Function Virtualization (NFV)?

More information

M/G/FQ: STOCHASTIC ANALYSIS OF FAIR QUEUEING SYSTEMS

M/G/FQ: STOCHASTIC ANALYSIS OF FAIR QUEUEING SYSTEMS M/G/FQ: STOCHASTIC ANALYSIS OF FAIR QUEUEING SYSTEMS MOHAMMED HAWA AND DAVID W. PETR Information and Telecommunications Technology Center University of Kansas, Lawrence, Kansas, 66045 email: {hawa, dwp}@ittc.ku.edu

More information

A Retrial Queueing model with FDL at OBS core node

A Retrial Queueing model with FDL at OBS core node A Retrial Queueing model with FDL at OBS core node Chuong Dang Thanh a, Duc Pham Trung a, Thang Doan Van b a Faculty of Information Technology, College of Sciences, Hue University, Hue, Viet Nam. E-mail:

More information

Wireless Internet Exercises

Wireless Internet Exercises Wireless Internet Exercises Prof. Alessandro Redondi 2018-05-28 1 WLAN 1.1 Exercise 1 A Wi-Fi network has the following features: Physical layer transmission rate: 54 Mbps MAC layer header: 28 bytes MAC

More information

Job Scheduling and Multiple Access. Emre Telatar, EPFL Sibi Raj (EPFL), David Tse (UC Berkeley)

Job Scheduling and Multiple Access. Emre Telatar, EPFL Sibi Raj (EPFL), David Tse (UC Berkeley) Job Scheduling and Multiple Access Emre Telatar, EPFL Sibi Raj (EPFL), David Tse (UC Berkeley) 1 Multiple Access Setting Characteristics of Multiple Access: Bursty Arrivals Uncoordinated Transmitters Interference

More information

Resource Allocation for Video Streaming in Wireless Environment

Resource Allocation for Video Streaming in Wireless Environment Resource Allocation for Video Streaming in Wireless Environment Shahrokh Valaee and Jean-Charles Gregoire Abstract This paper focuses on the development of a new resource allocation scheme for video streaming

More information

Distributed Systems Fundamentals

Distributed Systems Fundamentals February 17, 2000 ECS 251 Winter 2000 Page 1 Distributed Systems Fundamentals 1. Distributed system? a. What is it? b. Why use it? 2. System Architectures a. minicomputer mode b. workstation model c. processor

More information

A Generalized FAST TCP Scheme

A Generalized FAST TCP Scheme A Generalized FAST TCP Scheme Cao Yuan a, Liansheng Tan a,b, Lachlan L. H. Andrew c, Wei Zhang a, Moshe Zukerman d,, a Department of Computer Science, Central China Normal University, Wuhan 430079, P.R.

More information

communication networks

communication networks Positive matrices associated with synchronised communication networks Abraham Berman Department of Mathematics Robert Shorten Hamilton Institute Douglas Leith Hamilton Instiute The Technion NUI Maynooth

More information

DIMENSIONING BANDWIDTH FOR ELASTIC TRAFFIC IN HIGH-SPEED DATA NETWORKS

DIMENSIONING BANDWIDTH FOR ELASTIC TRAFFIC IN HIGH-SPEED DATA NETWORKS Submitted to IEEE/ACM Transactions on etworking DIMESIOIG BADWIDTH FOR ELASTIC TRAFFIC I HIGH-SPEED DATA ETWORKS Arthur W. Berger * and Yaakov Kogan Abstract Simple and robust engineering rules for dimensioning

More information

Congestion Control In The Internet Part 1: Theory. JY Le Boudec 2018

Congestion Control In The Internet Part 1: Theory. JY Le Boudec 2018 Congestion Control In The Internet Part 1: Theory JY Le Boudec 2018 1 Contents 1. What is the problem; congestion collapse 2. Efficiency versus Fairness 3. Definitions of fairness 4. Additive Increase

More information

c Copyright by Guanghui He, 2004

c Copyright by Guanghui He, 2004 c Copyright by Guanghui He, 24 EXPLOITATION OF LONG-RANGE DEPENDENCE IN INTERNET TRAFFIC FOR RESOURCE AND TRAFFIC MANAGEMENT BY GUANGHUI HE B.E., Tsinghua University, 1993 M.E., Tsinghua University, 1996

