Data Consistency in Distributed Systems: Algorithms, Programs, and Databases

Abstract : For decades distributed computing has been mainly an academic subject. Today, it has become mainstream: our connected world demands applications that are inherently distributed, and the usage of shared, distributed, peer-to-peer or cloud-computing infrastructures are increasingly common. However, writing distributed applications that are both correct and well distributed (e.g., highly available) is extremely challenging. In fact, there exists a fundamental trade-off between data consistency, availability, and the ability to tolerate failures. This trade-off has implications on the design of the entire distributed computing infrastructure, including storage systems, compilers and runtimes, application development frameworks and programming languages. Unfortunately, this also has significant implications on the programming model exposed to the designers and developers of applications. We need to enable programmers who are not experts in these subtle aspects to build distributed applications that remain correct in the presence of concurrency, failures, churn, replication, dynamically-changing and partial information, high load, absence of a single line of time, etc. This Dagstuhl Seminar proposes to bring together researchers and practitioners in the areas of distributed systems, programming languages, verifications, and databases. We would like to understand the lessons learnt in building scalable and correct distributed systems, the design patterns that have emerged, and explore opportunities for distilling these into programming methodologies, programming tools, and languages to make distributed computing easier and more accessible. Main issues in discussion: Application writers are constantly making trade-offs between consistency and availability. What kinds of tools and methodologies can we provide to simplify this decision making? How does one understand the implications of a design choice? Available systems are hard to design, test and debug. Do existing testing and debugging tools suffice for identifying and isolating bugs due to weak consistency? How can these problems be identified in production using live monitoring? Can we formalize commonly desired (generic) correctness (or performance) properties? How can we teach programmers about these formalisms and make them accessible to a wide audience? Can we build verification or testing tools to check that systems have these desired correctness properties? How do applications achieve the required properties, while ensuring adequate performance, in practice? What design patterns and idioms work well? To what degree can these properties be guaranteed by the platform (programming language, libraries, and runtime system)? What are the responsibilities of the application developer, and what tools and information does she have?
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Annette Bieniusa, Alexey Gotsman, Bettina Kemme, Marc Shapiro. Data Consistency in Distributed Systems: Algorithms, Programs, and Databases. Dagstuhl Reports, Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik, 2018, Dagstuhl Reports, 8 (2), pp.101-121. ⟨http://drops.dagstuhl.de/opus/volltexte/2018/9292/⟩. ⟨10.4230/DagRep.8.2.101⟩. ⟨hal-01848384⟩

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