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Traffic management 2007PowerPoint Presentation

Traffic management 2007

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An example

- Executive participating in a worldwide videoconference
- Proceedings are videotaped and stored in an archive
- Edited and placed on a Web site
- Accessed later by others
- During conference
- Sends email to an assistant
- Breaks off to answer a voice call

What this requires

- For video
- sustained bandwidth of at least 64 kbps
- low loss rate

- For voice
- sustained bandwidth of at least 8 kbps
- low loss rate

- For interactive communication
- low delay (< 100 ms one-way)

- For playback
- low delay jitter

- For email and archiving
- reliable bulk transport

What if…

- A million executives were simultaneously accessing the network?
- What capacity should each trunk have?
- How should packets be routed? (Can we spread load over alternate paths?)
- How can different traffic types get different services from the network?
- How should each endpoint regulate its load?
- How should we price the network?

- These types of questions lie at the heart of network design and operation, and form the basis for traffic management.

Traffic management

- Set of policies and mechanisms that allow a network to efficiently satisfy a diverse range of service requests
- Tension is between diversity and efficiency
- Traffic management = Connectivity + Quality of Service
- Traffic management is necessary for providing Quality of Service (QoS)
- Subsumes congestion control (congestion == loss of efficiency)

- One of the most challenging open problems in networking

Outline

- Economic principles
- Traffic models
- Traffic classes
- Time scales - Mechanisms

Basics: utility function

- Users are assumed to have a utility function that maps from a given quality of service to a level of satisfaction, or utility
- Utility functions are private information
- Cannot compare utility functions between users ?

- Rational users take actions that maximize their utility
- Can determine utility function by observing preferences

Example

- Let u = S - a t
- u = utility from file transfer
- S = satisfaction when transfer infinitely fast (t=0)
- t = transfer time
- a = rate at which satisfaction decreases with time

- As transfer time increases, utility decreases
- If t > S/a, user is worse off! (reflects time wasted) -- timeout
- Assumes linear decrease in utility
- S and a can be experimentally determined

Social welfare

- Suppose network manager knew the utility function of every user
- Social Welfare is maximized when some combination of the utility functions (such as sum) is maximized
- An economy (network) is efficientwhen increasing the utility of one user must necessarily decrease the utility of another -- conservation law
- An economy (network) is envy-free if no user would trade places with another (better performance also costs more) ?
- Goal: maximize social welfare
- subject to efficiency, envy-freeness, and making a profit

Example

- Assume: two users - A, B
- Single switch, each user imposes load 0.4 (=ρ)
- Same delay to both users: delay = d
- A’s utility: 4 - d
- B’s utility : 8 - 2d (wish less delay than A)

- Conservation law(KL_Q_v2_117)
- 0.4d + 0.4d = C => d = 1.25 C
- social welfare (sum of utilities) = 12-3.75 C

- If B’s delay reduced to 0.5C, then A’s delay = 2C
- sum of utilities = 12 - 3C

- Increase in social welfare (sum of utilities) need not benefit everyone
- A loses utility, but may pay less for service

Some economic principles

- Lowering delay of delay-sensitive traffic increased welfare
- can increase welfare by matching service menu to user requirements
- BUT need to know what users want (signaling)

- A single network that provides heterogeneous QoS is better than separate networks for each QoS - Q theory
- unused capacity is available to others

- For typical utility functions, welfare increases more than linearly with increase in capacity ?
- individual users see smaller overall fluctuations
- can increase welfare by increasing capacity - Q theory, Large number law

Principles applied

- A single wire that carries both voice and data is more efficient than separate wires for voice and data
- IP Phone
- ADSL

- Moving from a 20% loaded10 Mbps Ethernet to a 20% loaded 100 Mbps Ethernet will still improve social welfare
- increase capacity whenever possible

- Better to give 5% of the traffic lower delay than all traffic low delay
- should somehow mark and isolate low-delay traffic

The two camps

- Can increase welfare either by
- matching services to user requirements or
- increasing capacity blindly

- small and smart vs. big and dumb
- Which is cheaper? no one is really sure!
- It seems that smarter ought to be better
- otherwise, to get low delays for some traffic, we need to give all traffic low delay, even if it doesn’t need it

- But, perhaps, we can use the money spent on traffic management to increase welfare
- We will study traffic management, assuming that it matters!

