BRRL: a recovery library for main-memory applications in the cloud

Authors:
Tuan Cao;Benjamin Sowell;Marcos Vaz Salles;Alan Demers;Johannes Gehrke
Affiliations:
Cornell University, Ithaca, NY, USA;Cornell University, Ithaca, NY, USA;Cornell University, Ithaca, NY, USA;Cornell University, Ithaca, NY, USA;Cornell University, Ithaca, NY, USA
Venue:
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Year:
2011

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Abstract

In this demonstration we present BRRL, a library for making distributed main-memory applications fault tolerant. BRRL is optimized for cloud applications with frequent points of consistency that use data-parallelism to avoid complex concurrency control mechanisms. BRRL differs from existing recovery libraries by providing a simple table abstraction and using schema information to optimize checkpointing. We will demonstrate the utility of BRRL using a distributed transaction processing system and a platform for scientific behavioral simulations.