The network reading group discusses network research literature and ideas.
Location: 380 Soda Hall
Date & Time: Thursday 11:00am-12:00pm
Organizer: George Porter gporter@EECS.Berkeley.EDU.
To join the email list: Send mail to majordomo@iceberg.CS.Berkeley.EDU with the line "subscribe netread".
To email people: The alias for the group is netread@iceberg.CS.Berkeley.EDU.
Past meetings of the reading group:
Schedule:
01/31/02 |
George Porter |
02/07/02 |
Matthew Caesar |
02/14/02 |
Karthik Lakshminarayanan |
02/21/02 | Christos Papadimitriou. "Power Laws and the Internet" |
02/28/02 | Yan Chen: Practice talk for IPTPS paper (Powerpoint Slides) |
03/07/02 | Adam (12) |
03/14/02 | Lakshmi: Relationships between Proof Carrying Codes and Dynamic Packet State (11) |
03/21/02 | Sridhar: Checksums and Packet Reordering (12) PPT Slides |
03/28/02 | No NETREAD -- Spring Break |
04/04/02 | Weidong (12) |
04/11/02 |
Bhaskaran Raman |
04/11/02 | Bhaskar |
04/18/02 |
AP Presentation 1 Slides | Presentation 2 Slides John Byers, Jeffrey Considine, Michael Mitzenmacher and Stanislav Rost, "Informed Content Devlivery Across Adaptive Overlay Networks," SIGCOMM 2002. Ross Anderson, "Why Cryptosystems Fail?," Communications of the ACM, 1994. |
04/25/02 | Yan Chen |
05/09/02 |
Chris Olston (10:30am) Title: Approximate Data Caching In many data replication environments, exact synchronization between source data objects and cached copies is infeasible due to its demands on network bandwidth. As a result, stale (out-of-date) copies are permitted and synchronization is performed less frequently than source data changes. Overall, less frequent synchronization means decreased network cost but also decreased cache precision. Historically, the fundamental tradeoff between network cost and the precision of cached data has been managed in an ad-hoc way. In this talk I will outline a principled approach to managing and exploiting this tradeoff. If network usage is flexible but communication incurs a cost, it is desirable to minimize network cost while still providing guarantees about the precision of cached objects. Our solution, called TRAPP (for Tradeoff in Replication Precision and Performance), caches approximate values with bounded imprecision, rather than stale exact values. TRAPP guarantees to satisfy per-query precision constraints while minimizing network cost. Our solution incorporates adaptive algorithms that work well under fluctuating data update rates and query loads. The talk will provide an overview of our work, some empirical results, and an animated demonstration of one of our adaptive algorithms. |
Links of Interest: