MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Journal of Functional Programming
HaLoop: efficient iterative data processing on large clusters
Proceedings of the VLDB Endowment
The F# asynchronous programming model
PADL'11 Proceedings of the 13th international conference on Practical aspects of declarative languages
A monad for deterministic parallelism
Proceedings of the 4th ACM symposium on Haskell
Proceedings of the 4th ACM symposium on Haskell
IncMR: Incremental Data Processing Based on MapReduce
CLOUD '12 Proceedings of the 2012 IEEE Fifth International Conference on Cloud Computing
Implementing a high-level distributed-memory parallel haskell in haskell
IFL'11 Proceedings of the 23rd international conference on Implementation and Application of Functional Languages
ShmStreaming: A Shared Memory Approach for Improving Hadoop Streaming Performance
AINA '13 Proceedings of the 2013 IEEE 27th International Conference on Advanced Information Networking and Applications
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As cloud computing and big data gain prominence in today's economic landscape, the challenge of effectively articulating complex algorithms in distributed environments becomes ever more important. In this paper we describe MBrace; a novel programming model/framework for performing large scale computation in the cloud. Based on the .NET software stack, it utilizes the power of the F# programming language. MBrace introduces a declarative style for specifying and composing parallelism patterns, in what is known as cloud workflows or a cloud monad. MBrace is also a distributed execution runtime that handles orchestration of cloud workflows in the data centre.