Page 1 Hans Peter SchwefelSIPCom9-3: RT Networking Lecture 1, Fall05
Networking and protocols for real-time signal transmissionsby Hans-Peter Schwefel
• Mm1 Introduction & simple performance models
• Mm2 Real-time Support in Wireless Technologies
• Mm3 Transport Layer Aspects and Header Compression
• Mm4 IP Quality of Service: Advanced Concepts
• Mm5 Session Signalling and Application Layer aspects
http://www.kom.auc.dk/~hps
Page 2 Hans Peter SchwefelSIPCom9-3: RT Networking Lecture 1, Fall05
Content1. Motivation & Background
• Real-Time applications• Parameters
2. Layering Models Revisited• Layers and their real-time relevant functionalities• Packet-switched (IP) vs. circuit switched transmission• ’compromise’: ATM
3. L2 QoS Provisioning: Ethernet• Traffic classes: ATM, UMTS, 802.1q• Queueing/scheduling
4. Introduction to Performance Modeling• Continuous-time Markov chains• Birth-Death Processes, M/M/1 type Queues• Circuit-Switched case: Erlang formula• Packet-based traffic models
5. Summary and Outlook
Page 3 Hans Peter SchwefelSIPCom9-3: RT Networking Lecture 1, Fall05
Real-time requirements: Parameters• User Plane QoS/Network Performance
– End-2-End Packet Delay (in particular interactive applications)
– Delay Jitter – Packet Loss– Throughput/Goodput
• Application Level QoS– e.g. Video/Voice Quality (depending on codecs)
• Signalling Plane– Call Setup Delays– Fraction of blocked Calls
• Reliability Aspects– Failure probablities of entities– Downtime distribution
• Behavior at Handover– Dropped Calls– Delayed / Lost packets
MM1-4
See other lectures (Wireless Networks II and III)
MM5
Page 4 Hans Peter SchwefelSIPCom9-3: RT Networking Lecture 1, Fall05
Extended layered communication model• Ultimate goal of RT service
provisioning: user satisfaction• Focus in this course:
network and application aspects, i.e. L2-5 and application layer
Relevant functionalities:• PHY Layer
– Bit/Symbol transmission Throughput
– Symbol error probabilities (channel conditions, interference)
– Propagation delays
L3: Network Layer: IP
L2: MAC/LLC
L4: Transport: TCP, UDP, RTP/UDP
Application
(L5) Session Control, e.g. SIP
Middleware
User Interface
User
L1: PHYS
User Environment
Netw
ork QoS
Application Q
oS
User perceived Q
oS
Page 5 Hans Peter SchwefelSIPCom9-3: RT Networking Lecture 1, Fall05
Relevant Functionalities (cntd.)• Link Layer (L2)
– Medium Access Delays– Collisions/unsuccessful transmissions– Fragmentation– Forward error correction (FEC) and error detection (CRC)– Link-layer Retransmission Mechanisms (ARQ)– L2 scheduling, switching, buffering
• Network Layer (L3)– Path selection (routing)– Processing delays (e.g. for routing table lookup)– L3 buffering, scheduling, buffer management (RED)– [L3 fragmentation]
Page 6 Hans Peter SchwefelSIPCom9-3: RT Networking Lecture 1, Fall05
Relevant Functionalities (cntd.)• Transport Layer (L4)
– Multiplexing/de-multiplexing (UDP/TCP)– Error detection/checksums– In-order delivery, sequence numbers (TCP)– Acknowledgements and Retransmissions (TCP)– Flow/Congestion Control (TCP)
• Application Layer/Codecs– FEC/CRC– Application Layer Retransmissions– Application Layer sequence numbers
All Layers– Increased volume due to headers
Page 7 Hans Peter SchwefelSIPCom9-3: RT Networking Lecture 1, Fall05
Internet Protocol (IP)Internet Protocol IP, IPv4:• Layer 3 Protocol (Network Layer): implemented in hosts and routers• Packet (IP datagram) transmission between two hosts
(variable packet size up to 65535 bytes, often restricted by Layer 2 protocols)• Routing using 32 bit adresses (v4)
– Normally based on destination address only!
