Akshay Shah on "Epidemic algorithms in replicated databases"
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Akshay Shah on "Epidemic algorithms in replicated databases"
ABSTRACT
Abstract 1 Introduction We present a family of epidemic algorithms for maintaining replicated data in a transactional framework. The algorithms are based on the causal delivery of log records where each record corresponds to one transaction instead of one operation. The fist algorithm in this family is a pessimistic protocol that ensures serializability and guarantees strict executions. Since we expect the epidemic algorithms to be used in environments with low probability of conflicts among transactions, we develop a variant of the pessimistic algorithm in which locks are released as soon as transactions finish their execution locally. However, this optimistic releasing of locks introduces the possibility of cascading aborts while ensuring serializable executions. The last member of thii family of epidemic algorithms is motivated from the need for asynchronous replication solutions that are being increasingly used in commercial systems. The protocol is optimistic in that transactions commit as soon as they terminate locally and inconsistencies are detected asynchronously as the effects of committed transactions propagate through the system.
https://dl.acm.org/doi/pdf/10.1145/263661.263680
AI summary
By Meetup
A research talk for researchers on epidemic algorithms for replicated databases, comparing pessimistic, optimistic lock-release, and async replication.
AI summary
By Meetup
A research talk for researchers on epidemic algorithms for replicated databases, comparing pessimistic, optimistic lock-release, and async replication.