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We present the design and evaluation of Rapid, a distributed membershipservice. At Rapid's core is a scheme for multi-process cut detection (CD) thatrevolves around two key insights: (i) it suspects a failure of a process onlyafter alerts arrive from multiple sources, and (ii) when a group of processesexperience problems, it detects failures of the entire group, rather thanconclude about each process individually. Implementing these insightstranslates into a simple membership algorithm with low communication overhead.  We present evidence that our strategy suffices to drive unanimous detectionalmost-everywhere, even when complex network conditions arise, such as one-wayreachability problems, firewall misconfigurations, and high packet loss.Furthermore, we present both empirical evidence and analyses that proves thatthe almost-everywhere detection happens with high probability. To complete thedesign, Rapid contains a leaderless consensus protocol that convertsmulti-process cut detections into a view-change decision. The resultingmembership service works both in fully decentralized as well as logicallycentralized modes.  We present an evaluation of Rapid in moderately scalable cloud settings.Rapid bootstraps 2000 node clusters 2-5.8x faster than prevailing tools such asMemberlist and ZooKeeper, remains stable in face of complex failure scenarios,and provides strong consistency guarantees. It is easy to integrate Rapid intoexisting distributed applications, of which we demonstrate two.

Stable and Consistent Membership at Scale with Rapid