More information

Sensitivity Analysis of BCN with ZRL Congestion Benchmark. Part 1. Mitch Gusat and Cyriel Minkenberg IEEE 802 Dallas Nov. 2006

Sensitivity Analysis of BCN with ZRL Congestion Benchmark. Part 1. Mitch Gusat and Cyriel Minkenberg IEEE 802 Dallas Nov. 2006 Sensitivity Analysis of BCN with ZRL Congestion Benchmark Part Mitch Gusat and Cyriel Minkenberg IEEE 802 Dallas Nov. 2006 IBM Zurich Research Lab GmbH Next phase: BCN validation larger datacenter networks

More information

A Generalized Processor Sharing Approach to Flow Control in Integrated Services Networks: The Single Node Case. 1

A Generalized Processor Sharing Approach to Flow Control in Integrated Services Networks: The Single Node Case. 1 A Generalized Processor Sharing Approach to Flow Control in Integrated Services Networks: The Single Node Case 1 Abhay K Parekh 2 3 and Robert G Gallager 4 Laboratory for Information and Decision Systems

More information

Window Flow Control Systems with Random Service

Window Flow Control Systems with Random Service Window Flow Control Systems with Random Service Alireza Shekaramiz Joint work with Prof. Jörg Liebeherr and Prof. Almut Burchard April 6, 2016 1 / 20 Content 1 Introduction 2 Related work 3 State-of-the-art

More information

Assured Horizon A new Combined Framework for Burst Assembly and Reservation in Optical Burst Switched Networks

Assured Horizon A new Combined Framework for Burst Assembly and Reservation in Optical Burst Switched Networks Assured Horizon A new Combined Framework for Burst Assembly and Reservation in Optical Burst Switched Networks Klaus Dolzer University of Stuttgart, Institute of Communication Networks and Computer Engineering,

More information

Singular perturbation analysis of an additive increase multiplicative decrease control algorithm under time-varying buffering delays.

Singular perturbation analysis of an additive increase multiplicative decrease control algorithm under time-varying buffering delays. Singular perturbation analysis of an additive increase multiplicative decrease control algorithm under time-varying buffering delays. V. Guffens 1 and G. Bastin 2 Intelligent Systems and Networks Research

More information

Rate adaptation, Congestion Control and Fairness: A Tutorial. JEAN-YVES LE BOUDEC Ecole Polytechnique Fédérale de Lausanne (EPFL)

Rate adaptation, Congestion Control and Fairness: A Tutorial. JEAN-YVES LE BOUDEC Ecole Polytechnique Fédérale de Lausanne (EPFL) Rate adaptation, Congestion Control and Fairness: A Tutorial JEAN-YVES LE BOUDEC Ecole Polytechnique Fédérale de Lausanne (EPFL) December 2000 2 Contents 31 Congestion Control for Best Effort: Theory 1

More information

CSE 123: Computer Networks

CSE 123: Computer Networks CSE 123: Computer Networks Total points: 40 Homework 1 - Solutions Out: 10/4, Due: 10/11 Solutions 1. Two-dimensional parity Given below is a series of 7 7-bit items of data, with an additional bit each

More information

Switched Systems: Mixing Logic with Differential Equations

Switched Systems: Mixing Logic with Differential Equations research supported by NSF Switched Systems: Mixing Logic with Differential Equations João P. Hespanha Center for Control Dynamical Systems and Computation Outline Logic-based switched systems framework

More information

Some Background Information on Long-Range Dependence and Self-Similarity On the Variability of Internet Traffic Outline Introduction and Motivation Ch

Some Background Information on Long-Range Dependence and Self-Similarity On the Variability of Internet Traffic Outline Introduction and Motivation Ch On the Variability of Internet Traffic Georgios Y Lazarou Information and Telecommunication Technology Center Department of Electrical Engineering and Computer Science The University of Kansas, Lawrence

More information

CPU scheduling. CPU Scheduling

CPU scheduling. CPU Scheduling EECS 3221 Operating System Fundamentals No.4 CPU scheduling Prof. Hui Jiang Dept of Electrical Engineering and Computer Science, York University CPU Scheduling CPU scheduling is the basis of multiprogramming

