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A partial approach to model checking
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Impossibility of distributed consensus with one faulty process
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Distributed snapshots: determining global states of distributed systems
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Chord: a scalable peer-to-peer lookup protocol for internet applications
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Boolean and Cartesian Abstraction for Model Checking C Programs
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CMC: a pragmatic approach to model checking real code
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SplitStream: high-bandwidth multicast in cooperative environments
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Automatic detection and repair of errors in data structures
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Dynamic partial-order reduction for model checking software
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OpenDHT: a public DHT service and its uses
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Vigilante: end-to-end containment of internet worms
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Speculative execution in a distributed file system
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Rx: treating bugs as allergies---a safe method to survive software failures
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Using model checking to find serious file system errors
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Using queries for distributed monitoring and forensics
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We propose a new approach for developing and deploying distributed systems, in which nodes predict distributed consequences of their actions, and use this information to detect and avoid errors. Each node continuously runs a state exploration algorithm on a recent consistent snapshot of its neighborhood and predicts possible future violations of specified safety properties. We describe a new state exploration algorithm, consequence prediction, which explores causally related chains of events that lead to property violation. This paper describes the design and implementation of this approach, termed CrystalBall. We evaluate CrystalBall on RandTree, BulletPrime, Paxos, and Chord distributed system implementations. We identified new bugs in mature Mace implementations of three systems. Furthermore, we show that if the bug is not corrected during system development, CrystalBall is effective in steering the execution away from inconsistent states at runtime.