Stronger Consistency and Semantics for Geo-Replicated Storage

Geo-replicated storage systems provide the backend for massive-scale
websites such as Google and Facebook. These storage systems seek to
provide an always-on experience where every operation completes
quickly because of a widely demonstrated link between page load times,
user engagement, and revenue. We term systems that provide an
always-on experience and can handle data at the required scale "aLPS"
systems because they provide four key properties: Availability, Low
latency, Partition tolerance, and Scalability.

Previous ALPS systems made large usability sacrifices in pursuit of
their scale and performance goals. They settled for eventually
consistent replication between datacenters and inconsistent batch
operations within them. My research shows that these sacrifices are
not fundamental.

In this talk, I will present the first ALPS system to provide
consistency that is stronger than eventual. Specifically, I will show
how to provide causal consistency for data stored in multiple
datacenters, each of which spreads the data across many servers. Then,
I will show how to strengthen the semantics of that system with a
richer data model as well as read-only and write-only transactions.
Wyatt Lloyd is a Ph.D. candidate in Computer Science at Princeton
University. His research interests include the distributed systems
and networking problems that underlie the architecture of large-scale
websites, cloud computing, and big data. He received his masters
degree in Computer Science from Princeton University, and a bachelors
degree in Computer Science from Penn State University.