Window Flow Control Systems with Random Service
|
|
- Kellie George
- 5 years ago
- Views:
Transcription
1 Window Flow Control Systems with Random Service Alireza Shekaramiz Joint work with Prof. Jörg Liebeherr and Prof. Almut Burchard April 6, / 20
2 Content 1 Introduction 2 Related work 3 State-of-the-art 4 Results: Stochastic service analysis of feedback systems 5 Results: Variable bit rate server with feedback system 6 Results: Markov-modulated On-Off server with feedback system 7 Numerical results 2 / 20
3 Feedback system Feedback system: apple window arrivals throttle Network departures delay For the analysis we use network calculus methodology Network calculus has analyzed feedback systems under deterministic assumptions Open problem in network calculus Analysis of feedback systems with probabilistic assumptions 3 / 20
4 Related work Performance bonds for flow control protocols 1 - Deterministic analysis - Min-plus algebra - Window flow control model A min,+ system theory for constrained traffic regulation and dynamic service guarantees 2 - Deterministic analysis - Min-plus algebra - Window flow control model TCP is max-plus linear 3 - Deterministic service process - Max-plus algebra - TCP Tahoe and TCP Reno 1 R. Agrawal et al. Performance bonds for flow control protocols. In: IEEE/ACM Transactions on Networking 7.3 (1999), pp C.-S Chang et al. A min,+ system theory for constrained traffic regulation and dynamic service guarantees. In: IEEE/ACM Transactions on Networking 10.6 (2002), pp F. Baccelli and D. Hong. TCP is max-plus linear and what it tells us on its throughput. In: ACM SIGCOMM 30.4 (2000), pp / 20
5 Related work TCP congestion avoidance 4 - Deterministic analysis - Min-plus algebra - Window flow control model - TCP Vegas and Fast TCP Window flow control in stochastic network calculus 5 - Stochastic analysis - Min-plus algebra - Window flow control model 4 M. Chen et al. TCP congestion avoidance: A network calculus interpretation and performance improvements. In: IEEE INFOCOM. vol , pp M. Beck and J. Schmitt. Window flow control in stochastic network calculus - The general service case. In: ACM VALUETOOLS. Jan / 20
6 Bivariate network calculus (f g) (s, t) = min{f (s, t), g(s, t)} (f g) (s, t) = min {f (s, τ) + g(τ, t)} s τ t (f g)(s, t) (g f )(s, t) (, ) operations form a non-commutative dioid over non-negative non-decreasing bivariate functions discrete-time domain (t = 0, 1, 2,...) Sub-additive closure: f δ f f (2) f (3)... = f (n) n=0 where f (n+1) = f (n) f for n 1, f (0) = δ, and f (1) = f 6 / 20
7 Moment-generating function network calculus 6 Moment-generating function of a random variable X: M X (θ) = E [e ] θx Moment-generating function of operations and : t M f g ( θ, s, t) M f ( θ, s, τ)m g ( θ, τ, t) τ=s s M f g (θ, s, t) M f (θ, τ, t)m g ( θ, τ, s) τ=0 ( ) For Pr S(s, t) S ε (s, t) ε, statistical service bound S ε (s, t) = max θ>0 1{ } log ε log M S ( θ, s, t) θ 6 M. Fidler. An end-to-end probabilistic network calculus with moment generating functions. In: IEEE IWQoS. 2006, pp / 20
8 State-of-the-art: Window flow control apple window arrivals throttle departures Network S win A A 0 min S D 0 D +w A = min { A, D } { w s t, δ +w (s, t) = s < t A D = min {A, D + w} D D + w D = w D = D δ +w = D + w 8 / 20
9 State-of-the-art: Window flow control Delay element represent feedback delay: δ d (s, t) = δ(s, t d) Equivalent feedback service: ( S win = S δ d δ +w) S A A 0 min S D D 0 A S win D +w d 9 / 20
10 Results: Exact result S win A A 0 min S D 0 +w d D Feedback system with w > 0, d 0 and with an additive service process t 1 c k k=s S(s, t) = c k s are arbitrary sequence of non-negative random variables If feedback delay is one (d = 1), t 1 S win (s, t) = min {c k, w} k=s 10 / 20
11 Results: Upper and lower bounds For the equivalent service process S win of a general feedback system with window size w > 0, and feedback delay d 0, we have Upper and lower bounds: S win(s, t) < S win (s, t) < min { S(s, t), } t s d w S win (s, t) is the equivalent service process of the feedback system with window size w = w/d and feedback delay d = 1 The lower bound corresponds to the exact result 11 / 20