• Real-time affecting properties– Packet duplications– Packet reordering– Packet loss– fragmentation
• Real-time relevant functionalities– Scheduling in routers– Buffer management– Route selection
Application
TCP/UDP
IPLink-Layer
L5-7
L4
L3
L2
Page 8 Hans Peter SchwefelSIPCom9-3: RT Networking Lecture 1, Fall05
IP Version 6 (IPv6)IPv6• Basic Header 40 Bytes• 128-bit Network Addresses• Flow label (QoS)• No fragmentation in the network• ‘Built-in’ Security• Neighbor Discovery• Extension Headers:
Routing, Fragmentation, Authentication, Encryption
IPv4• Basic Header 20 Bytes• 32-bit Network Addresses• Type of Service field• Router may fragment packets• IPsec as an enhancement• ARP (Address Resolution Protocol)• Options
Version Priority Flow LabelPayload Length Type of Next Hdr. Hop Limit
Source Address
Destination Address
Offset0812162024283236
4
40
0 1 2 3
Next Header
Page 9 Hans Peter SchwefelSIPCom9-3: RT Networking Lecture 1, Fall05
IPv6 (cntd.)• Large number of IP addresses • Stateless autoconfiguration (can replace DHCP)• Extension headers (selection)
– 43 Routing Header– 44 Fragment Header– 51 Authentication Header– 50 Encrypted Security Payload– 60 Destination Options Header– 0 Hop-by-hop header
Page 10 Hans Peter SchwefelSIPCom9-3: RT Networking Lecture 1, Fall05
Packet-Based Transport• Advantages of Packet-Based Transport (as opposed to circuit switched)
– Flexibility– Optimal Use of Link Capacities, Multiplex-Gain for bursty traffic
• Drawbacks– Buffering/Queueing at routers can be necessary– Delay / Jitter / Packet Loss can occur – Overhead from Headers (20 Byte IPv4, 20 Byte TCP)
... and it makes performance modeling harder!!
Main motivation for Performance Modeling:• Network Planning• Evaluation/optimization of protocols/architectures/etc.
queueing
ν+
Page 11 Hans Peter SchwefelSIPCom9-3: RT Networking Lecture 1, Fall05
’Compromise’: Asynchronous Transfer Mode (ATM)• Constant size cells (48 Byte payload)• Short header (5 Byte)• Connection oriented (unidirectional): Switched Virtual Circuit (SVC), PVCs (Permanent)
allows– Call Admission Control (CAC)– Traffic Policing/Shaping
• Switching using VPI/VCI• Routing at connection setup:
– source routing – Hierarchical approach (peer groups)– PNNI
• Differentiation of Traffic Classes• Cell Format:
Page 12 Hans Peter SchwefelSIPCom9-3: RT Networking Lecture 1, Fall05
ATM Switching
Source: Advanced Networks lecture
Source: CNTK
Page 13 Hans Peter SchwefelSIPCom9-3: RT Networking Lecture 1, Fall05
Content1. Motivation & Background
• Real-Time applications• Parameters
2. Layering Models Revisited• Layers and their real-time relevant functionalities• Packet-switched (IP) vs. circuit switched transmission• ’compromise’: ATM
3. L2 QoS Provisioning: Ethernet• Traffic classes: ATM, UMTS, 802.1q• Queueing/scheduling
4. Introduction to Performance Modeling• Continuous-time Markov chains• Birth-Death Processes, M/M/1 type Queues• Circuit-Switched case: Erlang formula• Packet-based traffic models
5. Summary and Outlook
Page 14 Hans Peter SchwefelSIPCom9-3: RT Networking Lecture 1, Fall05
ATM Traffic Classes Source: Advanced Networks lecture
Page 15 Hans Peter SchwefelSIPCom9-3: RT Networking Lecture 1, Fall05
Cellular Networks: UMTS Traffic Classes
Source: 3GPP TS23.107, V5.2.0
Four basic classes:
Page 16 Hans Peter SchwefelSIPCom9-3: RT Networking Lecture 1, Fall05
UMTS: Range of Traffic/QoS Parameters
(Source TS23.107, V5.2.0)
Page 17 Hans Peter SchwefelSIPCom9-3: RT Networking Lecture 1, Fall05
Link Layer: IEEE 802 standards
Selected IEEE 802 standards• 802.1 High-level interface• 802.2 Logical Link Control (LLC)• 802.3 CSMA/CD (Ethernet!)• 802.5 Token Ring• 802.11 Wireless Lan (PHY and MAC), WiFi• 802.16 Fixed Wireless Access, WiMax• 802.20 Mobile Wireless Access
Page 18 Hans Peter SchwefelSIPCom9-3: RT Networking Lecture 1, Fall05
Traffic Classesin 802