More information

Congestion Control. Phenomenon: when too much traffic enters into system, performance degrades excessive traffic can cause congestion

Congestion Control. Phenomenon: when too much traffic enters into system, performance degrades excessive traffic can cause congestion Congestion Control Phenomenon: when too much traffic enters into system, performance degrades excessive traffic can cause congestion Problem: regulate traffic influx such that congestion does not occur

More information

CS276 Homework 1: ns-2

CS276 Homework 1: ns-2 CS276 Homework 1: ns-2 Erik Peterson October 28, 2006 1 Part 1 - Fairness between TCP variants 1.1 Method After learning ns-2, I wrote a script (Listing 3) that runs a simulation of one or two tcp flows

More information

Bounded Delay for Weighted Round Robin with Burst Crediting

Bounded Delay for Weighted Round Robin with Burst Crediting Bounded Delay for Weighted Round Robin with Burst Crediting Sponsor: Sprint Kert Mezger David W. Petr Technical Report TISL-0230-08 Telecommunications and Information Sciences Laboratory Department of

More information

High speed access links. High speed access links. a(t) process (aggregate traffic into buffer)

High speed access links. High speed access links. a(t) process (aggregate traffic into buffer) Long Range Dependence in Network Traffic and the Closed Loop Behaviour of Buffers Under Adaptive Window Control Arzad A. Kherani and Anurag Kumar Dept. of Electrical Communication Engg. Indian Institute

More information

CPU SCHEDULING RONG ZHENG

CPU SCHEDULING RONG ZHENG CPU SCHEDULING RONG ZHENG OVERVIEW Why scheduling? Non-preemptive vs Preemptive policies FCFS, SJF, Round robin, multilevel queues with feedback, guaranteed scheduling 2 SHORT-TERM, MID-TERM, LONG- TERM

More information

CAC investigation for video and data

CAC investigation for video and data CAC investigation for video and data E.Aarstad a, S.Blaabjerg b, F.Cerdan c, S.Peeters d and K.Spaey d a Telenor Research & Development, P.O. Box 8, N-7 Kjeller, Norway,egil.aarstad@fou.telenor.no b Tele

More information

Analytic Performance Evaluation of the RED Algorithm

Analytic Performance Evaluation of the RED Algorithm Prof. Dr. P. Tran-Gia Analytic Performance Evaluation of the RED Algorithm Stefan Köhler, Michael Menth, Norbert Vicari TCP Model RED Model TCP over RED Results TCP > Reliable transmission > Closed loop

More information

Scheduling: Queues & Computation

Scheduling: Queues & Computation Scheduling: Queues Computation achieving baseline performance efficiently Devavrat Shah LIDS, MIT Outline Two models switched network and bandwidth sharing Scheduling: desirable performance queue-size

More information

ECN or Delay: Lessons Learnt from Analysis of DCQCN and TIMELY

ECN or Delay: Lessons Learnt from Analysis of DCQCN and TIMELY EN or Delay: Lessons Learnt from Analysis of DQN and IMELY Yibo Zhu, Monia Ghobadi, Vishal Misra, Jitendra Padhye Microsoft olumbia University ABSRA Data center networks, and especially drop-free RoEv

More information

Controlo Switched Systems: Mixing Logic with Differential Equations. João P. Hespanha. University of California at Santa Barbara.

Controlo Switched Systems: Mixing Logic with Differential Equations. João P. Hespanha. University of California at Santa Barbara. Controlo 00 5 th Portuguese Conference on Automatic Control University of Aveiro,, September 5-7, 5 00 Switched Systems: Mixing Logic with Differential Equations João P. Hespanha University of California

More information

Dynamic resource sharing

Dynamic resource sharing J. Virtamo 38.34 Teletraffic Theory / Dynamic resource sharing and balanced fairness Dynamic resource sharing In previous lectures we have studied different notions of fair resource sharing. Our focus

More information

ESTIMATION OF THE BURST LENGTH IN OBS NETWORKS

ESTIMATION OF THE BURST LENGTH IN OBS NETWORKS ESTIMATION OF THE BURST LENGTH IN OBS NETWORKS Pallavi S. Department of CSE, Sathyabama University Chennai, Tamilnadu, India pallavi.das12@gmail.com M. Lakshmi Department of CSE, Sathyabama University

