Post on 19-Aug-2018
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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.