Compiling Distributed System Models with PGo

Hackett, Finn; Hosseini, Seyed Hadi; Costa, Renato Lopes Da; Do, Matthew; Beschastnikh, Ivan

doi:10.1145/3575693.3575695

Cited by 7 publications

(2 citation statements)

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“…Due to such a restrictive definition about consistency, it is not generic and cannot be applied to find data consistency violations. More recently, researchers have developed a formal verification methodology and tool that can generate an implementation along with an abstract model for model checking [16]. The technique allows developers to specify their distributed systems implementation as an abstract model using formal language.…”

Section: Distributed Systems Model Checkingmentioning

confidence: 99%

“…However, fuzzing cannot systematically explore the state space of SUTs; therefore, it may miss some bugs. Although advancement of formal methods may be used to develop formally verified bug-free distributed systems, it still has several limits, such that it takes a lot of time and effort for non-researchers, cannot be directly applied to existing implementations, and a formally verified design does not always imply a correct implementation [14][15][16]. On the other hand, implementation-level model checking can systematically enumerate the state space of SUTs [17][18][19][20][21][22][23][24][25][26] unlike fuzzing and can directly verify the implementation of distributed systems without the limitations of formal method approaches.…”

Section: Introductionmentioning

confidence: 99%

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VConMC: Enabling Consistency Verification for Distributed Systems Using Implementation-Level Model Checkers and Consistency Oracles

Kim

2024

Electronics

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Many cloud services are relying on distributed key-value stores such as ZooKeeper, Cassandra, HBase, etc. However, distributed key-value stores are notoriously difficult to design and implement without any mistakes. Because data consistency is the contract for clients that defines what the correct values to read are for a given history of operations under a specific consistency model, consistency violations can confuse client applications by showing invalid values. As a result, serious consequences such as data loss, data corruption, and unexpected behavior of client applications can occur. Software bugs are one of main reasons why consistency violations may occur. Formal verification techniques may be used to make designs correct and minimize the risks of having bugs in the implementation. However, formal verification is not a panacea due to limitations such as the cost of verification, inability to verify existing implementations, and human errors involved. Implementation-level model checking has been heavily explored by researchers for the past decades to formally verify whether the underlying implementation of distributed systems have bugs or not. Nevertheless, previous proposals are limited because their invariant checking is not versatile enough to check for the wide spectrum of consistency models, from eventual consistency to strong consistency. In this work, consistency oracles are employed for consistency invariant checking that can be used by implementation-level model checkers to formally verify data consistency model implementations of distributed key-value stores. To integrate consistency oracles with implementation-level distributed system model checkers, the partial-order information obtained via API is leveraged to avoid the exhaustive search during consistency invariant checking. Our evaluation results show that, by using the proposed method for consistency invariant checking, our prototype model checker, VConMC, can detect consistency violations caused by several real-world software bugs in a well-known distributed key-value store, ZooKeeper.

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Section: Distributed Systems Model Checkingmentioning

confidence: 99%