More information

Understanding TCP Vegas: A Duality Model

Understanding TCP Vegas: A Duality Model Understanding TCP Vegas: A Duality Model Steven Low Departments of CS and EE, Caltech, USA slow@caltech.edu Larry Peterson Limin Wang Department of CS, Princeton University, USA {llp,lmwang}@cs.princeton.edu

More information

COMP9334: Capacity Planning of Computer Systems and Networks

COMP9334: Capacity Planning of Computer Systems and Networks COMP9334: Capacity Planning of Computer Systems and Networks Week 2: Operational analysis Lecturer: Prof. Sanjay Jha NETWORKS RESEARCH GROUP, CSE, UNSW Operational analysis Operational: Collect performance

More information

Stochastic Optimization for Undergraduate Computer Science Students

Stochastic Optimization for Undergraduate Computer Science Students Stochastic Optimization for Undergraduate Computer Science Students Professor Joongheon Kim School of Computer Science and Engineering, Chung-Ang University, Seoul, Republic of Korea 1 Reference 2 Outline

More information

Chapter 5. Elementary Performance Analysis

Chapter 5. Elementary Performance Analysis Chapter 5 Elementary Performance Analysis 1 5.0 2 5.1 Ref: Mischa Schwartz Telecommunication Networks Addison-Wesley publishing company 1988 3 4 p t T m T P(k)= 5 6 5.2 : arrived rate : service rate 7

More information

Capacity management for packet-switched networks with heterogeneous sources. Linda de Jonge. Master Thesis July 29, 2009.

Capacity management for packet-switched networks with heterogeneous sources. Linda de Jonge. Master Thesis July 29, 2009. Capacity management for packet-switched networks with heterogeneous sources Linda de Jonge Master Thesis July 29, 2009 Supervisors Dr. Frank Roijers Prof. dr. ir. Sem Borst Dr. Andreas Löpker Industrial

More information

A Measurement-Analytic Approach for QoS Estimation in a Network Based on the Dominant Time Scale

A Measurement-Analytic Approach for QoS Estimation in a Network Based on the Dominant Time Scale 222 IEEE/ACM TRANSACTIONS ON NETWORKING, VOL. 11, NO. 2, APRIL 2003 A Measurement-Analytic Approach for QoS Estimation in a Network Based on the Dominant Time Scale Do Young Eun and Ness B. Shroff, Senior

More information

Congestion Control. Topics

Congestion Control. Topics Congestion Control Topics Congestion control what & why? Current congestion control algorithms TCP and UDP Ideal congestion control Resource allocation Distributed algorithms Relation current algorithms

More information

Analysis of Scalable TCP in the presence of Markovian Losses

Analysis of Scalable TCP in the presence of Markovian Losses Analysis of Scalable TCP in the presence of Markovian Losses E Altman K E Avrachenkov A A Kherani BJ Prabhu INRIA Sophia Antipolis 06902 Sophia Antipolis, France Email:altman,kavratchenkov,alam,bprabhu}@sophiainriafr

More information

TUNABLE LEAST SERVED FIRST A New Scheduling Algorithm with Tunable Fairness

TUNABLE LEAST SERVED FIRST A New Scheduling Algorithm with Tunable Fairness TUNABLE LEAST SERVED FIRST A New Scheduling Algorithm with Tunable Fairness Pablo Serrano, David Larrabeiti, and Ángel León Universidad Carlos III de Madrid Departamento de Ingeniería Telemática Av. Universidad

More information

Effect of the Traffic Bursts in the Network Queue

Effect of the Traffic Bursts in the Network Queue RICE UNIVERSITY Effect of the Traffic Bursts in the Network Queue by Alireza KeshavarzHaddad A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree Master of Science Approved, Thesis

More information

Lecture 5. Logical Effort Using LE on a Decoder

Lecture 5. Logical Effort Using LE on a Decoder Lecture 5 Logical Effort Using LE on a Decoder Mark Horowitz Computer Systems Laboratory Stanford University horowitz@stanford.edu Copyright 00 by Mark Horowitz Overview Reading Harris, Logical Effort

