Detection of Conjunctive Stable Predicates in Dynamic Systems

Wang, Xinli; Mayo, Jean; Hembroff, Guy; Gao, Chunming

doi:10.1109/icpads.2009.66

Cited by 6 publications

(5 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, the processes are static. [42,43] address the predicate detection in dynamic distributed systems. However, the class of predicate is limited to the conjunctive predicate.…”

Section: Predicate Detection In Distributed Systemsmentioning

confidence: 99%

Using weaker consistency models with monitoring and recovery for improving performance of key-value stores

et al. 2019

View full text Add to dashboard Cite

Consistency properties provided by most key-value stores can be classified into sequential consistency and eventual consistency. The former is easier to program with but suffers from lower performance whereas the latter suffers from potential anomalies while providing higher performance. We focus on the problem of what a designer should do if he/she has an algorithm that works correctly with sequential consistency but is faced with an underlying key-value store that provides a weaker (e.g., eventual or causal) consistency. We propose a detect-rollback based approach: The designer identifies a correctness predicate, say P , and continues to run the protocol, as our system monitors P . If P is violated (because the underlying key-value store provides a weaker consistency), the system rolls back and resumes the computation at a state where P holds.We evaluate this approach with graph-based applications running on the Voldemort key-value store. Our experiments with deployment on Amazon AWS EC2 instances shows that using eventual consistency with monitoring can provide a 50% -80% increase in throughput when compared with sequential consistency. We also observe that the overhead of the monitoring itself was low (typically less than 4%) and the latency of detecting violations was small. In particular, in a scenario designed to intentionally cause a large number of violations, more than 99.9% of violations were detected in less than 50 milliseconds in regional networks (all clients and servers in the same Amazon AWS region), and in less than 3 seconds in global networks.We find that for some applications, frequent rollback can cause the program using eventual consistency to effectively stall. We propose alternate mechanisms for dealing with re-occurring rollbacks. Overall, for applications considered in this paper, we find that even with rollback, eventual consistency provides better performance than using sequential consistency.

show abstract

“…Furthermore, the processes are static. [42,43] address the predicate detection in dynamic distributed systems. However, the class of predicate is limited to the conjunctive predicate.…”

Section: Predicate Detection In Distributed Systemsmentioning

confidence: 99%

Using weaker consistency models with monitoring and recovery for improving performance of key-value stores

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Furthermore, the processes are static. [38], [39] address the predicate detection in dynamic distributed systems. However, the classes of predicate is limited to conjunctive predicate.…”

Section: A Predicate Detection In Distributed Systemsmentioning

confidence: 99%

Technical Report: Optimistic Execution in Key-Value Store

Nguyen,

Charapko,

Kulkarni

et al. 2018

Preprint

View full text Add to dashboard Cite

Limitations of the CAP theorem imply that if availability is desired in the presence of network partitions, one must sacrifice sequential consistency, a consistency model that is more natural for system design. We focus on the problem of what a designer should do if he/she has an algorithm that works correctly with sequential consistency but is faced with an underlying key-value store that provides a weaker (e.g., eventual or causal) consistency. We propose a detect-rollback based approach: The designer identifies a correctness predicate, say P , and continues to run the protocol, as our system monitors P . If P is violated (because the underlying key-value store provides a weaker consistency), the system rolls back and resumes the computation at a state where P holds.We evaluate this approach with practical graph applications running on the Voldemort key-value store. Our experiments with deployment on Amazon AWS EC2 instances shows that using eventual consistency with monitoring can provide a 50 − 80% increase in throughput when compared with sequential consistency. We also show that the overhead of the monitoring itself is low (typically less than 4%) and the latency of detecting violations is small. In particular, more than 99.9% of violations are detected in less than 50 milliseconds in regional AWS networks, and in less than 5 seconds in global AWS networks.

show abstract

“…Furthermore, the processes are static. [32], [33] address the predicate detection in dynamic distributed systems. However, the classes of predicate is limited to conjunctive predicate.…”

Section: A Predicate Detection In Distributed Systemsmentioning

confidence: 99%

Optimistic Execution in Key-Value Store

Nguyen¹,

Charapko²,

Kulkarni³

et al. 2018

Preprint

View full text Add to dashboard Cite

Limitations of CAP theorem imply that if availability is desired in the presence of network partitions, one must sacrifice sequential consistency, a consistency model that is more natural for system design. We focus on the problem of what a designer should do if she has an algorithm that works correctly with sequential consistency but is faced with an underlying key-value store that provides a weaker (e.g., eventual or causal) consistency. We propose a detect-rollback based approach: The designer identifies a correctness predicate, say P , and continue to run the protocol, as our system monitors P . If P is violated (because the underlying key-value store provides a weaker consistency), the system rolls back and resumes the computation at a state where P holds.We evaluate this approach in the Voldemort key-value store. Our experiments with deployment of Voldemort on Amazon AWS shows that using eventual consistency with monitoring can provide 20 − 40% increase in throughput when compared with sequential consistency. We also show that the overhead of the monitor itself is small (typically less than 8%) and the latency of detecting violations is very low. For example, more than 99.9% violations are detected in less than 1 second.

show abstract

Detection of Conjunctive Stable Predicates in Dynamic Systems

Cited by 6 publications

References 49 publications

Using weaker consistency models with monitoring and recovery for improving performance of key-value stores

Using weaker consistency models with monitoring and recovery for improving performance of key-value stores

Technical Report: Optimistic Execution in Key-Value Store

Optimistic Execution in Key-Value Store

Contact Info

Product

Resources

About