Defined in 802.1D (MAC layer bridge)
• 8 classes (3 bit)• Up to 8 queues for
each outbound port
Source: CNTK
Page 19 Hans Peter SchwefelSIPCom9-3: RT Networking Lecture 1, Fall05
Ethernet frames: Q-tagging802.1q: • extension of
MAC frame• 2 Byte Tag (TCI)• Contains 3 bit
user priority• And 12 bit VLAN
identifier
Source: CNTK
Page 20 Hans Peter SchwefelSIPCom9-3: RT Networking Lecture 1, Fall05
Provisioning of Ethernet QoS: Scheduling• Strict priority scheduling: high priority queues drained first• Number of queues allowed to be < 8• Mapping of queues described in 802.1d:
• Mapping of user priorities for internal use within bridges can be defined
Prio 0Q5
Q2
M1
M2
Forwardingengine
Prio 5
Prio 2
Prio 0
Prio 0
A
B
C
Source: CNTK
Page 21 Hans Peter SchwefelSIPCom9-3: RT Networking Lecture 1, Fall05
Content1. Motivation & Background
• Real-Time applications• Parameters
2. Layering Models Revisited• Layers and their real-time relevant functionalities• Packet-switched (IP) vs. circuit switched transmission• ’compromise’: ATM
3. L2 QoS Provisioning: Ethernet• Traffic classes: ATM, UMTS, 802.1q• Queueing/scheduling
4. Introduction to Performance Modeling• Continuous-time Markov chains• Birth-Death Processes, M/M/1 type Queues• Circuit-Switched case: Erlang formula• Packet-based traffic models
5. Summary and Outlook
Page 22 Hans Peter SchwefelSIPCom9-3: RT Networking Lecture 1, Fall05
Challenges in IP networks:• Multiplexing of packets at nodes (L3)• Burstiness of IP traffic (L3-7)• Impact of Dynamic Routing (L3)• Performance impact of transport layer, in particular TCP (L4)• Wide range of applications different traffic & QoS requirements (L5-7)• Feedback: performance traffic model, e.g. for TCP traffic, adaptive applications
Challenges in Wireless Networks:• Wireless link models (channel models)• MAC & LLC modeling• RRM procedures• Mobility models• Cross layer optimization
Analysis frequently with ‚stochastic‘ models
Challenges in Packet Switched SettingHTTP
TCP
IPLink-Layer
L5-7
L4
L3
L2
Page 23 Hans Peter SchwefelSIPCom9-3: RT Networking Lecture 1, Fall05
Basic concepts• Probabilities
– ’Random experiment’ with set of possible results Ω– Axiomatic definition on event set V(Ω)
• 0≤Pr(A)≤1; Pr(∅)=0; Pr(A∪B)=Pr(A)+Pr(B) if A∩B=∅ [ A,B∈℘(Ω) ]– Conditional probabilities: Pr(A|B)=Pr(A∩B) / Pr(B)
• Random Variables (RV)– Definition: X: Ω→ú; Pr(X=x)=Pr(X-1(x))– Probability density function f(x), cumulative distribution function F(x)=Pr(X≤x),
reliability function (complementary distr. Function) R(x)=1-F(x)=Pr(X>x)
– Expected value, moments: E(Xn)=∫ xn f(x) dx– Relevant Examples, e.g.:
• number of packets that arrive at the access router in the next hour (discrete)• Buffer occupancy (#packets) in switch x at time y (discrete)• Number of downloads (’mouse clicks’) in the next web session (discrete)• Time until arrival of the next IP packet at a base station (continuous)
Page 24 Hans Peter SchwefelSIPCom9-3: RT Networking Lecture 1, Fall05
Basic concepts: Exponential Distributions
Important Case: Exponentially distributed RV• Single parameter: rate λ• Density function f(x)= λ exp(- λx), x>0• Cdf: F(x)=1-exp(- λx), Reliability function: R(x)=exp(- λx)• Moments: EX=1/ λ; VarX=1/ λ2, C2 = VarX / [EX]2 = 1
Important properties:• Memory-less: Pr(X>x+y | X>x) = exp(- λy) • Properties of two independent exponential RV: X with rate λ, Y with rate µ
– Distribution of min(X,Y): exponential with rate (λ+µ)– Pr(X<Y)= λ/(λ+µ)
Page 25 Hans Peter SchwefelSIPCom9-3: RT Networking Lecture 1, Fall05
Basic concepts III: Stochastic Processes• Definition of process (Xi) (discrete) or (Nt) (continuous)
– Simplest type: Xi independent and identically distributed (iid)• Relevant Examples:
– Inter-arrival time process: Xi– Counting Process:
N(t) = maxn | Σi=1 Xi ≤t , alternatively Ni(∆) = N(i∆) – N([i-1]∆)
