- Keynote I: MapReduce, BigTable, and Other Distributed System Abstractions for Handling Large Datasets by Jeff Dean, Google, Inc.
- Keynote II: Scaling Google for Every User Marissa Mayer, Vice President, Search Products & User Experience, Google, Inc.
- Lustre File System by Peter Braam, Founder and President, Cluster File Systems, Inc.
- SCTP's Reliability and Fault Tolerance by Brad Penoff, Mike Tsai, and Alan Wagner, The University of British Columbia Department of Computer Science.
- Scalable Test Selection Using Source Code Deltas by Ryan Gerard, Symantec Corporation.
- VeriSign's Global DNS Infrastructure, Patrick Quaid, Technical Director and Scott Courtney, Principal Architect, VeriSign.
- Using MapReduce on Large Geographic Datasets, Barry Brummit, Software Engineer, Google, Inc.
- YouTube Scalability, Cuong Do, Engineering Manager, YouTube.
However, there are two additional talks are available that appear to be part of the conference but were not on the original schedule:
- Building a Scalable Resource Management, Khalid Ahmed, Platform Computing Corp.
- Lessons In Building Scalable Systems, Reza Behforooz, Google Inc.
See also my earlier post, "More on Google Scalability Conference", that has links off to notes some attendees took during some of the talks.
Update: By the way, Marissa's talk, which is light and talks about the future of Google, is most likely to be of broad interest. If you only have time to watch one of these, watch that one. Her presentation is good, but the Q&A session starting at 34:47 is the thing not to miss. Marissa also talks up personalized search at 33:08 of her presentation, saying that it is key for building "the search engine of the future."
Update: In case you don't watch the YouTube talk, let me summarize it briefly. They tried to do MySQL database replication, faced many problems for a long time, and finally switched to what they should have done in the first place, large scale database partitioning. Unfortunately, this is a common mistake at many places struggling with scaling, overdoing database replication and caching and underdoing partitioning.