Network Planning VITMM215

Markosz Maliosz PhD

10/10/2016

Circuit vs. Packet Switching

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Circuit vs. Packet Switching

Traditional telephone network: circuit switching – constant speed

(bandwidth), continuous transmission

– connection-oriented – first a circuit is built (call

setup), then the transmission is started

– not ideal for bursty traffic

– simple terminals, smart network

– allows explicit QoS

Data network: packet switching – forwarding data units

(packets) from source to destination

– no preliminary connection: connectionless

– no bandwidth reservation

– ideal for bursty traffic – smart terminals, simple

network – difficult to provide QoS

Circuit vs. Packet Switching

Can be mixed – circuit switching on one layer, packet switching on another

MPLS creates virtual circuits (LSPs) between endpoints – LSPs are not between end users though

– allows multiplexing of traffic sources inside a connection

– multiplexed traffic is less bursty

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Traffic Aggregation

Access-backbone Aggregated traffic

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Without Careful Planning…

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What is Network Planning?

Applying scientific methods

Ongoing, iterative process over time

Optimization problem

– minimizing cost

– maximizing income

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Network Planning as an Optimization Problem

Real world complex network planning single optimization problem – no single objective

cost?, capacity?, reliability?, … how to balance the criteria? multicriteria optimization use some of the objectives, as constraints

– e.g. with a fixed cost limit, how to achieve maximum performance

– size of the problem In practice: the overall problem is divided

into a number of smaller, more manageable subproblems

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Temporal Scale of Planning

Long-term – Strategic considerations: resources, scheduling, pro

and contra – Structure of the network

Medium-term – Developments (most typical!) /replacement,

extensions/ e.g. if load is over 50%, then start the process for capacity

extension

Short-term – Unexpected demands – Operator intervention: reconfiguration

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Planning and Operating a Network

Planning a network

– Months – years

– Based on traffic demands

Configuring a network

– Days – weeks

– Traffic engineering and resource configuration

Real-time network control

– Seconds – minutes

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Types of Planning and Outputs

Green field – Given: placement of nodes – No links installed

1. Topology planning – Placement of links, i.e. direct connections

2. Routing, planning of paths – Planning paths between node pairs

3. Dimensioning, capacity planning – Determining the required capacity for nodes and

links

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Traffic Description

Traffic matrix:

– Symmetric or asymmetric

– Units, e.g.: bps

#of STM-1 links (155,520 Μbps)

Values: busy hour or averaged

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Trends in Planning

Which one is more cost-effective? – Overprovisioning capacity at network deployment

– Capacity extensions later – can be even 50-80% more expensive

Rule of thumb: overprovisioning – Reason: quick increase of traffic demands (5-50%, or

even 100% year-by-year)

– In most of the cases an overprovisioned network is filled within 3 years

Feedback effect: if a network operates well, then it attracts new users it gets overloaded

Overview

We will investigate specific planning problems

Objectives: students should be able to

– define network planning problems (objectives, constraints, etc.)

– choose suitable algorithm for solving the problem

– understand the methods and algorithms and their applicability

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Cost modeling, cost functions

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Cost Types in Networking

Capital Expenditures (CAPEX) – network equipment: cables, switches, routers,

software, etc. – premises – land that cables run along (right of way)

Operational Expenditures (OPEX) – manpower cost (installation, administration, support,

etc.) – repairs and upgrades – planning, design – power – transit traffic costs

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Cost Components

Links: – Fixed costs

Investment: one-time cost, installation cost, etc. – Proportional cost

Distance proportional – in access networks cabling cost is more than half of the total cost

Bandwidth proportional – Variable cost (depends on utilization, load)

Nodes: (routers, switches, etc.) – Fixed costs

Investment: cost of equipment (hardware+software), installation, renting fee

– Proportional costs number and speed of line cards, capacity features

– Variable cost (depends on utilization, load) power consumption, cooling

Link Cost Model

L = k + αc + βd

linear model

c: link capacity

d: link distance

k, α, β are constants

some terms can be zero

this is a simplification: it is easier to handle this in optimization problems

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Cost Functions Examples

Capacity proportional component:

