Geo-replicated storage with scalable deferred update replication

Many current online services are deployed over geographically distributed sites (i.e., datacenters). Such distributed services call for geo-replicated storage, that is, storage distributed and replicated among many sites. Geographical distribution and replication can improve locality and availability of a service. Locality is achieved by moving data closer to the users. High availability is attained by replicating data in multiple servers and sites. This paper considers a class of scalable replicated storage systems based on deferred update replication with transactional properties. The paper discusses different ways to deploy scalable deferred update replication in geographically distributed systems, considers the implications of these deployments on user-perceived latency, and proposes solutions. Our results are substantiated by a series of microbenchmarks and a social network application.

[1]  Ricardo Jiménez-Peris,et al.  Middleware based data replication providing snapshot isolation , 2005, SIGMOD '05.

[2]  Marcos K. Aguilera,et al.  Transactional storage for geo-replicated systems , 2011, SOSP.

[3]  Fernando Pedone,et al.  Scalable deferred update replication , 2012, IEEE/IFIP International Conference on Dependable Systems and Networks (DSN 2012).

[4]  Fernando Pedone,et al.  P-Store: Genuine Partial Replication in Wide Area Networks , 2010, 2010 29th IEEE Symposium on Reliable Distributed Systems.

[5]  Leslie Lamport,et al.  The part-time parliament , 1998, TOCS.

[6]  Marcos K. Aguilera,et al.  Surviving Congestion in Geo-Distributed Storage Systems , 2012, USENIX Annual Technical Conference.

[7]  Hans-Arno Jacobsen,et al.  PNUTS: Yahoo!'s hosted data serving platform , 2008, Proc. VLDB Endow..

[8]  Tim Kraska,et al.  MDCC: multi-data center consistency , 2012, EuroSys '13.

[9]  Gustavo Alonso,et al.  Exploiting atomic broadcast in replicated databases , 1997 .

[10]  Marcos K. Aguilera,et al.  Sinfonia: a new paradigm for building scalable distributed systems , 2007, SOSP.

[11]  Jun Rao,et al.  Using Paxos to Build a Scalable, Consistent, and Highly Available Datastore , 2011, Proc. VLDB Endow..

[12]  Wilson C. Hsieh,et al.  Bigtable: A Distributed Storage System for Structured Data , 2006, TOCS.

[13]  Werner Vogels,et al.  Dynamo: amazon's highly available key-value store , 2007, SOSP.

[14]  J. T. Robinson,et al.  On optimistic methods for concurrency control , 1979, TODS.

[15]  Rachid Guerraoui,et al.  The Database State Machine Approach , 2003, Distributed and Parallel Databases.

[16]  Gustavo Alonso,et al.  MIDDLE-R: Consistent database replication at the middleware level , 2005, TOCS.

[17]  Gustavo Alonso,et al.  Don't Be Lazy, Be Consistent: Postgres-R, A New Way to Implement Database Replication , 2000, VLDB.

[18]  Michael J. Freedman,et al.  Don't settle for eventual: scalable causal consistency for wide-area storage with COPS , 2011, SOSP.