Language Support for Fault-Tolerant Parallel and Distributed Programming

2007 ◽  
pp. 55-78
Author(s):  
Richard D. Schlichting ◽  
David E. Bakken ◽  
Vicraj T. Thomas
Keyword(s):  
Fault Tolerant ◽  
Distributed Programming ◽  
Language Support

scholarly journals A multiparty session typing discipline for fault-tolerant event-driven distributed programming

2021 ◽  
Vol 5 (OOPSLA) ◽  
pp. 1-30
Author(s):  
Malte Viering ◽  
Raymond Hu ◽  
Patrick Eugster ◽  
Lukasz Ziarek
Keyword(s):  
Fault Tolerant ◽  
Failure Detection ◽  
Third Party ◽  
Distributed Programming ◽  
Failure Handling ◽  
Session Types ◽  
Dynamic Replacement ◽  
Event Driven ◽  
Industrial Strength ◽  
Novel Model

This paper presents a formulation of multiparty session types (MPSTs) for practical fault-tolerant distributed programming. We tackle the challenges faced by session types in the context of distributed systems involving asynchronous and concurrent partial failures – such as supporting dynamic replacement of failed parties and retrying failed protocol segments in an ongoing multiparty session – in the presence of unreliable failure detection. Key to our approach is that we develop a novel model of event-driven concurrency for multiparty sessions. Inspired by real-world practices, it enables us to unify the session-typed handling of regular I/O events with failure handling and the combination of features needed to express practical fault-tolerant protocols. Moreover, the characteristics of our model allow us to prove a global progress property for well-typed processes engaged in multiple concurrent sessions, which does not hold in traditional MPST systems. To demonstrate its practicality, we implement our framework as a toolchain and runtime for Scala, and use it to specify and implement a session-typed version of the cluster management system of the industrial-strength Apache Spark data analytics framework. Our session-typed cluster manager composes with other vanilla Spark components to give a functioning Spark runtime; e.g., it can execute existing third-party Spark applications without code modification. A performance evaluation using the TPC-H benchmark shows our prototype implementation incurs an average overhead below 10%.

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