• Calendar

May 3, Tuesday
12:00 – 13:00

Demanding historical data analysis like forecasting, formerly dependent on specialized data warehouses and temporal databases, can become available to everyday applications in off-the-shelf data stores. The challenge is to organize historical states so that they are ``always there'' when needed and "not in the way" when not. Retro approach integrates a low-level long-lived consistent snapshot system into a data store, allowing to run historical data analysis programs against the snapshots, side by side with programs running against the current state. The approach is attractive for several reasons. Applications can take snapshots at any frequency without disrupting the data store, and can garbage collect unneeded snapshots at low cost, an important feature in a long-lived system. Analysis programs can access snapshots at a cost that is independent of update workload and history length even for very old snapshots. A principled methodology allows to construct the snapshot system protocols in a modular way from the data store protocols, allowing to implement the snapshot system without extensive modifications to the data store internals, making the approach suitable in off-the-shelf data stores. The talk will describe the new techniques that underly Retro and present preliminary performance results from a prototype we built in Berkeley DB, indicating Retro is efficient, imposing moderate performance penalty on the native data store, on expected common workloads.

Bio: Liuba Shrira is a Professor in the Computer Science Department at Brandeis University, and is affiliated with the Computer Science and Artificial Intelligence Laboratory at MIT. She has been affiliated with Microsoft Research, Cambridge, UK, Microsoft Research Asia, Beijing, and is currently visiting the Computer Science Department, in the Technion, Haifa. Her research interests span aspects of design and implementation of distributed systems and especially storage systems. This includes fault-tolerance, availability and performance issues. Her recent focus is on long-lived transactional storage, time travel (in storage), software upgrades, and support for collaborative access to long-lived objects.