Determining Accurate Traffic Matrix for IP

Document Sample

```					Determining Accurate Traffic
Matrix for IP Networks

Based on NANOG39: Best Practices
for Determine the Traffic Matrix in IP
Network (Thomas Telkamp)

ECE Department, University of Manitoba
Background
Traffic Matrix: the amount of data
transmitted between every pair of
network nodes
Traffic Matrix can represent peak
traffic, or traffic at a specific time
Router-level or PoP-level matrices

2
Purpose
Analysis and Evaluation of other network states than
the current
Capacity Planning
network changes
“what-if” scenarios
Could be per class
Resilience Analysis
network under failure conditions
Optimization
Topology – Find bottlenecks
OSPF/IS-IS metric optimization/TE
MPLS TE tunnel placement

3
Types of Traffic Matrices
Internal Traffic Matrix
PoP to PoP matrix
Can be from core (CR) or access (AR) routers
Class based
External Traffic Matrix
PoP to External AS
BGP
Origin-AS or Peer-AS
Peer-AS sufficient for Capacity Planning and Resilience
Analysis
Useful for analyzing the impact of external failures on the core
network (capacity/resilience)

4
Traffic Matrix Data Collection
Data is collected at fixed intervals
E.g. every 5 or 15 minutes
Measurement of Byte Counters
Need to convert to rates
Based on measurement interval
Counter roll-over issues
Create Traffic Matrix
Peak Hour Matrix - 5 or 15 min. average at the
peak hour
Peak Matrix - Calculate the peak for every demand

5
Collection Methods
NetFlow
Routers collect “flow” information
Export of raw or aggregated data
BGP Policy Accounting/DCU
Routers collect aggregated destination statistics
• MPLS
RSVP - Measurement of Tunnel/LSP counters
LDP - Measurement of LDP counters
Estimation
Estimate Traffic Matrix based on Link Utilizations
Issues associated with each method?

6
General Formulation
Y = AX

A = Routing      Matrix
X = Po int − to − Po int   Demands

xirtaM gnituoR
644474448
 Y1                    X 
   a11 a12 . . a1k  1 
 Y2   a               X
a22 . . a2 k  2 
 .   21                
 = .                  . 
. . . . 
.                      . 
.  .     . . . .  
   al1 al 2 . . alk  . 
                
Y                     X 
 l                      k

7
Demand Estimation
Underdetermined system:
N nodes in the network
O(N2) demands (unknown)
Many algorithms exist:
Gravity model
Iterative Proportional Fitting (Kruithof’s Projection)
Maximum Likelihood Estimation
Entropy maximization
Bayesian statistics (model prior knowledge)
Etc...!
Calculate the most likely Traffic Matrix

8
Related Works
Several algorithms have been published
Comparisons and Improvements have
been proposed and implemented
Commercial Software e.g Cariden MATE
Software
Best Practices for Implementation have
been suggested

9
Issues with data collected and overhead
associated with the collection method – Best
Practice
Accuracy Traffic Matrix Estimation based on
existing methods
Possibility of proposing a new TM Estimation
Method or improving on existing ones
Use proposed TM for Capacity planning,
Route Optimization and resilience analysis
using real data and measure performance

10
11

```
DOCUMENT INFO
Shared By:
Categories:
Stats:
 views: 19 posted: 2/6/2010 language: English pages: 11
How are you planning on using Docstoc?