Linear

Entry cost:

– digging, cabling devices

Stepwise: jump at particular line speeds

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Cost Function Examples

Linear: by wavelengths

Step-wise: by fibers

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Cost Function Examples

Piece-wise linear

Different levels of economy

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Cost Function Examples

Distance proportional (WAN):

Step-wise: at regenerators – optical fiber – microwave link

Cost Model vs. Reality

The linear cost model is not really good In term of cost this is a discrete problem

– links have discrete capacities: e.g. Ethernet: 10Mbps, Fast-Ethernet: 100Mbps, GigE:

1 Gbps, 10GigE: 10 Gbps

– too complicated to handle – it is hard to get exact pricing information

depends on size of order, company policies, discounts from vendors for multiple orders, etc.

Despite: it is often treated as linear, continuous function, as an approximation

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Linear Programming

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Linear Programming

Linear Programming is the optimization of an objective based on some set of constraints using a linear mathematical model

The word “programming” is used in the sense of “planning” A Linear Program (LP) is a problem that can be expressed as

– a linear objective function, – subject to linear equality and linear inequality constraints

Has many applications – transportation, telecommunications, manufacturing, business

e.g. maximizing profit in a factory that manufactures a number of different products from the same raw material using the same resources

– engineering: planning, routing, scheduling, assignment, design minimizing resources in network planning to satisfy user demands maximizing performance of the network with a limited cost budget

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Linear Program Standard form: find the values of xj variables, that minimize (or

maximize) the objective function z

subject to (satisfying the following constraints):

Notation: – objective function: z – variables to be determined, decision variables: xj

– number of variables: n – coefficients: cj, aij, bi

– number of constraints: m – bounds for variables

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Linear Program

Standard matrix form:

c: cost coefficients

– subject to:

xczT

max

bxA

nx

x

x 1

n

Tccc 1

mnm

n

aa

aa

A

1

111

mb

b

b 1

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LP Example A company manufacturing two products: P1, P2 3 manufacturing units are required to manufacture both products:

U1, U2, U3 During a week the maximum operating hours for the machines:

– U1: 50 hours – U2: 35 hours – U3: 80 hours

To manufacture a product it has to be processed on all of the 3 manufacturing units with the following manufacture times in hours (the sequence does not matter): – U1 U2 U3 – P1 10 5 5 – P2 5 5 15

Price of P1: 100$ Price of P2: 80$ How many pieces should from P1 an P2 to be manufactured to

achieve maximum income?

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LP Example

The task is an optimization problem: find the maximum income

Variables to be determined: amount of P1 and P2 to be manufactured, denoted as: x1 and x2

Objective function – maximize income = max (100 x1 + 80 x2)

The limited working hours of the manufacturing units (U1, U2, U3) restrict the amount of products that can be produced during a week constraints

U1 U2 U3 P1 10 5 5 P2 5 5 15 MWH 50 35 80 MWH= max. working hours

Constraints: 10 x1 + 5 x2 ≤ 50 5 x1 + 5 x2 ≤ 35 5 x1 + 15 x2 ≤ 80 x1 ≥ 0 x2 ≥ 0

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Finding the Solution

Feasible solutions

– x vectors, which satisfy the constraints and bounds

Optimal solution

– x is feasible and cTx is minimal (maximal)

Problem can be

– infeasible

– unbounded

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Geometrical solution 2D representation:

Constraints: set of straight lines

F(x) = 100 x1 + 80 x2 = 800 (const)

(a) 10x1 + 5x2 50 (b) 5x1 + 5x2 35 (c) 5x1 + 15x2 80

a b x1

x2

10

5

15 10 5 c

P2

P1 3

4

P0

F(x) = 800

Sol:

X1 = 3

X2 = 4 Extreme

point

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Geometrical Representation Ax b

Optimum:

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Solving LP

Good general-purpose techniques exist for finding optimal solutions – All polynomial-size linear programs can be solved

in polynomial time – Algorithms

Simplex Barrier or Interior Point Method

– Software CPLEX GNU Linear Programming Kit (GLPK) lp_solve etc.