Papers
Topics
Authors
Recent
Search
2000 character limit reached

ViSearch: Weak Consistency Measurement for Replicated Data Types

Published 6 May 2022 in cs.DC | (2205.03160v2)

Abstract: Large-scale replicated data type stores often resort to eventual consistency to guarantee low latency and high availability. It is widely accepted that programming over eventually consistent data stores is challenging, since arbitrary divergence among replicas is allowed. Moreover, pragmatic protocols actually achieve consistency guarantees stronger than eventual consistency, which can be and need to be utilized to facilitate the reasoning of and programming over replicated data types. Toward the challenges above, we propose the ViSearch framework for precise measurement of eventual consistency semantics. ViSearch employs the visibility-arbitration specification methodology in concurrent programming, which extends the linearizability-based specification methodology with a dynamic visibility relation among operations, in addition to the standard dynamic happen-before and linearization relations. The consistency measurement using ViSearch is NP-hard in general. To enable practical and efficient consistency measurement in replicated data type stores, the ViSearch framework refactors the existing brute-force checking algorithm to a generic algorithm skeleton, which further enables efficient pruning of the search space and effective parallelization. We employ the ViSearch framework for consistency measurement in two replicated data type stores Riak and CRDT-Redis. The experimental evaluation shows the usefulness and cost-effectiveness of consistency measurement based on the ViSearch framework in realistic scenarios.

Citations (1)

Summary

No one has generated a summary of this paper yet.

Paper to Video (Beta)

No one has generated a video about this paper yet.

Whiteboard

No one has generated a whiteboard explanation for this paper yet.

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Continue Learning

We haven't generated follow-up questions for this paper yet.

Collections

Sign up for free to add this paper to one or more collections.