Traffic Profiles & Mgnt.

for Community Networks

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

Traffic Profiles and Management

for Support of Community Networks

Gerhard Haßlinger1, Anne Schwahn2, Franz Hartleb2 1Deutsche Telekom Technik, 2T-Systems, Darmstadt, Germany

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

Traffic Profiles & Mgnt.

for Community Networks

Overall Measurement Statistics and Mean Values

Traffic profiles

Number of packets

[x 1000]

Packet size [Byte]

(Mean)

Number of flows

Flow size [MB]

(Mean)

Flow rate [Mbit/s]

(Mean)

Flow duration

[s] (Mean)

YouTube 17 809 1468 8 419 3.07 1.44 66

Twitter 857 662 318 0.25 0.04 129

Facebook 14 619 564 13 555 0.38 0.06 657

Uploaded 15 013 1508 508 44.54 0.46 872

Voice 4 149 295 270 4.53 0.08 455

Total traffic 1 446 065 1177 697 786 2.26 1.40 56

Traffic Profiles & Mgnt.

for Community Networks

Flow Rates for Different Application Types

Traffic Profiles & Mgnt.

for Community Networks

Flow Volume for Different Application Types

Traffic Profiles & Mgnt.

for Community Networks

Flow Durations for Different Application Types

Traffic Profiles & Mgnt.

for Community Networks

Round Trip Delays for Different Application Types

0%

20%

40%

60%

80%

100%

0,01 0,1 1

TCP Round Trip Time [s]

Facebook

Twitter

Total traffic

Youtube

Uploaded

Traffic Profiles & Mgnt.

for Community Networks

Traffic in Multiple Time Scales: 2nd Order Statistics

500

600

700

800

900

1000

0 1 2 3 4 5 6 7 8 9 10

Seconds

Tra

ffic

ra

te p

er

0.0

1s

in

terv

al

[Mb

it/s

]

500

600

700

800

900

1000

0 10 20 30 40 50 60

Seconds

Tra

ffic

ra

te p

er

0.1

s i

nte

rva

l [M

bit

/s]

500

600

700

800

900

1000

0 10 20 30 40 50 60

Seconds

Tra

ffic

ra

te p

er

1s

in

terv

al

[Mb

it/s

]

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 Profiles & Mgnt.

for Community Networks

2nd Order Statistics for Different Application Types

Traffic Profiles & Mgnt.

for Community Networks

Users

Global

Internet Access

Network

ISP

Backbone

Peering

Other

ISPs

Long paths for P2P data exchange P2P

Short CDN paths

CDN

PoPs

Points of Presence Access Control

P2P

Users

Global Content Delivery: CDN Peer-to-peer overlays

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

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

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