Network Resource Design - Overview ECE/CSC 570: Fall, 2010, Sections 001, 601.

Network Resource Design - Network Resource Design - OverviewOverview

ECE/CSC 570: Fall, 2010, Sections 001, 601

PositioningPositioning Networks must be designed (resource

provisioned) Design should proceed on the basis of

– What use the network is likely to be put to– What behavior is expected or desired from network

Different answers to above questions– Will result in different approach to design

Copyright Rudra Dutta, NCSU, Fall, 2010


Network TrafficNetwork Traffic Ultimately, networks exist to serve traffic (enable traffic

to be carried) What is traffic?

– That which occupies / is carried by links Traffic is offered to the network by/at network nodes

– Network is made of end nodes, intermediate nodes, and links– All traffic ultimately originated by end-nodes– However, for hierarchical networks, aggregation may occur

In some network paradigms, E2E traffic is recognizable at all “places” in network

In others, components within aggregated traffic not recognizable inside network


Traffic CharacterizationTraffic Characterization Traffic - “Demand” for networking services: b/w and

switching Magnitude (bandwidth)

– Could vary with time, if “reasonably long” life Lifetime

– How long it is resident in the network Arrival and departure patterns

– Call (like telephony) arrival and departure– Increment and decrement– Periodic (scheduled)– Static (long-term)

Requirement of performance– Hard or statistical


Network DesignNetwork Design Various aspects of the network must be

determined/chosen/configured Network resources - nodes and links Nodes

– Circuit (physical connection) interface– Buffers, scheduling, routing/forwarding, protocol

Links– Circuit enablement, bandwidth (bitrate capacity), protocol

Goals are in terms of network performance (experienced by traffic)– Basic goal: Connectivity– Basic design methodology: Routing– Others: b/w (if possible), buffer, resource management e.g. link

scheduling– Topology, transmission power, battery allocation, …


Issue of Traffic EngineeringIssue of Traffic Engineering Connectivity-only routing (traditional shortest path)

ignores all traffic metrics But traffic exists

– Consider flows 1 4, 1 6, 24, 26

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5

6

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Network PerformanceNetwork Performance Ultimately, measured in quantities the end-user

cares about– Assuming we have connectivity, now what?

Delay, throughput– Other metrics derived from these

More sophisticated metrics– Predictability of above metrics– Predictability of connectivity: Reliability / Survivability– Predictability of delay or throughput

Guarantees - Quality of Service contracts

– Other emergent characteristics: e.g. Security


Designing in Traffic NetworksDesigning in Traffic Networks Controversial proposition:

– “If delay is not important, capacity is not important”– “If delay is important, capacity must be large OR aggregation

must be slotted OR both” Consider the position of router R below

R

1

3

2

4

Q


Statistical TDM PerformanceStatistical TDM Performance Bursty traffic, statistical TDM Usual M/M/1 assumptions

– In reality, traffic process is heavier-tailed D(λ, μ) = 1 / (μ - λ) Delay is lower on average: “Statistical Multiplexing Gain”

– But unpredictable for individual packet - prediction is statistical

Link utilization λ/μ

Ave

rag

e D

ela

y (m

s)

R

1

3

2

4

Q


M/M/1 QueueM/M/1 Queue

0 1 2 3 4 5 6

p2λ + p4μ = p3 (λ+μ)p1 = p0 -λμ

λ λ λ λ λ λ λ

μ μ μ μ μ μ μ

Network of RoutersNetwork of Routers



Blocking in TelephonyBlocking in Telephony Delay - very small and constant, operative

quantity is blocking ratio Average call rate λ Average holding time τ Offered traffic load or intensity a = λτ

ac / c! B(a,c) = -------------------

Σk=0 ak / k!c

X Q

Telephone NetworkTelephone Network



Static Traffic PerformanceStatic Traffic Performance Given “matrix” of traffic demand components

– Static, “always-on”– Usually aggregate– Measured or estimated

Delay - fairly constant for each demand, small Blocking - none; loss - none

– Except in unusual circumstances Performance is measured globally

– Various objectives– Delay or throughput (global, across all components)– Revenue, fairness, protection, …


Transport, Demand, CapacityTransport, Demand, Capacity Traffic Networks and Transport Networks Traffic networks: where stochastic demand

picture is operative– Short term switching/routing

Transport networks: where traffic demands of static magnitude are seen to be operative– (Semi-) Permanent– QoS considerations paramount– Demands seen to be injected at transport network

nodes, lower level networks not visible Links must have capacity to carry traffic

– But routing can be designed on basis of traffic


Flow Routing and Global RoutingFlow Routing and Global Routing Most general view of routing

– Any part of any flow can be routed along some path from source to destination

Requires the ability to “mark” every part that has to be routed in a distinct manner

– Using labels, or timeslots

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Mathematical Programming ProblemsMathematical Programming Problems A steel company must decide how to allocate production time on a rolling

mill. The mill takes unfinished slabs of steel as input and can produce either of two products: bands and coils. Bands roll off the mill at 200 Tons/hr, generate $25/Ton profit, and at most 6000 Tons per week can be produced. The same figures for coils are 140 Tons/hr, $30/Ton profit, 4000 Tons/wk. If 40 hours of production time are available, what should be produced to maximize profit?