Outline

- Economic principles
- Traffic models (+ our research)
- Traffic classes
- Time scales - Mechanisms

Traffic models

- To effectively manage traffic, need to have some idea of how users or aggregates of users behave => traffic model
- e.g. average size of a file transfer
- e.g. how long a user uses a modem

- Models change with network usage
- We can only guess about the future
- Two types of models - hard to match both
- from measurements
- mathematical analysis - educated guesses

Telephone traffic models

- How are calls placed?
- call arrival model
- studies show that time between calls is drawn from an exponential distribution
- memoryless: the fact that a certain amount of time has passed since the last call gives no information of time to next call
- call arrival process is therefore Poisson

- How long are calls held?
- usually modeled as exponential
- however, measurement studies show it to be heavy tailed
- means that a significant number of calls last a very long time

Internet traffic modeling

- A few apps account for most of the traffic
- WWW, FTP, telnet

- A common approach is to model apps (this ignores distribution of destination!)
- time between app invocations
- connection duration
- # bytes transferred
- packet interarrival distribution

- Little consensus on models - hard problem
- Poisson models, Markov-modulated models
- Self-similar models

Internet traffic models: features

- LAN connections differ from WAN connections
- Higher bandwidth (more bytes/call)
- longer holding times

- Many parameters are heavy-tailed --> Self-similarity
- Examples: # bytes in call, call duration
- means that a few calls are responsible for most of the traffic
- these calls must be well-managed
- can have long bursts

- also means that evenaggregates with many calls not be smooth

- New models appear all the time, to account for rapidly changing traffic mix

Fractal dimension (1)

- A point has no dimensions
- A line has one dimension - length
- A plane has two dimensions - length and width, no depth
- Space, a huge empty box, has three dimensions - length, width, and depth
- Fractals can have fractional (or fractal) dimension
- A fractal might have dimension of 1.6 or 2.4
- If you divide a line segment into N identical parts, each part will be scaled down by the ratio r = 1/N

Fractal dimension (2)

- A two dimensional object, such as a square, can be divided into N self-similar parts, each part being scaled down by the factor
r = 1/N(1/2)

- If one divides a self-similar D-dimensional object into N smaller copies of itself, each copy will be scaled down by a factor r, where
r = 1 / N(1/D)

- given a self-similar object of N parts scaled down by the factor r, we can compute its fractal dimension (also called similarity dimension) from the above equation as
- D = log (N) / log (1/r)

The Koch Snowflake

- D = log (4) / log (3) = 1.26

Fractal images

- Fractal images can be very beautiful. Here are some of them, enjoy them!
- Fractal is rather an art than a science.

Self-similarity (1)

- How long is the coast-line of Great Britain?
- Using sticks of different size S to estimate the length L of a coastline

Self-similarity (2)

- It turns out that as the scale of measurement decreases the estimated length increases without limit.
- Thus, if the scale of the (hypothetical) measurements were to be infinitely small, then the estimated length would become infinitely large!

Self-similarity (3)

- self-similarity
- any portion of the curve, if blown up in scale, would appear identical to the whole curve (see a coast-line from an airplane)
- Thus the transition from one scale to another can be represented as iterations of a scaling process

- fractal ( by Mandelbrot )
- any curve or surface that is independent of scale
- This property, referred to as self-similarity

Self-similar feature of traffic

- Fractal characteristics
- order of dimension = fractal

- Self-similar feature
- across wide range of time scales

- Burstness
- across wide range of time scales

- Long-range dependence
- autocorrelations that span many time scales
- ACF (autocorrelation function)
r(k) = limT∫-T~T XtXt+kdt

- ACF does not decay exponentially as lag increases

Presence of self-similar features in measured network traffic

- Ethernet [Bellcore Leland 1992]
- Variable-Bit-Rate video traces [Bellcore 95]
- WAN-TCP [Paxson], NSFNET [Klivan],CERNET [TJU]
- Common Channel Signaling Network (CCSN) - SS7 - Terabyte range [Bellcore]
- MAN-DQDB and LAN cluster [Cinotti]
- FTP - a file server [Raatikainen]
- Web [Crovella][TJU]
- ATM-Bay Area Gigabit Testbed (BAGNet) [Siddevad]
- WiFi [Stanford, UCSD, TJU]