12 Results: Equivalent service Feedback system with window size w > 0 and delay d 0: S win A A 0 min S D D 0 +w d S win (s, t) = { t s d n=0 min C n(s,t) where C n (s, t) is given as ( n ) } ( ) S(τ i 1, τ i d) + S(τ n, t) + nw i=1 C n (s, t) = { s = τ o τ n t i = 0,..., n τ i τ i 1 d } 12 / 20
13 Results: Feedback system with VBR Variable Bit Rate (VBR) server t 1 c k k=s S(s, t) = where c k s are independent and identically distributed random variables For a feedback system with VBR server with window size w > 0 and delay d 0: M Swin ( θ, s, t) (M c ( θ) d + de θw) t s d M c (θ) is the moment-generating function of c k, [ ] M c (θ) = E e θc k 13 / 20
14 Results: Feedback system with MMOO Markov-modulated On-Off (MMOO) server operates in two states: ON (state 1): The server transmits a constant amount of P > 0 units of traffic per time slot, c k = P OFF (state 0): The server does not transmit, c k = 0 The MMOO server offers an additive service process t 1 c k k=s S(s, t) = For a feedback system with MMOO server with window size w > 0 and delay d 0, if p 01 + p 10 < 1: M Swin ( θ, s, t) (m + ( θ) d + de θw) t s d m + (θ) is the larger eigenvalue of the matrix ( ) ( p00 p L(θ) = p 10 p 11 0 e θp ) 14 / 20
15 Numerical results: Statistical service bounds S ε win(s, t) = max θ>0 VBR server with exponential c k 1{ } log ε log M Swin ( θ, s, t) θ MMOO server with p 00 = 0.2, p 11 = 0.9, P = Mb Service (Mb) Swin ε Supper ε Slower ε and Sε win for d = 1 ms d = 100 ms w = 50 Mb d = 400 ms w = 200 Mb d = 5 ms w = 2.5 Mb d = 20 ms w = 10 Mb d = 10 ms w = 5 Mb d = 1 ms w = 500 Kb Time (ms) Service (Mb) d = 5 ms w = 2.5 Mb S ε win Upper bound Lower bound and S ε win for d = 1 ms d = 400 ms w = 200 Mb d = 10 ms w = 5 Mb d = 20 ms w = 10 Mb d = 100 ms w = 50 Mb d = 1 ms w = 500 Kb Time (ms) Average rate = 1 Gbps, time unit = 1 ms, w/d = 500 Mbps, ε = / 20
16 Numerical results: Effective capacity γ Swin ( θ) = lim t 1 θt log M S win ( θ, 0, t) VBR server with exponential c k MMOO server with p 00 = 0.2, p 11 = 0.9, P = Mb Effective capacity (Mbps) d = 100 ms w = 50 Mb d = 20 ms w = 10 Mb Lower bounds for γswin( θ) Lower bound Upper bound d = 10 ms w = 5 Mb d = 5 ms w = 2.5 Mb d = 1 ms w = 500 Kb θ ( 10 5 ) Effective capacity (Mbps) d = 10 ms w = 5 Mb d = 20 ms w = 10 Mb d = 100 ms w = 50 Mb d = 5 ms w = 2.5 Mb Lower bounds of γswin ( θ) Lower bound Upper bound d = 1 ms w = 500 Kb θ ( 10 5 ) Average rate = 1 Gbps, w/d = 500 Mbps 16 / 20
17 Numerical results: Backlog and delay bounds t 1 a k k=s A(s, t) = with exponential a k and average rate λ Backlog bound Delay bound ε = 10 9 ε = 10 6 ε = 10 3 Sim. ε = ε = 10 9 ε = 10 6 ε = 10 3 Sim. ε = 10 6 Backlog bound (Mb) Delay bound (ms) w/d = 500 Mbps 10 5 w/d = 100 Mbps w/d = 500 Mbps 50 w/d = 100 Mbps Arrival rate λ (Mbps) Arrival rate λ (Mbps) Exponential VBR, time unit = 1 ms, feedback delay d = 1 ms 17 / 20
18 Conclusions S win A A 0 min S D D 0 +w d Results: Exact results Upper and lower service bounds Equivalent service of the feedback system Bounds for a feedback system with VBR server Bounds for a feedback system with MMOO server Backlog and delay bounds 18 / 20
19 Technical Report - July 2016 A. Shekaramiz, J. Liebeherr, and A. Burchard. Window Flow Control Systems with Random Service. arxiv: , July / 20
20 Thank you Q & A 20 / 20
Window Flow Control Systems with Random Service
Window Flow Control Systems with Random Service Alireza Shekaramiz*, Jörg Liebeherr*, Almut Burchard** arxiv:1507.04631v1 [cs.pf] 16 Jul 2015 * Department of ECE, University of Toronto, Canada. ** Department
More informationNetwork Calculus Analysis of Feedback Systems with Random Service. Alireza Shekaramiz
Network Calculus Analysis of Feedback Systems with Random Service by Alireza Shekaramiz A thesis submitted in conformity with the requirements for the degree of Master of Applied Science Graduate Department
More informationStochastic Network Calculus
Stochastic Network Calculus Assessing the Performance of the Future Internet Markus Fidler joint work with Amr Rizk Institute of Communications Technology Leibniz Universität Hannover April 22, 2010 c