Example adapted from “AMPL”, by Kernighan et al, 1993

Maximize: total profitSubject to: total number of production hours 40

tons of bands produced 6,000tons of coils produced 4,000

Verbal model – Put the objective and constraints

into words– For constraints, use the form

{a verbal description of the LHS} {a relationship} {an RHS constant}

Define the Decision Variables – XB number of tons of bands produced.

– XC number of tons of coils produced.

Construct the Symbolic ModelMaximize:

Subject to:

CB XX 3025 +

( ) ( ) 4014012001 ≤+ CB XX

60000 ≤≤ BX40000 ≤≤ CX


Solving LP ProblemsSolving LP Problems

Bands00 2000 4000 6000 8000

Coils

2000

4000

6000Constraints

Feasible region

00 2000 4000 6000 8000

Bands

Coils

2000

4000

6000220K

192K

120K

Profit

Optimal solution

Hours

Graphical Solution Method


Solving LP ProblemsSolving LP Problems

Unique Optimal Solution Alternate Optimal Solutions

No Feasible Solution Unbounded Optimal Solution

4 Possible Outcomes


Solving LP ProblemsSolving LP Problems Simplex method

– Efficient algorithm to solve LP problems by performing matrix operations on the LP Tableau

– Developed by George Dantzig (1947)– Can be used to solve small LP problems by hand

AMPL and CPLEX– AMPL: modeling language (and software) for designing large and

complex LP/IP problems (now use OPL)– CPLEX: software package (“solver”) to solve large and complex

LP/IP problems Sub-Optimal Algorithms (Heuristics)

– Simulated annealing– Genetic algorithms– Tabu search– Many others, often very specific to the type of problem.


Integer ProgrammingInteger Programming

Maximize:

Subject to:

CB XX ′+′ 3000025000

( ) ( ) 4014010002001000 ≤′+′ CB XX

,60 ≤′≤ BX,40 ≤′≤ CX

integer

integer

Convert Example to Integer Program– Assume that orders for bands and coils are placed (and filled) in

1,000s of pounds only.– Although feasible region is greatly reduced, problem becomes much

more difficult. New Symbolic Model

– Let the new decision variables be the number of 1000 pound “units” or orders of bands and coils.


Integer ProgrammingInteger Programming

00 2 4 6 8

2

4

6

Feasible integer solutions

Bands

Coils

$185K

Optimal integer solution ($185K)

Graphical Solution Method


Multi-Commodity Flow FormulationMulti-Commodity Flow Formulation Parameters

– n : number of nodes– A : set of all links (i, j) – uij : bitrate of link– cij : cost per bit on link– bkl : traffic demand from node k to node l

Variables– xkl

ij : traffic from k to l using link from i to j Goal: minimize total cost

Source: Bertsimas and Tsitsilkis

j

l

i

k


Multi-Commodity Flow FormulationMulti-Commodity Flow Formulation

i


Management Cycle and DesignManagement Cycle and Design

Near Real-Time

Capacity Mgmt, Netw Engg.

Network Planning

Reactive Protocol Design

Algorithm Design

Resource Design


SummationSummation In low level networks, traffic is bursty, unpredictable, and in general

low– A traffic network– Impractical to design for peak traffic, other notions not very meaningful– Design for connectivity, with roughly correct capacities

L3-switched/routed traffic can be thought of as static at a high level of network

– A transport view of network is appropriate, using slotted TDM– This approach is indispensable when strong guarantees must be made

w.r.t. delay, variability of delay, and bandwidth– Capacity of links becomes important in meeting such guarantees– Capacity, routing, and other variables can be thought of as “control

knobs” in the ensuing design problem For circuits, can reflect physical resource occupations to obtain

quantitative idea– May also be useful for “logical” circuits at L3 (or not)

Network Resource Design - Overview ECE/CSC 570: Fall, 2010, Sections 001, 601.

Documents

securitycopyright rudra

designcopyright rudra

aggregated traffic

e2e traffic

linksall traffic

network paradigms

network performanceultimately

network trafficultimately