Important Example: Poisson Process• Assume i.i.d. exponential packet inter-arrival times (rate λ): Xi:=Ti-Ti-1• Counting Process: Number of packets Nt until time t
– Pr(Nt=n)= (λt)n exp(- λt) / n!• Properties:
– Merging: arrivals from two independent Poisson processes with rate λ1 and λ2 Poisson process with rate (λ1+ λ2)
– Thinning: arrivals from a Poisson process of rate λ are discarded independently with probability p Poisson process with rate (1-p) λ
– Central Limit Theorem: superposition of n independent processes results in the limit n→∞in a Poisson process (under some conditions on the processes)
n-1
Page 26 Hans Peter SchwefelSIPCom9-3: RT Networking Lecture 1, Fall05
• Defined by– State-Space: finite or countable infinite, w/o.l.g. E=0,1,2,...,K (K=∞ also allowed)– Transition rates: µjk
• Holding time in state j: exponential with rate Σk≠j µjk =: µj
• Transition probability from state j to k: µjk / Σl≠j µjl = µjk / µj
• Xt = RV indicating the current state at time t; πi(t):=Pr(Xt=i)• ’Markov Property’: transitions do not depend on history but only on current state
t0<t1<...<tn , ∀i0,i1,...in ,j ∈E
• Computation of steady-state probabilities– Chapman Kolmogorov Equations: dπi(t)/dt = - µi πi(t) + Σj≠i µji πj(t) – Flow-balance equations, steady-state (t ∞): µi πi = Σj≠i µji πj
• Here: restriction to irreducible, homogeneous processes
Continuous Time Markov Processes
Page 27 Hans Peter SchwefelSIPCom9-3: RT Networking Lecture 1, Fall05
Queueing Models: Kendall NotationX/Y/C[/B] Queues (example: M/M/1, GI/M/2/10, M/M/10/10, ...)• X: Specifies Arrival Process
– M=Markovian Poisson– GI=General Independent iid
• Y: Specifies Service Process (M,G(I),...)• C: Number of Servers• B: size of finite waiting room (buffer)
[also counting the packet in service]– If not specified: B=∞
• Often also specified: service discipline– FIFO: First-In-First-Out (default)– Processor Sharing: PS– Last-in-first-out LIFO (preemptive or non-preemptive)– Earliest Deadline First (EDF), etc.
Finite buffer (size B)
µλ
Scope here: Point-process models as opposed to fluid-flow queues
Page 28 Hans Peter SchwefelSIPCom9-3: RT Networking Lecture 1, Fall05
M/M/1 queue• Poisson arrival of packets (first ’M’ Markovian) with rate λ• Exponentially distributed service times of rate µ (second ’M’)• Single Server (1)• FIFO service discipline
Qt = Number of packets in system is continuous-time Markov Process
’Derived’ Parameter:• Utilization, ρ= λ/ µ : if ρ≥1, instable case (no steady-state q.l.d)
Performance Parameters• Queue-length distribution: π(t) , steady-state limit: π=lim π(t) (if ρ<1)• Queue-length that an arriving customer sees• Waiting/System time distribution• Buffer-Overflow Probability for level B = Pr(arriving customers sees buffer occupancy B or
higher)
Infinite buffer
µλ
Page 29 Hans Peter SchwefelSIPCom9-3: RT Networking Lecture 1, Fall05
M/M/1 queue: Performance
• Birth-Death Process– Probability of i packets in queue [using flow-balance equations]
πi := Pr(Q=i) = (1-ρ)* ρi , where ρ= λ/ µ <1– Probability of idle server: π0 = (1-ρ)– Average Queue-length: EQ= ρ/(1-ρ)– Average Delay (System Time): ES= EQ/ λ = 1/(µ-λ)– Buffer Overflow Probabilities (PASTA principle)
Pr(Q(a)≥B)= Pr(Q≥B) = ρB
λ λ λ
µµ µ
Page 30 Hans Peter SchwefelSIPCom9-3: RT Networking Lecture 1, Fall05
General Birth-Death Processes
• Steady-State Probabilities (from balance equations):πi := Pr(Q=i) = π0 ∏k=0
i-1 λk / ∏ k=1i µk
• Models in this class, e.g.– M/M/1/B– M/M/C, M/M/C/C– Load-dependent services, discouraged arrivals
λ0 λ1 λ2
µ2µ1 µ3
Page 31 Hans Peter SchwefelSIPCom9-3: RT Networking Lecture 1, Fall05
Packet-based link model: M/M/1/K queue• Assumptions
– Poisson arrival of packets with rate λ– Exponentially distributed service times of rate µ– Single Server– Finite waiting room (buffer) for K packets
• Suitable e.g. for modeling ’bottleneck’ link in packet-based wireless networks
– [Full network models: see traffic analysis lecture]