More informationWindow Flow Control in Stochastic Network Calculus The General Service Case
Window Flow Control in Stochastic Network Calculus The General Service Case Michael Beck University of Kaiserslautern Distributed Computer Systems Lab (DISCO Germany beck@cs.uni-kl.de ABSTRACT The problem
More informationRecent Progress on a Statistical Network Calculus
Recent Progress on a Statistical Network Calculus Jorg Liebeherr Deartment of Comuter Science University of Virginia Collaborators Almut Burchard Robert Boorstyn Chaiwat Oottamakorn Stehen Patek Chengzhi
More informationA Calculus for End-to-end Statistical Service Guarantees
A Calculus for End-to-end Statistical Service Guarantees Almut Burchard y Jörg Liebeherr Stephen Patek y Department of Mathematics Department of Computer Science Department of Systems and Information Engineering
More informationResource 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 informationAmr 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 informationA Network Calculus with Effective Bandwidth
A Network Calculus with Effective Bandwidth Technical Report: University of Virginia, CS-2003-20, November 2003 Chengzhi Li Almut Burchard Jörg Liebeherr Department of Computer Science Department of Mathematics
More informationA Network Calculus Approach for the Analysis of Multi-Hop Fading Channels
A Network Calculus Approach for the Analysis of Multi-Hop Fading Channels Hussein Al-Zubaidy*, Jörg Liebeherr*, Almut Burchard** * Department of ECE, University of Toronto, Canada. ** Department of Mathematics,
More informationPerformance 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 informationOn the Delay Performance of Interference Channels
On the Delay Performance of Interference Channels Sebastian Schiessl, Farshad Naghibi, Hussein Al-Zubaidy, Markus Fidler, James Gross School of Electrical Engineering, KTH Royal Institute of Technology,
More informationNetwork-Layer Performance Analysis of Multi-Hop Fading Channels
Network-Layer Performance Analysis of Multi-Hop Fading Channels Hussein Al-Zubaidy, Jörg Liebeherr, Almut Burchard Abstract A fundamental problem for the delay and backlog analysis across multi-hop paths
More informationWireless 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 informationOn the Impact of Link Scheduling on End-to-End Delays in Large Networks
1 On the Impact of Link Scheduling on End-to-End Delays in Large Networks Jörg Liebeherr, Yashar Ghiassi-Farrokhfal, Almut Burchard Abstract We seek to provide an analytical answer whether the impact of
More informationTowards a Statistical Network Calculus Dealing with Uncertainty in Arrivals
Towards a Statistical Network Calculus Dealing with Uncertainty in Arrivals Michael A. Beck, Sebastian A. Henningsen, Simon B. Birnbach, Jens B. Schmitt Distributed Computer Systems DISCO Lab, University
More informationDelay Bounds for Networks with Heavy-Tailed and Self-Similar Traffic
Delay Bounds for Networks with Heavy-Tailed and Self-Similar Traffic Jörg Liebeherr, Almut Burchard, Florin Ciucu Abstract 1 arxiv:0911.3856v1 [cs.ni] 19 Nov 2009 We provide upper bounds on the end-to-end
More informationFairness 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 informationStatistical Per-Flow Service Bounds in a Network with Aggregate Provisioning
Statistical Per-Flow Service Bounds in a Network with Aggregate Provisioning Technical Report: University of Virginia, S-2002-27 Jörg Liebeherr Stephen Patek Almut Burchard y y Department of Mathematics
More informationA Network Calculus with Effective Bandwidth
A Network Calculus with Effective Bandwidth Chengzhi Li, Almut Burchard, Jörg Liebeherr Abstract This paper establishes a link between two principal tools for the analysis of network traffic, namely, effective
More informationDesign of IP networks with Quality of Service
Course of Multimedia Internet (Sub-course Reti Internet Multimediali ), AA 2010-2011 Prof. Pag. 1 Design of IP networks with Quality of Service 1 Course of Multimedia Internet (Sub-course Reti Internet