More information

MPTCP is not Pareto-Optimal: Performance Issues and a Possible Solution

MPTCP is not Pareto-Optimal: Performance Issues and a Possible Solution MPTCP is not Pareto-Optimal: Performance Issues and a Possible Solution Ramin Khalili, Nicolas Gast, Miroslav Popovic, Jean-Yves Le Boudec To cite this version: Ramin Khalili, Nicolas Gast, Miroslav Popovic,

More information

Stochastic Hybrid Systems: Applications to Communication Networks

Stochastic Hybrid Systems: Applications to Communication Networks research supported by NSF Stochastic Hybrid Systems: Applications to Communication Networks João P. Hespanha Center for Control Engineering and Computation University of California at Santa Barbara Talk

More information

Modeling Impact of Delay Spikes on TCP Performance on a Low Bandwidth Link

Modeling Impact of Delay Spikes on TCP Performance on a Low Bandwidth Link Modeling Impact of Delay Spikes on TCP Performance on a Low Bandwidth Link Pasi Lassila and Pirkko Kuusela Networking Laboratory Helsinki University of Technology (HUT) Espoo, Finland Email: {Pasi.Lassila,Pirkko.Kuusela

More information

Che-Wei Chang Department of Computer Science and Information Engineering, Chang Gung University

Che-Wei Chang Department of Computer Science and Information Engineering, Chang Gung University Che-Wei Chang chewei@mail.cgu.edu.tw Department of Computer Science and Information Engineering, Chang Gung University } 2017/11/15 Midterm } 2017/11/22 Final Project Announcement 2 1. Introduction 2.

More information

Module 5: CPU Scheduling

Module 5: CPU Scheduling Module 5: CPU Scheduling Basic Concepts Scheduling Criteria Scheduling Algorithms Multiple-Processor Scheduling Real-Time Scheduling Algorithm Evaluation 5.1 Basic Concepts Maximum CPU utilization obtained

More information

Communication constraints and latency in Networked Control Systems

Communication constraints and latency in Networked Control Systems Communication constraints and latency in Networked Control Systems João P. Hespanha Center for Control Engineering and Computation University of California Santa Barbara In collaboration with Antonio Ortega

More information

Chapter 6: CPU Scheduling

Chapter 6: CPU Scheduling Chapter 6: CPU Scheduling Basic Concepts Scheduling Criteria Scheduling Algorithms Multiple-Processor Scheduling Real-Time Scheduling Algorithm Evaluation 6.1 Basic Concepts Maximum CPU utilization obtained

More information

Multiaccess Communication

Multiaccess Communication Information Networks p. 1 Multiaccess Communication Satellite systems, radio networks (WLAN), Ethernet segment The received signal is the sum of attenuated transmitted signals from a set of other nodes,

More information

WiFi MAC Models David Malone

WiFi MAC Models David Malone WiFi MAC Models David Malone November 26, MACSI Hamilton Institute, NUIM, Ireland Talk outline Introducing the 82.11 CSMA/CA MAC. Finite load 82.11 model and its predictions. Issues with standard 82.11,

More information

Methodology for Computer Science Research Lecture 4: Mathematical Modeling

Methodology for Computer Science Research Lecture 4: Mathematical Modeling Methodology for Computer Science Research Andrey Lukyanenko Department of Computer Science and Engineering Aalto University, School of Science and Technology andrey.lukyanenko@tkk.fi Definitions and Goals

More information

Fairness comparison of FAST TCP and TCP Vegas

Fairness comparison of FAST TCP and TCP Vegas Fairness comparison of FAST TCP and TCP Vegas Lachlan L. H. Andrew, Liansheng Tan, Tony Cui, and Moshe Zukerman ARC Special Research Centre for Ultra-Broadband Information Networks (CUBIN), an affiliated

More information

Intro to Queueing Theory

Intro to Queueing Theory 1 Intro to Queueing Theory Little s Law M/G/1 queue Conservation Law 1/31/017 M/G/1 queue (Simon S. Lam) 1 Little s Law No assumptions applicable to any system whose arrivals and departures are observable

More information

A Queueing System with Queue Length Dependent Service Times, with Applications to Cell Discarding in ATM Networks

A Queueing System with Queue Length Dependent Service Times, with Applications to Cell Discarding in ATM Networks A Queueing System with Queue Length Dependent Service Times, with Applications to Cell Discarding in ATM Networks by Doo Il Choi, Charles Knessl and Charles Tier University of Illinois at Chicago 85 South

