SLOG: Serializable, Low-latency, Geo-replicated Transactions
Daniel J Abadi, University of Maryland
For decades, applications deployed on a world-wide scale have been forced to give up at least one of (1) strict serializability (2) low latency writes (3) high transactional throughput. In this talk we discuss SLOG: a system that avoids this tradeoff for workloads which contain physical region locality in data access. SLOG achieves high-throughput, strictly serializable ACID transactions at geo-replicated distance and scale for all transactions submitted across the world, all the while achieving low latency for transactions that initiate from a location close to the home region for data they access. SLOG can reduce latency by more than an order of magnitude relative to state-of-the-art strictly serializable geo-replicated database systems such as Spanner and Calvin, while maintaining high throughput under contention.

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About the speaker
Prof. Abadi performs research on database system architecture and implementation, especially at the intersection with scalable and distributed systems. He is best-known for the development of the storage and query execution engines of the C-Store (column-oriented database) prototype, which was commercialized by Vertica and eventually acquired by Hewlett-Packard in 2011, for his HadoopDB research on fault tolerant scalable analytical database systems which was commercialized by Hadapt and acquired by Teradata in 2014, and deterministic, scalable, transactional, distributed systems such as Calvin which is currently being commercialized by Fauna. Abadi has been a recipient of a Churchill Scholarship, a NSF CAREER Award, a Sloan Research Fellowship, a VLDB Best Paper Award, two VLDB Test of Time Awards (for the work on C-Store and HadoopDB), the 2008 SIGMOD Jim Gray Doctoral Dissertation Award, the 2013-2014 Yale Provost's Teaching Prize, and the 2013 VLDB Early Career Researcher Award. He was the PhD dissertation advisor of Alexander Thomson's and Jose Falerio's PhD dissertations, both of which won SIGMOD Jim Gray Doctoral Dissertation Awards (in 2015 and 2020 respectively). He received his PhD in 2008 from MIT.
Date & Time
Thursday, December 3, 2020 - 14:00