More informationPostprint.
http://www.diva-portal.org Postprint This is the accepted version of a paper presented at IEEE Global Telecommunications Conference 2016 GLOBECOM 2016). Citation for the original published paper: Yang,
More informationMarkovian Model of Internetworking Flow Control
Информационные процессы, Том 2, 2, 2002, стр. 149 154. c 2002 Bogoiavlenskaia. KALASHNIKOV MEMORIAL SEMINAR Markovian Model of Internetworking Flow Control O. Bogoiavlenskaia Petrozavodsk State University
More informationA New Technique for Link Utilization Estimation
A New Technique for Link Utilization Estimation in Packet Data Networks using SNMP Variables S. Amarnath and Anurag Kumar* Dept. of Electrical Communication Engineering Indian Institute of Science, Bangalore
More informationDelay Bounds in Communication Networks with Heavy-Tailed and Self-Similar Traffic
Delay Bounds in Communication Networks with Heavy-Tailed and Self-Similar Traffic Jörg Liebeherr, Almut Burchard, Florin Ciucu 1 Abstract Traffic with self-similar and heavy-tailed characteristics has
More informationStochastic 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 Deterministic
More informationin accordance with a window ow control protocol. Alternatively, the total volume of trac generated may in fact depend on the network feedback, as in a
Proc. Allerton Conf. on Comm.,Control, and Comp., Monticello, IL. Sep t. 1998. 1 A Framework for Adaptive Service Guarantees Rajeev Agrawal ECE Department University of Wisconsin Madison, WI 53706-1691
More informationStochastic 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 informationCompound TCP with Random Losses
Compound TCP with Random Losses Alberto Blanc 1, Konstantin Avrachenkov 2, Denis Collange 1, and Giovanni Neglia 2 1 Orange Labs, 905 rue Albert Einstein, 06921 Sophia Antipolis, France {alberto.blanc,denis.collange}@orange-ftgroup.com
More informationM/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 informationMin 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 informationNetwork management and QoS provisioning - Network Calculus
Network Calculus Network calculus is a metodology to study in a deterministic approach theory of queues. First a linear modelization is needed: it means that, for example, a system like: ρ can be modelized
More informationStatistical Per-Flow Service Bounds in a Network with Aggregate Provisioning
1 Statistical Per-Flow Service Bounds in a Network with Aggregate Provisioning Jörg Liebeherr, Department of omputer Science, University of Virginia Stephen D. Patek, Department of Systems and Information
More informationA Network Service Curve Approach for the Stochastic Analysis of Networks
A Network Service Curve Approach for the Stochastic Analysis of Networks Florin Ciucu Dept of Computer Science University of Virginia Charlottesville, VA 94 florin@csvirginiaedu Almut Burchard Department
More informationCompound TCP with Random Losses
Compound TCP with Random Losses Alberto Blanc 1, Konstantin Avrachenkov 2, Denis Collange 1, and Giovanni Neglia 2 1 Orange Labs, 905 rue Albert Einstein, 06921 Sophia Antipolis, France {alberto.blanc,denis.collange}@orange-ftgroup.com
More informationStochastic 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 informationDynamic Power Allocation and Routing for Time Varying Wireless Networks
Dynamic Power Allocation and Routing for Time Varying Wireless Networks X 14 (t) X 12 (t) 1 3 4 k a P ak () t P a tot X 21 (t) 2 N X 2N (t) X N4 (t) µ ab () rate µ ab µ ab (p, S 3 ) µ ab µ ac () µ ab (p,
More informationTransient Analysis for Wireless Networks
Transient Analysis for Wireless Networks Jaya Prakash Champati, Hussein Al-Zubaidy, James Gross School of Electrical Engineering, KTH Royal Institute of Technology, Stockholm, Sweden E-mail:jpra@kth.se,james.gross@ee.kth.se
More informationA Stochastic Model for TCP with Stationary Random Losses
A Stochastic Model for TCP with Stationary Random Losses Eitan Altman, Kostya Avrachenkov Chadi Barakat INRIA Sophia Antipolis - France ACM SIGCOMM August 31, 2000 Stockholm, Sweden Introduction Outline