More information

Introduction. Motivation Recent measurements of local-area and wide-area trac [,7,3,] have shown that network trac exhibits variability at a wide rang

Introduction. Motivation Recent measurements of local-area and wide-area trac [,7,3,] have shown that network trac exhibits variability at a wide rang Multiple Time Scale Congestion Control for Self-Similar Network Trac Tsunyi Tuan a; Kihong Park a;3 a Network Systems Lab, Department of Computer Sciences, Purdue University, West Lafayette, IN 7907, USA

More information

Chapter 3 Balance equations, birth-death processes, continuous Markov Chains

Chapter 3 Balance equations, birth-death processes, continuous Markov Chains Chapter 3 Balance equations, birth-death processes, continuous Markov Chains Ioannis Glaropoulos November 4, 2012 1 Exercise 3.2 Consider a birth-death process with 3 states, where the transition rate

More information

2/5/07 CSE 30341: Operating Systems Principles

2/5/07 CSE 30341: Operating Systems Principles page 1 Shortest-Job-First (SJR) Scheduling Associate with each process the length of its next CPU burst. Use these lengths to schedule the process with the shortest time Two schemes: nonpreemptive once

More information

Signalling Analysis for Adaptive TCD Routing in ISL Networks *

Signalling Analysis for Adaptive TCD Routing in ISL Networks * COST 272 Packet-Oriented Service delivery via Satellite Signalling Analysis for Adaptive TCD Routing in ISL Networks * Ales Svigelj, Mihael Mohorcic, Gorazd Kandus Jozef Stefan Institute, Ljubljana, Slovenia

More information

Electrical Engineering Written PhD Qualifier Exam Spring 2014

Electrical Engineering Written PhD Qualifier Exam Spring 2014 Electrical Engineering Written PhD Qualifier Exam Spring 2014 Friday, February 7 th 2014 Please do not write your name on this page or any other page you submit with your work. Instead use the student

More information

RECENT technological advances in Wavelength Division

RECENT technological advances in Wavelength Division 1 Achieving Multi-Class Service Differentiation in WDM Optical Burst Switching Networks: A Probabilistic Preemptive Burst Segmentation Scheme Chee Wei Tan, Gurusamy Mohan, Member, IEEE, and John Chi-Shing

More information

Stability Analysis of QCN: The Averaging Principle

Stability Analysis of QCN: The Averaging Principle Stability Analysis of QCN: The Averaging Principle Mohammad Alizadeh, Abdul Kabbani, Berk Atikoglu, and Balaji Prabhakar Department of Electrical Engineering, Stanford University {alizade, akabbani, atikoglu,

More information

Achieving Proportional Loss Differentiation Using Probabilistic Preemptive Burst Segmentation in Optical Burst Switching WDM Networks

Achieving Proportional Loss Differentiation Using Probabilistic Preemptive Burst Segmentation in Optical Burst Switching WDM Networks Achieving Proportional Loss Differentiation Using Probabilistic Preemptive Burst Segmentation in Optical Burst Switching WDM Networks Chee-Wei Tan 2, Mohan Gurusamy 1 and John Chi-Shing Lui 2 1 Electrical

More information

Discrete Random Variables

Discrete Random Variables CPSC 53 Systems Modeling and Simulation Discrete Random Variables Dr. Anirban Mahanti Department of Computer Science University of Calgary mahanti@cpsc.ucalgary.ca Random Variables A random variable is

More information

End-to-end Estimation of the Available Bandwidth Variation Range

End-to-end Estimation of the Available Bandwidth Variation Range 1 End-to-end Estimation of the Available Bandwidth Variation Range Manish Jain Georgia Tech jain@cc.gatech.edu Constantinos Dovrolis Georgia Tech dovrolis@cc.gatech.edu Abstract The available bandwidth

More information

Understanding TCP Vegas: A Duality Model

Understanding TCP Vegas: A Duality Model Understanding TCP Vegas: A Duality Model STEVEN H. LOW Caltech, Pasadena, California AND LARRY L. PETERSON AND LIMIN WANG Princeton University, Princeton, New Jersey Abstract. We view congestion control

More information