SAR: semantic-aware replication

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Gao, Lei

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This dissertation presents a replication framework that facilitates semantic-aware data replication (SAR) in wide area networks (WANs). WAN data replication is fundamentally difficult. As a result, generic replication algorithms must make compromises among Consistency, Availability, Response time, and Partition resilience (CARP) when used in WANs. This dissertation seeks to design algorithms based on specific semantics of the shared data sets (e.g. data properties, workload characteristics, and update patterns) to achieve the optimized CARP trade-offs. Integrating a set of semantic-aware algorithms using distributed objects to form the SAR framework, we implement a practically important e-commerce application, the distributed TPC-W benchmark. Our prototype evaluations show significant improvements on system availability and response time while preserving the consistency guarantees desired by the TPC-W benchmark. The primary focus of the dissertation is on the development of the SAR framework. Within the framework, contributions include (a) exploiting application semantics using the object-oriented approach, (b) employing a hybrid method that integrates a number of novel replication algorithms to make an important class of applications work, (c) proposing a novel replication algorithm for the multi-writer/multi-reader replication scenario with a high access locality, and (d) outlining a general purpose replication library that uses semantic-aware objects for building other distributed applications in WANs.