Maintaining consistency in a replicated software archive

Charlton, Colin; Leng, Paul; Lloyd, R. K.; Turnbull, Rob

doi:10.1002/(sici)1097-024x(199803)28:3<285::aid-spe153>3.3.co;2-t

Cited by 2 publications

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“…In Phase 2 (Tasks 4-6), the leader will choose a value (val c ) based on the messages received from acceptors; then it commits its proposal for this value with its unique identifier (vid c ) and cid as Phase 2A message to all acceptors (lines [24][25][26][27][28][29][30][31][32][33][34]. Different from original Paxos that requires the proposer to receive ACKs from a majority of acceptors, CC-Paxos allows to start Phase 2 after it receives a particular d (which can be any value between 1 and OEn=2 C 1) ACKs from acceptors.…”

Section: Normal Operationmentioning

confidence: 99%

“…Data consistency is a prominent problem for replication systems and has been receiving a lot of attention for many years. Early replication systems pursue strong consistency through ad hoc methods or widely‐used protocols. In recent years, a lot of research efforts have turned to understand the associated factors that affect consistency and varying consistency requirements in practical applications.…”

Section: Related Workmentioning

confidence: 99%

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Adaptive trade‐off between consistency and performance in data replication

et al. 2016

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Summary Replication is widely adopted in modern Internet applications and distributed systems to improve the reliability and performance. Though maintaining the strong consistency among replicas can guarantee the correctness of application behaviors, however, it will affect the application performance at the same time because there is a well‐known trade‐off between consistency and performance. Many real‐world applications favoring performance often choose to enforce weak consistency. Although there has been some work on flexible configuration of consistency, most focuses on design or deployment time. As the system settings constantly change during runtime, the tuning of the consistency‐performance trade‐off needs to be handled dynamically. Failing to do that will cause either underestimation or overestimation of the consistency and performance that can be achieved. Existing work does not well support the dynamic tuning of the aforementioned trade‐off in runtime, which is mainly because of the lack of an appropriate quantitative model of consistency and performance. In this work, based on our previous effort on the quantitative model of consistency and latency, we design a replication protocol, CC‐Paxos, to achieve an adaptive trade‐off between consistency and performance according to application preferences and runtime information. By design, CC‐Paxos is not bound to any specific underlying data stores. We have implemented CC‐Paxos and applied it to MySQL databases. And real experiments both within a data center and across data centers show that CC‐Paxos not only can dynamically adjust the delivered consistency in return for ensured performance but also outperforms MySQL Cluster in the case of strong consistency guarantee. Copyright © 2016 John Wiley & Sons, Ltd.

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