Finite buffer (size K)
µλ
Page 32 Hans Peter SchwefelSIPCom9-3: RT Networking Lecture 1, Fall05
M/M/1/K queue: Performance
• From Birth-Death Process Theory:– Probability of i packets in queue
πi := Pr(Q=i) = (1-ρ)/(1- ρK+1) * ρi , where ρ= λ/ µ ≠1, i=0,…,K
– Probability of packet loss:p(loss) = πK = (1-ρ)/(1- ρK+1) * ρK
– Average Delay:
Ď = 1/[λ (1-pK)] * ρ/(1- ρK+1) * [(1- ρK)/ (1-ρ) – K ρK ]
λ λ λ λ
µµ µ µ
K
Page 33 Hans Peter SchwefelSIPCom9-3: RT Networking Lecture 1, Fall05
Extension: Models for packet traffic
• Poisson assumption for packet arrivals may be applicable for highly aggregated traffic (core networks), but otherwise traffic tends to be bursty– High data rates in ftp download but less activity between downloads– http: activities after mouse-clicks– Video streaming: high data rates in frame transmissions– Interactive Voice: talk and silent periods
• Model Modifications:– Bulk Arrival processes– ON/OFF models– Hierarchical models
Page 34 Hans Peter SchwefelSIPCom9-3: RT Networking Lecture 1, Fall05
The circuit switched scenario
• K channels• Users allocate one channel per call for certain call duration• If all channels are allocated additional starting calls are blocked• How many channels are necessary to achieve a call certain maximal
blocking probability?
Common Model Assumptions:• Calls are arriving according to a Poission Process (justified for large user
population, limit theorems for stochastic processes) with rate λ• Call durations are exponentially distributed with mean T (okay for voice
calls)
Page 35 Hans Peter SchwefelSIPCom9-3: RT Networking Lecture 1, Fall05
Computation of blocking probabilities:M/M/K/K model, Erlang-B formula
• Finite Birth-Death Process:– Probability of i calls active
πi := Pr(n=i) = π0 (λT)i /i! , i=1,…,K
where π0 = 1/[Σ(λT)i /i!] (sum taken over i=0 to K)– Probability of blocked call:
p(Blocking) = πK = π0 (λT)K /K![also known as Erlang-B formula]
λ λ λ λ
2/T1/T 3/T K/T
K
Page 36 Hans Peter SchwefelSIPCom9-3: RT Networking Lecture 1, Fall05
Content1. Motivation & Background
• Real-Time applications• Parameters
2. Layering Models Revisited• Layers and their real-time relevant functionalities• Packet-switched (IP) vs. circuit switched transmission• ’compromise’: ATM
3. L2 QoS Provisioning: Ethernet• Traffic classes: ATM, UMTS, 802.1q• Queueing/scheduling
4. Introduction to Performance Modeling• Continuous-time Markov chains• Birth-Death Processes, M/M/1 type Queues• Circuit-Switched case: Erlang formula• Packet-based traffic models
5. Summary and Outlook
Page 37 Hans Peter SchwefelSIPCom9-3: RT Networking Lecture 1, Fall05
Outlook: Bulk Arrival Models
• Queue-length at arrival instances increases not only by 1, but by a Random Variable B, the bulk-size
• Parameter set of model– Bulk arrival process, e.g. exponential with rate λ– Bulk-Size distribution: pi (e.g. geometric)– Service rate (single packet)
• Steady-state solution for mean system time [Chaudhry & Templeton 83]:ES = [ EB+EB2 ] / [2 EB µ (1-ρ) ]
• Example: M(B)/M/1 queue with geometrically distributed B
Page 38 Hans Peter SchwefelSIPCom9-3: RT Networking Lecture 1, Fall05
Outlook: ON/OFF Models
Parameters:• N sources, each average rate κ
• During ON periods: peak-rate λp
• Mean duration of ON and OFF times
κ = λp ON/(ON+OFF)
’bursty’ traffic, when λp >> κ
Page 39 Hans Peter SchwefelSIPCom9-3: RT Networking Lecture 1, Fall05
Outlook: General hierarchical models• Frequently used: Several levels with increasing granularity
– E.g. 3 levels: sessions, connections, packets– Or: 5-level model:
Page 40 Hans Peter SchwefelSIPCom9-3: RT Networking Lecture 1, Fall05
Example: HTTP traffic model• ‘Main’ objects contain zero or more embedded objects that the browser retrieves
Correlated requests for embedded objects within retrieval of main object
HTTP Session (User A)
HTTP Session (User B)
HTTP Session (User C)...
