Network Aware Forward Caching Presenter: Alexandre Gerber Jeffrey Erman, Mohammad T. Hajiaghayi, Dan Pei, Oliver Spatscheck AT&T Labs Research April 24 th, 2009

Outline Whats happening on the Internet? Traffic characterization Efficiency of existing delivery mechanisms Cacheability of HTTP traffic Lets revisit forward caching with a new twist! Network Aware Forward caching Page 2 © 2009 AT&T Intellectual Property. All rights reserved.

Data available to understand Internet traffic Application Mix and HTTP requests ( ): 100,000 broadband subscribers: US only: California, Texas, Illinois DSL not Cable Monitoring at the edge: BRAS (aggregation point) Application classification based on application header (e.g. MIME type) Efficiency of delivery mechanisms (October 2008): Complete Netflow based PoP to PoP Traffic matrix of a US broadband ISP Air Miles between Points of Presence (PoP) © 2009 AT&T Intellectual Property. All rights reserved. Page 3

Aggregate downstream traffic growth per subscriber Stable over the last 8 years in aggregate! 20-30% per subscriber per year Page 4 © 2009 AT&T Intellectual Property. All rights reserved.

Lets drill down: Application Mix over the last 2 years HTTP is back and it is growing fast! Thanks in part to Multimedia streams over HTTP! © 2009 AT&T Intellectual Property. All rights reserved. Page 5 Growing 3 times faster than the historical aggregate growth rate

Application Mix: January % of potentially reusable content © 2009 AT&T Intellectual Property. All rights reserved. Page 6 DownstreamUpstream Busy Hour Average

Lets keep drilling down: HTTP & Multimedia Its no longer about text and images, HTTP is the workhorse for data delivery (streaming or direct downloads) © 2009 AT&T Intellectual Property. All rights reserved. Page 7 /http/download accounts for 10.2% traffic of overall traffic while FTP accounts for 0.3% http accounts for 80% of the multimedia content delivery (flash)

Is this content delivered efficiently? CDNs are doing a good job! P2P protocols are not! © 2009 AT&T Intellectual Property. All rights reserved. Page 8 CDN traffic is traversing 60% fewer air miles than the average traffic P2P flows are traversing more air miles than the average traffic

Are CDNs big enough to have an impact? Yes, because they carry a significant fraction of large files. © 2009 AT&T Intellectual Property. All rights reserved. Page 9 46% of very large files are distributed by the 3 large CDNs studied

Are all the Points of Presence equal in the US? No, some are much closer/farther from content Page 10 © 2009 AT&T Intellectual Property. All rights reserved.

Are all the Autonomous Systems traversing the same distance on an ISPs backbone? No, some are much closer/farther from content Page 11 © 2009 AT&T Intellectual Property. All rights reserved.

What have we learned so far? 89% of the content during the busy hour may be reusable (HTTP content, Multimedia streams, P2P / File sharing) 68% of the content during the busy hour is coming from HTTP and HTTP is growing fast: it is the workhorse for data delivery and includes the strong growth of Multimedia streams There are big differences in the delivery of content when comparing air miles on an ISPs backbone: CDNs are doing a good job but other web content providers and P2P protocols are not Distance traversed to reach PoPs varies significantly What if we only cached the HTTP traffic that has to traverse the longest distance? © 2009 AT&T Intellectual Property. All rights reserved. Page 12

Lets first understand if HTTP content is really cacheable: Yes, it is! 32% of HTTP bytes served from the cache (24% of total downstream traffic during the busy hour) Cache size is reasonable (TB) Page 13 © 2009 AT&T Intellectual Property. All rights reserved. Based on 20,000 subscribers in January 2008

Network Aware Forward caching HTTP forward cache that is aware of the network: Understand the cost of each bit: Backbone cost: Air Miles traversed on ISPs network by each bit Source of traffic: free peering vs. paid transit traffic Lets only cache the bits that need it most Tradeoff between bandwidth cost vs. caching cost Caching decision made for: Each PoP: no caches? X caches? And each e ntity in each PoP: BGP prefix, AS, IP addresses, etc. Tradeoff between amount of traffic generated vs. storage requirement Combinatorial, knapsack like, NP-hard problem Can be solved with dynamic programming in pseudo polynomial time Or greedy heuristic by sorting entities in decreasing order of benefits © 2009 AT&T Intellectual Property. All rights reserved. Page 14

Solution sensitive to backbone costs and caching costs Page 15 © 2009 AT&T Intellectual Property. All rights reserved. Base scenario: $4/Mbps/Month for transit and backbone costs, $20K for caches (400 Mbps, 4TB) Sensitivity to Backbone costsSensitivity to Caching costs The optimal deployment is to cache 68% of the traffic. This is 37% better than a simple cache all solution!

Drilling down into the optimal solution: 25% of the PoPs dont need it a cache Only for 15% of the PoPs does it make sense to cache all the cacheable content Page 16 © 2009 AT&T Intellectual Property. All rights reserved.

Conclusion & Future Work Contribution: Characterization of Internet traffic & its distribution efficiency: There is a strong growth of HTTP content, especially multimedia streams Significant differences in distribution efficiencies Proposed and evaluated Network Aware Forward Caching for HTTP Future work: Explore other mechanisms to better distribute Internet traffic: network aware P2P, Multicast, Anycast, etc. Extend study to other environments: wireless IP networks © 2009 AT&T Intellectual Property. All rights reserved. Page 17