More informationNICTA 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 informationPerformance Effects of Two-way FAST TCP
Performance Effects of Two-way FAST TCP Fei Ge a, Sammy Chan b, Lachlan L. H. Andrew c, Fan Li b, Liansheng Tan a, Moshe Zukerman b a Dept. of Computer Science, Huazhong Normal University, Wuhan, P.R.China
More informationQueueing Theory I Summary! Little s Law! Queueing System Notation! Stationary Analysis of Elementary Queueing Systems " M/M/1 " M/M/m " M/M/1/K "
Queueing Theory I Summary Little s Law Queueing System Notation Stationary Analysis of Elementary Queueing Systems " M/M/1 " M/M/m " M/M/1/K " Little s Law a(t): the process that counts the number of arrivals
More informationIntroduction to queuing theory
Introduction to queuing theory Claude Rigault ENST claude.rigault@enst.fr Introduction to Queuing theory 1 Outline The problem The number of clients in a system The client process Delay processes Loss
More informationcommunication 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 informationScaling Properties in the Stochastic Network Calculus
Scaling Properties in the Stochastic Network Calculus A Dissertation Presented to the faculty of the School of Engineering and Applied Science University of Virginia In Partial Fulfillment of the requirements
More informationStochastic Service Guarantee Analysis Based on Time-Domain Models
Stochastic Service Guarantee Analysis Based on Time-Domain Models Jing Xie Department of Telematics Norwegian University of Science and Technology Email: jingxie@item.ntnu.no Yuming Jiang Department of
More informationStochastic Network Calculus for Rician Fading in Underwater Wireless Networks
Appl. Math. Inf. Sci. 10, No. 4, 1465-1474 (2016) 1465 Applied Mathematics & Information Sciences An International Journal http://dx.doi.org/10.18576/amis/100425 Stochastic Network Calculus for Rician
More informationAnalysis of TCP Westwood+ in high speed networks
Analysis of TCP Westwood+ in high speed networks 1 E. Altman, C. Barakat, S. Mascolo, N. Möller and J. Sun Abstract TCP Westwood+ is modelled and analyzed using stochastic recursive equations. It is shown
More informationPerformance Evaluation of Queuing Systems
Performance Evaluation of Queuing Systems Introduction to Queuing Systems System Performance Measures & Little s Law Equilibrium Solution of Birth-Death Processes Analysis of Single-Station Queuing Systems
More informationcs/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 informationCS276 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 informationHomework 1 - SOLUTION
Homework - SOLUTION Problem M/M/ Queue ) Use the fact above to express π k, k > 0, as a function of π 0. π k = ( ) k λ π 0 µ 2) Using λ < µ and the fact that all π k s sum to, compute π 0 (as a function
More informationA Data Center Interconnects Calculus
A Data Center Interconnects Calculus Hao Wang, Haoyun Shen, Philipp Wieder, Ramin Yahyapour GWDG / University of Göttingen, Germany Abstract For many application scenarios, interconnected data centers
More informationAnalysis 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 informationMixed Stochastic and Event Flows
Simulation Mixed and Event Flows Modeling for Simulation Dynamics Robert G. Cole 1, George Riley 2, Derya Cansever 3 and William Yurcick 4 1 Johns Hopkins University 2 Georgia Institue of Technology 3
More informationEfficient 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 informationReal-Time Calculus. LS 12, TU Dortmund
Real-Time Calculus Prof. Dr. Jian-Jia Chen LS 12, TU Dortmund 09, Dec., 2014 Prof. Dr. Jian-Jia Chen (LS 12, TU Dortmund) 1 / 35 Arbitrary Deadlines The worst-case response time of τ i by only considering
More informationSharp Per-Flow Delay Bounds for Bursty Arrivals: The Case of FIFO, SP, and EDF Scheduling
Sharp Per-Flow Delay Bounds for Bursty Arrivals: The Case of FIFO, SP, and EDF Scheduling Florin Ciucu University of Warwick Felix Poloczek University of Warwick / TU Berlin Jens Schmitt University of
More informationApproximate Queueing Model for Multi-rate Multi-user MIMO systems.
