1. Traffic Profiles & Mgnt.
for Community Networks
Traffic Profiles and Management
for Support of Community Networks
Gerhard Haßlinger1, Anne Schwahn 2, Franz Hartleb2
1Deutsche Telekom Technik, 2 T-Systems, Darmstadt, Germany

Measurement on Network Links
– Packet and flow based analysis methods
– Traffic profiles for some large community networks

Traffic Management for Content and Service Delivery

Conclusions and Outlook

2. Traffic Profiles & Mgnt.
for Community Networks
Measurement of Application and Traffic Profiles

Probes can capture each IP packet: header, payload, time stamp

DPI: Content inspection (not applied for our statistics)

Analysis traffic pattern of per IP flow

A flow is identified by IP address/TCP port of source/receiver
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Flow statistics are relevant for quality management
– Dimensioning with regard to variability and QoS demands
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Traffic profiles are used to identify portions of applications
– We consider portions of Facebook, Twitter, Uploaded,
YouTube, VoIP
– Measurement from March’13 on 3 x 1Gb/s aggregation links

3. Traffic Profiles & Mgnt.
for Community Networks
Overall Measurement Statistics and Mean Values
Traffic
profiles
YouTube
Twitter
Facebook
Uploaded
Voice
Total traffic
Number
of packets
[x 1000]
17 809
857
14 619
15 013
4 149
1 446 065
Packet size
[Byte]
(Mean)
1468
662
564
1508
295
1177
Number
of flows
8 419
318
13 555
508
270
697 786
Flow size
[MB]
(Mean)
3.07
0.25
0.38
44.54
4.53
2.26
Flow rate
[Mbit/s]
(Mean)
1.44
0.04
0.06
0.46
0.08
1.40
Flow
duration
[s] (Mean)
66
129
657
872
455
56

4. Traffic Profiles & Mgnt.
for Community Networks
Flow Rates for Different Application Types

5. Traffic Profiles & Mgnt.
for Community Networks
Flow Volume for Different Application Types

6. Traffic Profiles & Mgnt.
for Community Networks
Flow Durations for Different Application Types

7. Traffic Profiles & Mgnt.
for Community Networks
Round Trip Delays for Different Application Types
100%
80%
Facebook
60%
Twitter
Total traffic
40%
Youtube
Uploaded
20%
0%
0,01
0,1
TCP Round Trip Time [s]
1

8. Traffic Profiles & Mgnt.
for Community Networks
Traffic in Multiple Time Scales: 2 nd Order Statistics
Evaluation of a traffic trace in
0.01s , 0.1s  and 1s  intervals
on broadband access platform:
Variability is decreasing on
larger time scales, although
long range dependency persists
Traffic rate per 0.01s interval [Mbit/s]
1000
900
800
700
600
500
0
1
2
3
4
5
6
7
8
9
10
Seconds
1000
Traffic rate per 1s interval [Mbit/s]
Traffic rate per 0.1s interval [Mbit/s]
1000
900
800
700
600
900
800
700
600
500
500
0
10
20
30
Seconds
40
50
60
0
10
20
30
Seconds
40
50
60

9. Traffic Profiles & Mgnt.
for Community Networks
2nd Order Statistics for Different Application Types

10. Traffic Profiles & Mgnt.
for Community Networks
Global Content Delivery: CDN  Peer-to-peer overlays
P2P
P2P
Long paths for P2P data exchange
CDN
Short CDN paths
Users
Users
Other
ISPs
Access
Network
ISP
Backbone
PoPs
Peering
Points of Presence
Access Control
Global
Internet

11. Traffic Profiles & Mgnt.
for Community Networks
Cacheability on the Internet
 An essential portion of IP traffic uses HTTP protocol (80% in 2013),




most of which is marked as being cacheable, often with expiry date
Requests focus on most popular content  small caches are efficient
Zipf law  90 10 rule: 90% of requests address only 10% of content
Some content providers/CDNs support caching, e.g. software updates
… others don’t: Personalised communication with user
 makes content identification difficult for cache manager;
no standard feedback & control between cache  content provider
Some content providers/CDNs have business relations
with content owners and/or users but often
without involving network providers

12. Traffic Profiles & Mgnt.
for Community Networks
IETF Standardization Groups on CDNI and ALTO
 Caching is applied in global content delivery networks
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
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and in network provider platforms of large ISPs …
but usually without much cooperation!
Content and CDN provider would like full control on client-server
activity  ISP would like full control of their network and caches
IETF working group on CDN interconnection (CDNI) since 2011
<http://datatracker.ietf.org/wg/cdni/charter/>
IETF WG on Application Layer Traffic Optimization (ALTO)
- Focus on localized data exchange for P2P and other applications
- ALTO servers collect data on locations of peers/clients
and make it available to applications/overlay networks
- Infos: provider network (AS) of endpoints; topology & cost maps
- Network providers can host ALTO servers to recommend sources
for content delivery without revealing their network

13. Traffic Profiles & Mgnt.
for Community Networks
Conclusions and Outlook
 We analyzed traffic profiles of popular applications
in community networks

IP flow and packet analysis is useful for classifying portions of
application traffic even without DPI

Characteristics of flow rates, volume, duration and 2nd order stat.
differ for each application; community networks generate a mix
of applications
For further study: QoS Characteristics in TCP round trip delay
and packet loss; improved identification using traffic profiles



Popular global communities with high traffic demand are using
CDN and P2P overlays, which are subject to long transport paths
Traffic optimization is considered by IETF working groups
CDNI and ALTO based on cooperative approaches
between administrative domains to improve local data exchange