Epidemic algorithms for replicated databases

JA Holliday, R Steinke, D Agrawal… - IEEE Transactions on …, 2003 - ieeexplore.ieee.org
We present a family of epidemic algorithms for maintaining replicated database systems.
The algorithms are based on the causal delivery of log records where each record …

The IceCube approach to the reconciliation of divergent replicas

AM Kermarrec, A Rowstron, M Shapiro… - Proceedings of the …, 2001 - dl.acm.org
We describe a novel approach to log-based reconciliation called IceCube. It is general and
is parameterised by application and object semantics. IceCube considers more flexible …

Saturn: A distributed metadata service for causal consistency

M Bravo, L Rodrigues, P Van Roy - Proceedings of the Twelfth …, 2017 - dl.acm.org
This paper presents the design, implementation, and evaluation of Saturn, a metadata
service for geo-replicated systems. Saturn can be used in combination with several …

Chapar: certified causally consistent distributed key-value stores

M Lesani, CJ Bell, A Chlipala - ACM SIGPLAN Notices, 2016 - dl.acm.org
Today's Internet services are often expected to stay available and render high
responsiveness even in the face of site crashes and network partitions. Theoretical results …

Write fast, read in the past: Causal consistency for client-side applications

M Zawirski, N Preguiça, S Duarte, A Bieniusa… - Proceedings of the 16th …, 2015 - dl.acm.org
Client-side apps (eg, mobile or in-browser) need cloud data to be available in a local cache,
for both reads and updates. For optimal user experience and developer support, the cache …

Cobra: Making Transactional {Key-Value} Stores Verifiably Serializable

C Tan, C Zhao, S Mu, M Walfish - 14th USENIX Symposium on …, 2020 - usenix.org
Today's cloud databases offer strong properties, including serializability, sometimes called
the gold standard database correctness property. But cloud databases are complicated …

Lineage-driven fault injection

P Alvaro, J Rosen, JM Hellerstein - Proceedings of the 2015 ACM …, 2015 - dl.acm.org
In large-scale data management systems, failure is practically a certainty. Fault-tolerant
protocols and components are notoriously difficult to implement and debug. Worse still …

Implementing linearizability at large scale and low latency

C Lee, SJ Park, A Kejriwal, S Matsushita… - Proceedings of the 25th …, 2015 - dl.acm.org
Linearizability is the strongest form of consistency for concurrent systems, but most large-
scale storage systems settle for weaker forms of consistency. RIFL provides a general …

XMIDDLE: A data-sharing middleware for mobile computing

C Mascolo, L Capra, S Zachariadis… - Wireless Personal …, 2002 - Springer
An increasing number of distributed applications will be written for mobilehosts, such as
laptop computers, third generation mobile phones, personaldigital assistants, watches and …

[PDF][PDF] Object storage on CRAQ: High-throughput chain replication for read-mostly workloads

J Terrace, MJ Freedman - USENIX Annual Technical Conference, 2009 - usenix.org
Massive storage systems typically replicate and partition data over many potentially-faulty
components to provide both reliability and scalability. Yet many commerciallydeployed …