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Gerd Windisch - Chair for Communication Networks - Technische Universität Chemnitz

Analysing the impact of CDN

based service delivery on traffic

engineering

Gerd Windisch

Chair for Communication Networks

Technische Universität Chemnitz


Outline

• Introduction & Motivation

• Distributed Measurement Approach

• YouTube CDN Infrastructure

• Video Server Selection

• Impacts on Traffic Engineering

• Conclusion


Introduction & Motivation

• CDNs account for a big traffic share in todays networks

• Server selection strategies of CDNs are usually not aware of ISP internal

traffic congestion -> could negatively effect network performance

• Thus, the knowledge about the behaviour of server selection strategies of

major CDNs can provide valuable information to network operators (to

adapt the traffic engineering accordingly)

• Targets of the measurement study:

• get insight into the YouTube CDN infrastructure

• get insight into the video server selection strategies applied in the

YouTube CDN


Outline






• Conclusion


Distributed Measurement Approach

• Use of openly available HTTP

proxy servers located in several

ISP networks in Europe

• With this approach YouTube

could be seen from the

perspective of different ISPs

• Through these proxy servers a

set of 20 videos is requested

periodically and the response

(HTTP) is analysed

• 5 measurement traces with a

duration between 3 and 7 days

have been obtained with a time

resolution of 15 min


Outline






• Conclusion


YouTube Infrastructure

• 137 different YouTube locations were found within the measurement

traces

• 2 types of YouTube server locations were identified:

• YouTube owned data center locations

• Google Global Cache data center locations located in ISP networks

•

• 3779 IP addresses where measured, 3005 belonging to YouTube and

774 belonging to other ASes

• Remark: for this analysis all data sets were combined regardless of the

ISP and the measurement duration

YouTube AS

Locations

GGC

Locations

Total

EU 22 107 129

USA 8 0 8

Total 30 107 137


Outline






• Conclusion


Video Server Selection - Mechanism

• Server selection mechanism is used to direct a user request to the best

video server location (data center)

• multiple selection criteria might be used (e.g. distance, server load)

• Most common approach for CDNs: DNS based server selection

• Observation: YouTube changed its video server selection from an DNS

based approach to a URL rewriting based approach



DNS based Approach:

3) HTTP Get Response

Video web site

4) DNS Request

v1.lscache1.c.youtube.com 5) Select best

matching video

server and return IP6) DNS Response

Youtube Video Server IP

7) HTTP Get Request

v1.lscache1.c.youtube.com/...


video file

2) Map watchID to

static video server

URL 1) HTTP Get Request

www.youtube.com/watch?v=...

Local

DNS Server

YouTube DNS

Server

YouTube HTTP

Frontend Server

Client

YouTube

Video Server



URL-rewriting based Approach:

2) Select a video

server in the best

location, and embed

URL in web page


Video web site

5) DNS Response

Youtube Video Server IP

1) HTTP Get Request

www.youtube.com/watch?v=...

Local

DNS Server

YouTube DNS

Server

6) HTTP Get Request

r1---sn-4g57ln7d.c.youtube.com/...


video file

4) DNS Request

r1---sn-4g57ln7d.c.youtube.com

YouTube HTTP

Frontend Server

Client

YouTube

Video Server



• Advantages of URL rewriting based server selection mechanism:

• the server selection can be done based on the user IP address and

not on the IP address of the DNS Server -> better geo-location of

user

• additional criteria (other HTTP header fields) can be applied

• Disadvantage:

• URL rewriting only works for subsequent requests (but: the initial

request has to be handled via DNS selection mechanisms)


Video Server Selection – Pattern Classification

• Based on the measurement traces the regularity of the video server

selection patterns is analysed

• For a fair comparison all similar patterns observed in an ISP network on

different proxies and in different measurement traces are counted as

one observation

• Main result: the majority (166 out of 168) of all pattern observations can

be classified as two types:

• constant pattern

• daily recursive pattern

• Some shifts (16) within the patterns have been identified which are

however not daily recurrent


Video Server Selection - Pattern Classification

• Video server selection pattern types:

• Constant pattern

• no, or little changes of the video server locations

• this pattern appears most frequently

• Daily recurrent pattern

• clearly visible daily recurrence

• usually one server location in off-peak hours; load balancing among

some few server locations during peak traffic hours

• Results:

GGC

Locations

YouTube AS

Locations

Total

Constant pattern 75 27 102

Daily recurr. pattern 25 39 64

Total 100 66 166



Example: constant pattern – single source



Example: constant pattern – load balancing



Example: daily recurrent pattern



Example: daily recurrent pattern

prg

par

lhr

ams

fra

mia

lga

ord

AS35236,Czech Republic

0 h

1 h

2 h

3 h

4 h

5 h

6 h

7 h

8 h

9 h10

h11

h12

h13

h14

h15

h16

h17

h18

h19

h20

h21

h22

h23

h 0 h


Video Server Selection – Pattern Classification

Example: neither constant nor daily recurrent


Outline






• Conclusion


Impacts on Traffic Engineering

• Consequences of non-optimal traffic engineering -> packet loss/ delay

increase due to overloaded ISP internal paths

• Goal: optimized dynamic traffic engineering based on traffic shift

prediction

• traffic shifts are predicted based on observed pattern shifts

• only those pattern shifts are relevant which lead to traffic load shifts

on interconnect points

• from the predicted pattern shifts the expected shifts of the traffic

matrix can be derived -> path optimization

• Quality metrics:

• traffic matrix prediction precision

• optimization performance (speed, small optimality gap)

• ISP network reconfiguration speed


Outline






• Conclusion


Conclusion

• Key findings:

• YouTube utilizes a high number of GGC video server locations

• YouTube recently changed its video server selection mechanism to

a URL-rewriting based scheme

• the majority of all patterns can be classified into two categories:

• constant pattern

• daily recursive pattern

• Next steps:

• investigation of server selection strategies of other CDNs like

Akamai and Limelight

• finishing the development of an pattern prediction model (markov

model)

analysing the impact of cdn based service delivery on ... · major cdns can provide valuable...

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