An Approximate Queueing Model for Multi-rate Multi-user MIMO systems Boris Bellalta,Vanesa Daza, Miquel Oliver Abstract A queueing model for Multi-rate Multi-user MIMO systems is presented. The model is
More informationWorst-case delay control in multigroup overlay networks. Tu, Wanqing; Sreenan, Cormac J.; Jia, Weijia. Article (peer-reviewed)
Title Author(s) Worst-case delay control in multigroup overlay networks Tu, Wanqing; Sreenan, Cormac J.; Jia, Weijia Publication date 2007-0 Original citation Type of publication Link to publisher's version
More informationEfficient Network-wide Available Bandwidth Estimation through Active Learning and Belief Propagation
Efficient Network-wide Available Bandwidth Estimation through Active Learning and Belief Propagation mark.coates@mcgill.ca McGill University Department of Electrical and Computer Engineering Montreal,
More informationLarge Deviations for Channels with Memory
Large Deviations for Channels with Memory Santhosh Kumar Jean-Francois Chamberland Henry Pfister Electrical and Computer Engineering Texas A&M University September 30, 2011 1/ 1 Digital Landscape Delay
More informationThe Performance Impact of Delay Announcements
The Performance Impact of Delay Announcements Taking Account of Customer Response IEOR 4615, Service Engineering, Professor Whitt Supplement to Lecture 21, April 21, 2015 Review: The Purpose of Delay Announcements
More informationA 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 informationA Simple Memoryless Proof of the Capacity of the Exponential Server Timing Channel
A Simple Memoryless Proof of the Capacity of the Exponential Server iming Channel odd P. Coleman ECE Department Coordinated Science Laboratory University of Illinois colemant@illinois.edu Abstract his
More informationLoad Balancing in Distributed Service System: A Survey
Load Balancing in Distributed Service System: A Survey Xingyu Zhou The Ohio State University zhou.2055@osu.edu November 21, 2016 Xingyu Zhou (OSU) Load Balancing November 21, 2016 1 / 29 Introduction and
More information1 Introduction Future high speed digital networks aim to serve integrated trac, such as voice, video, fax, and so forth. To control interaction among
On Deterministic Trac Regulation and Service Guarantees: A Systematic Approach by Filtering Cheng-Shang Chang Dept. of Electrical Engineering National Tsing Hua University Hsinchu 30043 Taiwan, R.O.C.