Session Level
Download Phase 1 Download Phase 2 Dld. Phase 3 ...Idle timeRead time
’exit browser’’click’ ’click’
Dld. Phase K
’click’’start browser’
Get Main Object Get embedded Obj. 1 Get emb. Obj. 2 Get emb. Obj. N...
Connection/ Flow Level
Packet Level, TCP dynamics (not shown here)
• Statistics:– Session arrivals: Renewal process (Poisson)– Idle time: heavy-tail
– # embedded objects: geometric (measurements e.g. mean 5)
– Object size: heavy-tailed
Page 41 Hans Peter SchwefelSIPCom9-3: RT Networking Lecture 1, Fall05
Outlook: Feedback Models, e.g. for TCP
End-to-End flow/congestion control Feedback: network behavior (congestion) ↔ ingress trafficNo separation traffic model/network model possibleNew traffic/performance models required
Approaches:1. Connection level models2. Sender/receiver behavior & fixpoint iteration3. Integrated models
Traffic Model
Network Model Performance
Values(Delay, Loss, etc.)
feedback
Page 42 Hans Peter SchwefelSIPCom9-3: RT Networking Lecture 1, Fall05
Exercises:1. Analytic Models: Traffic measurements in a GPRS radio cell result in the following traffic model: voice calls
arrive at Poisson rate 1call/min and have an average duration of 1.5 min. GPRS data sessions start at rate 1session/5min, have an average duration of 20min, and generate traffic with an averate rate of 10kb/sec using IP packets of 1500 byte size and CS-II.
a) How many time-slots would have to be reserved for GSM voice calls to keep the call blocking probability below 1e-6?
b) Compute the average RLC block delay, if 4 GPRS time-slots are used for the data traffic (as simplification: use an M/M/1 queue on RLC layer, RLC block size for CS-II is 247 bits, TDMA frame duration is 4.615ms; neglect header overhead as well as the overhead of TBF assignments).
2. Priority Queues: Assume a single Ethernet output port (rate mu) that supports two QoS classes. The two types of packets, High priority (HP) and low-priority (LP), arrive according to Poisson processes with rates lambda_l and lambda_h. Strict priority scheduling is used. Also, assume that an arrival of a high-priority packet pre-empts a currently ongoing service of a low-priority packet.– Draw the Markov chain for such priority model for buffer-sizes 2 packets for HP, 3 packets for LP.– (optional) Implement a MATALB function which allows to set-up the Q matrix of the Markov chain for
arbitrary lambda_I and mu, and for arbitrary buffer-size for the low-priority packets (the high priority buffer is fixed to size 2).
– (optional) Compute the steady-state probability vector and the average queue-lengths (the latter for varying lambda_h).
Page 43 Hans Peter SchwefelSIPCom9-3: RT Networking Lecture 1, Fall05
References• ’Quality of Service – State of the art survey’, Deliverable, Center for Network and
Service Convergence (CNTK), in progress.
Analytic Models• Cassandras, Lafortune, ’Introduction to Discrete Event Systems’,
Chapts. 7 and 8, Kluwer, 1999.Extended Material:• H. Gogl, ’Measurement and Characterization of Traffic Streams in High-Speed Wide Area
Networks’, VDI Verlag, 2001.• H.-P. Schwefel: ’Performance Analysis of Intermediate Systems Serving Aggregated ON/OFF
Traffic with Long-Range Dependent Properties’, Dissertation, TU Munich, 2000.
Related Lectures• ’Traffic analysis I and II’ (H Schiøler, HP Schwefel, 8/9th Sem DIRS/NPM)• Stochastic Models (B. Lindberg, HP Schwefel, B Fleury, 9th Sem SipCom)