traffic delivery evolution in the internet enog 4 – moscow – 23 rd october 2012
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Traffic delivery evolution in the InternetENOG 4 – Moscow – 23rd October 2012
Christian KaufmannDirector Network ArchitectureAkamai Technologies, Inc.January 29th, 2008
©2012 Akamai
way-back machineway-back machine
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way-back machineway-back machine
Web 1998
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Web 1998
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Web 2012
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Web and Cloud 2012
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So what’s the difference?
• Lots of “high definition” content being pushed to end users
• Read “large files”
• Can the Internet scale to support this?
-> Short answer: NO
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Key Issues
• Problems with a centralized approach, especially for large media files
• Problems with Peering
• Problems with routing protocols
• Inter AS Multicast not really existent
• QoS not really existent and consistent
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Media
Gaming
eCommerce
Mobile
Blogging
Online Banking
Social Networking
Trends 2012
Immersive
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Media
Gaming
eCommerce
Mobile
Blogging
Online Banking
Social Networking
Simple on the outside …
Immersive
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… complicated on the inside
Media
Gaming
eCommerce
Mobile
Blogging
Online Banking
Social Networking
Immersive
L3
Telia DT
IX
IX
Verizon
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©2012 Akamai
•Centralized sites create an inherent bottleneck and target for attackers
•Worldwide user population = huge infrastructure problem
•Not scalable
•Long latency between server and end-user
The Centralization Bottleneck
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•Content Distribution Networks solve the centralization problem by distributing content.
•Greater distribution means greater performance and reliability
The Centralization Bottleneck
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The Edge is Highly Distributed
% ofAccessTraffic
ASes (29,000+)
4.4% Network A3.6% Network B1.8% Network C1.7% Network D1.7% Network E1.7% Network F1.6% Network G1.4% Network H
• No one Autonomous System has more than 4.4% of the access traffic.
• The top 50 ASes add up to only 48%.
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Edge Proximity From 1 Location
3000
1
Locations
Average Miles to Edge
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Edge Proximity From 30 Locations
3000
1500
1 30
Locations
Average Miles to Edge
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Edge Proximity From 1000 Locations
3000
1500
250
1 30 1000
Locations
Average Miles to Edge
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Does Latency Matter for Large File Downloads?
…who cares if the latency to
download a 2-hour DVD
is 10ms or 100ms?
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Big Data Center
Approach
Akamai
The Fat-file Paradox: Latency Limits Throughput…
0.6%
1.4%
1.0%
0.7%
1.6 ms
96 ms.
48 ms.
16 ms
12.2 min.
20 hrs.
8.16 hrs.
2.2 hrs.
Local <100 mi.
Different Continent <6,000 mi.
Cross Continent <3,000 mi.
Regional 500-1000 mi.
…and throughput limits the time to download large files (e.g., 4 GB DVDs)
Download Time
Packet Loss
Network Latency
Distance from Server to User Throughput
44 Mbs
0.4 Mbs
1 Mbs
4 Mbs
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©2012 Akamai
Centralized Approach with “Big Data Centers”
Clusters of Web servers in largedata centers at the core of the Internet
• Tens of data centers
• Transit provided by large backbone ISPs
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• Economic considerations limit peering capacity – results in congestion and poor performance
• Routing algorithms (BGP) ignore congestion
• BGP ignores latency
• Data used to determine routes is subject to intentional inaccuracies and human error
The Problems with Peering
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BGP picks the “best” route and all packets flow over that path
Normal traffic flow
CW
Web Server
Level 3
SprintEnd User
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CW
Web Server
Level 3
SprintEnd User
If there is congestion on a link, BGP will continue to send packets down that link
Normal traffic flow
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Web Content
Web Users
$
Infrastructure
50X50X
20X20X
6X6X
BottleneckEconomics
$$
$$$
$$$
$$$
First Mile
Hosting
Broadband
Last Mile
MiddleMile
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FirstMile
FirstMile
Edge ISPs
Edge ISPs
FirstMile
Edge ISPs Edge ISPs Edge IPSs
Edge ISPs Edge ISPs
The Big Data Center Approach to Content Delivery
Data Centers(10s)
Local ISPs(1,000s)
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FirstMile
FirstMile
Edge ISPs
Edge ISPs
FirstMile
Edge ISPs Edge ISPs Edge IPSs
Edge ISPs Edge ISPs
The Big Data Center Approach to Content Delivery
Problem 1 Data centers are on the wrong side of the bottleneck
Data Centers(10s)
Bottleneck
Users
Local ISPs(1,000s)
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The Big Data Center Approach to Content Delivery
Problem 1 Data centers are on the wrong side of the bottleneck
Edge ISPs
Edge ISPs Edge ISPs Edge ISPs Edge IPSs
Edge ISPs Edge ISPs
Tier 1IPS’s
Tier 1IPS’s
Tier 1IPS’s
Solution Caching servers are on the right side of the bottleneck
Users
Bottleneck
Data Centers(10s)
Local ISPs(1,000s) Cache Servers
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FirstMile
FirstMile
FirstMile
CDNCDNCDN
The Big Data Center Approach to Content Delivery
Edge ISPs
Edge ISPs Edge ISPs Edge ISPs Edge IPSs
Edge ISPs Edge ISPs
Problem 2 Data centers are far from end users
Local ISPs (1,000s)
Data Centers(10s)
Users
500-5,000 miles
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Tier 1IPS’s
Tier 1IPS’s
Tier 1IPS’s
CDNCDNCDN
The Big Data Center Approach to Content Delivery
Edge ISPs
Edge ISPs Edge ISPs Edge ISPs Edge IPSs
Edge ISPs Edge ISPs
Problem 2 Data centers are far from end users
Local ISPs (1,000s)
Data Centers(10s)
Users
Solution Cache servers are close to end users
Cache Servers
10-100 miles
500-5,000 miles
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©2012 Akamai
The Akamai System
The world’s largest on-demand, distributed computing platform
delivers all forms of Web content and applications for over 130,000 domains.
Resulting in traffic of:13 Tbps peak traffic100+ petabytes / day1,468+ billion hits / day 560+ million unique clients IPs / day
The Akamai EdgePlatform:
120,000+Servers
1928POPs
83Countries
1069Networks
660+ Cities
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©2012 Akamai
Conclusion: How to deliver HD traffic in the future?
• Overlay CDN networks are state of the Art
• Akamai, Google, Netflix, using on-net servers
• Protocols like Multicast and QoS are not the solution
• CDN Interconnects and Federations are not the solution either
• Peer to peer networks - unclear so far
• Waiting for the next big idea
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©2012 Akamai
Questions?
• ck@akamai.com
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