Self-similar models traffic

- Traditional models can only capture short-range dependence -- Poisson process, ARIMA(p,d,q) etc.
- Computer networks exhibits self-similarity, i.e. long-range dependence.
- Self-similar models
- only describe the long-range dependence, can't be used to describe the short-range dependence
- FGN (fractional Gaussian noise)
- FDN (fractional differencing noise) = FARIMA(0,d,0)

- FARIMA(p,d,q) (fractional autoregressive integrated moving average) model can describe both long-range and short-range dependence simultaneously

Network delay on FGN models traffic

Network delay on FARIMA models traffic

Network delay on FARIMA models trafficwith non-Gaussian distribution

N*N Cell Switch trafficwith Input and Output Queueing

FARIMA( trafficp,d,q) model and application

- FARIMA models
- Generating FARIMA processes
- Traffic modeling using FARIMA models
- Traffic prediction using FARIMA models
- Prediction-based bandwidth allocation
- Prediction-based admission control

The definition of chaos traffic

- Definition:
- Chaos is aperiodic time-asymptotic behaviour in a deterministicnon-linear dynamic system which exhibits sensitive dependence on initial conditions.

- Sensitive dependence on initial conditions (Butterfly effect) ：
- It refers to sensitive dependence on initial conditions. In nonlinear systems, making small changes in the initial input values will have dramatic effects on the final outcome of the system.
- The result of the butterfly effect is unpredictability.

Chaos vs. Randomness traffic

Do not confuse chaotic with random:

Random:

- irreproducible and unpredictable
Chaotic:

- deterministic - same initial conditions lead to same final state…but the final state is very different for small changes to initial conditions
- difficult or impossible to make long-term predictions

Nonlinear Instabilities in TCP-RED trafficPriya Ranjan, Eyad H. Abed and Richard J. La INFOCOM 2002

- This work develops a discrete time feedback system model for a simplified TCP network with RED (Random Early Detection) control.
- The model involves sampling the buffer occupancy variable at certain instants.
- The non-linear dynamical model is used to analyze the TCP-RED operating point and its stability with respect to various RED controller and system parameters.

Fig.5. trafficBifurcationdiagram of average and instantaneous queue length w.r.t. w, max = 0.1

Study on the chaotic nature of wireless traffic traffic

- Thecorrelation dimensionof wireless traffic traces isnon-integer number.
- The largest Lyapunov exponentof wireless traffic traces ispositive.
- The principal components analysisshowed that the intrinsic information of the traffic isaccumulatedin thefirst and few lower-index components.
- All those results indicated that the wireless traffic is a low dimensional chaotic system.
- This gives us the good theoretical basis for the analysis and modeling of wireless traffic using Chaos Theory.

SVM-Based Models for Predicting WLAN Traffic traffic

- SVM (Support Vector Machine): a novel type of learning machine, Presented by V. Vapnik et. al. in 1995
- Advantages
- Good generalization performance: SVM implements the Structural Risk Minimization Principle which seeks to minimize the upper bound of the generalization error rather than only minimize the training error
- Absence of local minima: Training SVM is equivalent to solving a linearly constrained quadratic programming problem. Hence the solution of SVM is unique and globally optimal
- Small amount of training samples: In SVM, the solution to the problem only depends on a subset of training data points, called support vectors

Related Works traffic

- SVM Applications
- Pattern recognition, document classification, etc.
- Time series prediction, Internet traffic prediction, call classification for AT&T’s natural dialog system, multi-user detection and signal recovery for a CDMA system, SVM-based bandwidth reservation in sectored cellular communications

- Our work:
- Applying SVM for wireless traffic prediction
- One-step-ahead prediction
- Multi-step ahead prediction

- Comparing its performance with three baseline predictors

- Applying SVM for wireless traffic prediction

One-step-ahead prediction traffic

- Show the one-step-ahead prediction performance for various prediction methods

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