More informationImpact 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 informationECE 3511: Communications Networks Theory and Analysis. Fall Quarter Instructor: Prof. A. Bruce McDonald. Lecture Topic
ECE 3511: Communications Networks Theory and Analysis Fall Quarter 2002 Instructor: Prof. A. Bruce McDonald Lecture Topic Introductory Analysis of M/G/1 Queueing Systems Module Number One Steady-State
More informationWiFi 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 informationqueue KTH, Royal Institute of Technology, Department of Microelectronics and Information Technology
Analysis of the Packet oss Process in an MMPP+M/M/1/K queue György Dán, Viktória Fodor KTH, Royal Institute of Technology, Department of Microelectronics and Information Technology {gyuri,viktoria}@imit.kth.se
More informationWorst Case Burstiness Increase due to FIFO Multiplexing
Worst Case Burstiness Increase due to FIFO Multiplexing EPFL esearch eport IC/22/17 Vicent Cholvi DLSI Universitat Jaume I 1271 Castelló (Spain) vcholvi@lsi.uji.es Juan Echagüe DICC Universitat Jaume I
More informationjwperiod. font=small,labelsep=jwperiod,justification=justified,singlelinecheck=false
jwperiod. font=smalllabelsep=jwperiodjustification=justifiedsinglelinecheck=false 1 EUROPEAN TRANSACTIONS ON TELECOMMUNICATIONS Euro. Trans. Telecomms. : 2?? Published online in Wiley InterScience www.interscience.wiley.com
More informationOptimal Media Streaming in a Rate-Distortion Sense For Guaranteed Service Networks
Optimal Media Streaming in a Rate-Distortion Sense For Guaranteed Service Networks Olivier Verscheure and Pascal Frossard Jean-Yves Le Boudec IBM Watson Research Center Swiss Federal Institute of Tech.
More informationTuning the TCP Timeout Mechanism in Wireless Networks to Maximize Throughput via Stochastic Stopping Time Methods
Tuning the TCP Timeout Mechanism in Wireless Networks to Maximize Throughput via Stochastic Stopping Time Methods George Papageorgiou and John S. Baras Abstract We present an optimization problem that
More informationI 2 (t) R (t) R 1 (t) = R 0 (t) B 1 (t) R 2 (t) B b (t) = N f. C? I 1 (t) R b (t) N b. Acknowledgements
Proc. 34th Allerton Conf. on Comm., Cont., & Comp., Monticello, IL, Oct., 1996 1 Service Guarantees for Window Flow Control 1 R. L. Cruz C. M. Okino Department of Electrical & Computer Engineering University
More informationORIGINATING from the seminal works by Shannon in
1280 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 14, NO. 3, MARCH 2015 Capacity Delay Error Boundaries: A Composable Model of Sources and Systems Markus Fidler, Senior Member, IEEE, Ralf Lübben,
More informationRobustness of Real and Virtual Queue based Active Queue Management Schemes
Robustness of Real and Virtual Queue based Active Queue Management Schemes Ashvin Lakshmikantha, C. L. Beck and R. Srikant Department of General Engineering University of Illinois lkshmknt@uiuc.edu, rsrikant@uiuc.edu,
More informationQuiz 1 EE 549 Wednesday, Feb. 27, 2008
UNIVERSITY OF SOUTHERN CALIFORNIA, SPRING 2008 1 Quiz 1 EE 549 Wednesday, Feb. 27, 2008 INSTRUCTIONS This quiz lasts for 85 minutes. This quiz is closed book and closed notes. No Calculators or laptops
More informationState-dependent Limited Processor Sharing - Approximations and Optimal Control
State-dependent Limited Processor Sharing - Approximations and Optimal Control Varun Gupta University of Chicago Joint work with: Jiheng Zhang (HKUST) State-dependent Limited Processor Sharing Processor
More informationLecture 7: Simulation of Markov Processes. Pasi Lassila Department of Communications and Networking
Lecture 7: Simulation of Markov Processes Pasi Lassila Department of Communications and Networking Contents Markov processes theory recap Elementary queuing models for data networks Simulation of Markov
More informationAnalyzing Queuing Systems with Coupled Processors. through Semidefinite Programming
Analyzing Queuing Systems with Coupled Processors through Semidefinite Programming Balaji Rengarajan, Constantine Caramanis, and Gustavo de Veciana Dept. of Electrical and Computer Engineering The University
More informationA First Course in Stochastic Network Calculus
A First Course in Stochastic Network Calculus Dipl.-Math. Michael Alexander Beck November 14, 2013 Contents 1 Basics of Probability 4 2 Stochastic Arrivals and Service 5 2.1 Stochastic Arrivals...............................
