We are using Haskell to develop an ultra-scalable high-availability resource management system for big clusters (millions of nodes). A key element of the design is to provide scalable and reliable mechanisms for communicating failures and coordinating recovery transitions. In this talk, we’ll describe the design of the project and some challenges we’ve faced along the way. For distributed concurrency, we use the actor model provided by Cloud Haskell, which provides good network semantics and remote process management, the importance of both of which we will explain. The challenges of using Cloud Haskell include dealing with scalability issues and providing backend transports for multiple kinds of network hardware. A replicated state machine preserves the state arising from failures and transitions and is at the center of our design. It uses the Paxos algorithm, and we used formal verification in the design (and debugging) of this algorithm, beautifully combining Promela and Haskell.
is a development lead as Parallel Scientific. He has an extensive background with managing teams and developing software of many types, including web sites, digital telephony, embedded systems, and distributed systems. He has a graduate degree from Cambridge University, where he studied under Simon Peyton Jones.