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Traffic Profiles and Management for Support of Community Networks
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
Flow statistics are relevant for quality management
– Dimensioning with regard to variability and QoS demands
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
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
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