More informationMultiplicative Multifractal Modeling of. Long-Range-Dependent (LRD) Trac in. Computer Communications Networks. Jianbo Gao and Izhak Rubin
Multiplicative Multifractal Modeling of Long-Range-Dependent (LRD) Trac in Computer Communications Networks Jianbo Gao and Izhak Rubin Electrical Engineering Department, University of California, Los Angeles
More informationStochastic Hybrid Systems: Modeling, analysis, and applications to networks and biology
research supported by NSF Stochastic Hybrid Systems: Modeling, analysis, and applications to networks and biology João P. Hespanha Center for Control Engineering and Computation University of California
More informationModelling TCP with a Discrete Time Markov Chain
Modelling TCP with a Discrete Time Markov Chain José L Gil Motorola josegil@motorola.com ABSTRACT TCP is the most widely used transport protocol in the Internet. The end-to-end performance of most Internet
More informationSome properties of variable length packet shapers
Some properties of variable length packet shapers Jean-Yves Le Boudec EPFL-DSC CH-1015 Lausanne, Switzerland EPFL-DSC esearch eport DSC/2000/037 20 Decembre 2000 Abstract The
More informationA Mechanism for Pricing Service Guarantees
A Mechanism for Pricing Service Guarantees Bruce Hajek Department of Electrical and Computer Engineering and the Coordinated Science Laboratory University of Illinois at Urbana-Champaign Sichao Yang Qualcomm
More informationarxiv: v1 [math.oc] 13 Mar 2019
Behavior and Management of Stochastic Multiple-Origin-Destination Traffic Flows Sharing a Common Link arxiv:1903.05510v1 [math.oc] 13 Mar 2019 Li Jin and Yining Wen March 14, 2019 Abstract In transportation
More informationMethodology 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 informationIntro 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 informationTiming Analysis of AVB Traffic in TSN Networks using Network Calculus
Timing Analysis of AVB Traffic in TSN Networks using Network Calculus Luxi Zao, Paul Pop Tecnical University of Denmark Zong Zeng, Qiao Li Beiang University Outline Introduction System Model Arcitecture
More informationScheduling Coflows in Datacenter Networks: Improved Bound for Total Weighted Completion Time
1 1 2 Scheduling Coflows in Datacenter Networs: Improved Bound for Total Weighted Completion Time Mehrnoosh Shafiee and Javad Ghaderi Abstract Coflow is a recently proposed networing abstraction to capture
More informationTCP 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 informationA Demand Response Calculus with Perfect Batteries
A Demand Response Calculus with Perfect Batteries Dan-Cristian Tomozei Joint work with Jean-Yves Le Boudec CCW, Sedona AZ, 07/11/2012 Demand Response by Quantity = distribution network operator may interrupt
More informationInformation in Aloha Networks
Achieving Proportional Fairness using Local Information in Aloha Networks Koushik Kar, Saswati Sarkar, Leandros Tassiulas Abstract We address the problem of attaining proportionally fair rates using Aloha
More informationEnergy Optimal Control for Time Varying Wireless Networks. Michael J. Neely University of Southern California
Energy Optimal Control for Time Varying Wireless Networks Michael J. Neely University of Southern California http://www-rcf.usc.edu/~mjneely Part 1: A single wireless downlink (L links) L 2 1 S={Totally
More informationOFDMA Cross Layer Resource Control
OFDA Cross Layer Resource Control Gwanmo Ku Adaptive Signal Processing and Information Theory Research Group Jan. 25, 2013 Outline 2/20 OFDA Cross Layer Resource Control Objective Functions - System Throughput
More informationQuality of Real-Time Streaming in Wireless Cellular Networks : Stochastic Modeling and Analysis
Quality of Real-Time Streaming in Wireless Cellular Networs : Stochastic Modeling and Analysis B. Blaszczyszyn, M. Jovanovic and M. K. Karray Based on paper [1] WiOpt/WiVid Mai 16th, 2014 Outline Introduction
More informationEE 550: Notes on Markov chains, Travel Times, and Opportunistic Routing
EE 550: Notes on Markov chains, Travel Times, and Opportunistic Routing Michael J. Neely University of Southern California http://www-bcf.usc.edu/ mjneely 1 Abstract This collection of notes provides a
More information