Fri 9 Nov 2018 13:52 - 14:15 at Studio 1 - Verification Chair(s): Tony Hosking

High-level data types are often associated with semantic invariants that must be preserved by any correct implementation. While having implementations enforce strong guarantees such as linearizability or serializability can often be used to prevent invariant violations in concurrent settings, such mechanisms are impractical in geo-distributed replicated environments, the platform of choice for many scalable Web services. To achieve high-availability essential to this domain, these environments admit various forms of weak consistency that do not guarantee all replicas have a consistent view of an application’s state. Consequently, they often admit difficult-to-understand anomalous behaviors that violate a data type’s invariants, but which are extremely challenging, even for experts, to understand and debug.

In this paper, we propose a novel programming framework for replicated data types (RDTs) equipped with an automatic (bounded) verification technique that discovers and fixes weak consistency anomalies. Our approach, implemented in a tool called Q7, involves systematically exploring the state space of an application executing on top of an eventually consistent data store, under an unrestricted consistency model but with a finite concurrency bound. Q7 uncovers anomalies (i.e., invariant violations) that manifest as finite counterexamples, and automatically generates repairs for such anamolies by selectively strengthening consistency guarantees for specific operations. Using Q7, we have uncovered a range of subtle anomalies in implementations of well-known benchmarks, and have been able to apply the repairs it mandates to effectively eliminate them. Notably, these benchmarks were written adopting best practices suggested to manage distributed replicated state (e.g., they are composed of provably convergent RDTs (CRDTs), avoid mutable state, etc.). While the safety guarantees offered by our technique are constrained by the concurrency bound, we show that in practice, proving bounded safety guarantees typically generalize to the unbounded case.

Fri 9 Nov

splash-2018-OOPSLA
13:30 - 14:15: OOPSLA - Verification at Studio 1
Chair(s): Tony HoskingAustralian National University / Data61
splash-2018-OOPSLA13:30 - 13:52
Talk
Brett BostonMassachusetts Institute of Technology, Zoe GongMassachusetts Institute of Technology, Michael CarbinMassachusetts Institute of Technology
splash-2018-OOPSLA13:52 - 14:15
Talk
Gowtham KakiPurdue University, Kapil EarankyPurdue University, KC SivaramakrishnanUniversity of Cambridge, Suresh JagannathanPurdue University