Linearizability: a correctness condition for concurrent objects
ACM Transactions on Programming Languages and Systems (TOPLAS)
Managing update conflicts in Bayou, a weakly connected replicated storage system
SOSP '95 Proceedings of the fifteenth ACM symposium on Operating systems principles
SIAM Journal on Computing
The serializability of concurrent database updates
Journal of the ACM (JACM)
Towards robust distributed systems (abstract)
Proceedings of the nineteenth annual ACM symposium on Principles of distributed computing
The costs and limits of availability for replicated services
SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
Design and evaluation of a conit-based continuous consistency model for replicated services
ACM Transactions on Computer Systems (TOCS)
Trading Replication Consistency for Performance and Availability: an Adaptive Approach
ICDCS '03 Proceedings of the 23rd International Conference on Distributed Computing Systems
Queue - Scalable Web Services
Benchmarking cloud serving systems with YCSB
Proceedings of the 1st ACM symposium on Cloud computing
Future Generation Computer Systems
ZooKeeper: wait-free coordination for internet-scale systems
USENIXATC'10 Proceedings of the 2010 USENIX conference on USENIX annual technical conference
Analyzing consistency properties for fun and profit
Proceedings of the 30th annual ACM SIGACT-SIGOPS symposium on Principles of distributed computing
YCSB++: benchmarking and performance debugging advanced features in scalable table stores
Proceedings of the 2nd ACM Symposium on Cloud Computing
Don't settle for eventual: scalable causal consistency for wide-area storage with COPS
SOSP '11 Proceedings of the Twenty-Third ACM Symposium on Operating Systems Principles
Conflict-free replicated data types
SSS'11 Proceedings of the 13th international conference on Stabilization, safety, and security of distributed systems
Eventual consistency: How soon is eventual? An evaluation of Amazon S3's consistency behavior
Proceedings of the 6th Workshop on Middleware for Service Oriented Computing
On the availability of non-strict quorum systems
DISC'05 Proceedings of the 19th international conference on Distributed Computing
Probabilistically bounded staleness for practical partial quorums
Proceedings of the VLDB Endowment
An adaptive quality of service aware middleware for replicated services
IEEE Transactions on Parallel and Distributed Systems
Eventual consistency today: limitations, extensions, and beyond
Communications of the ACM
Eventual Consistency Today: Limitations, Extensions, and Beyond
Queue - Storage
PBS at work: advancing data management with consistency metrics
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Archiving the relaxed consistency web
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Client-centric benchmarking of eventual consistency for cloud storage systems
Proceedings of the 4th annual Symposium on Cloud Computing
Eventually consistent: not what you were expecting?
Communications of the ACM
Eventually Consistent: Not What You Were Expecting?
Queue - Performance
| Hi-index | 0.03 |
Large-scale key-value storage systems sacrifice consistency in the interest of dependability (i.e., partition-tolerance and availability), as well as performance (i.e., latency). Such systems provide eventual consistency, which--to this point--has been difficult to quantify in real systems. Given the many implementations and deployments of eventually-consistent systems (e.g., NoSQL systems), attempts have been made to measure this consistency empirically, but they suffer from important drawbacks. For example, state-of-the art consistency benchmarks exercise the system only in restricted ways and disrupt the workload, which limits their accuracy. In this paper, we take the position that a consistency benchmark should paint a comprehensive picture of the relationship between the storage system under consideration, the workload, the pattern of failures, and the consistency observed by clients. To illustrate our point, we first survey prior efforts to quantify eventual consistency. We then present a benchmarking technique that overcomes the shortcomings of existing techniques to measure the consistency observed by clients as they execute the workload under consideration. This method is versatile and minimally disruptive to the system under test. As a proof of concept, we demonstrate this tool on Cassandra.