Message Passing Interface (MPI) has been the predominant standardized system for writing parallel and distributed applications. However, while MPI has been the software system of choice for traditional parallel and distributed computing platforms such as large compute clusters and Grid, MPI is not the system of choice for cloud platforms. The primary reasons for this is the lack of low latency high bandwidth network capabilities of the cloud platforms and the inherent architectural differences from traditional compute clusters. Prior studies suggest that the message latency of cloud platforms could be as much as 35x slower than that of an infiniband-connected cluster [1] for popular MPI implementations. MPI-like environment on cloud platforms is desirable for a large class of applications that run for long time spans with varying computing needs, such as the modeling and analysis to predict swath of a hurricane. Such applications could benefit from cloud's resiliency and on-demand access for a robust and green solution. Interestingly, most of the cloud vendors provide APIs to access cloud resources in an efficient manner different than how an MPI implementation would avail of those resources. We have done extensive research to identify the pain-points for designing and implementing an MPIlike framework for cloud platforms. Our research has provided us with vital guidelines that we are sharing in this paper. We present the details of the key components required for such a framework along with our experience while implementing a preliminary MPI-like framework over Azure dubbed cloudMPI and evaluate its pros and cons. A large GIS application has been ported over cloudMPI to study its effectiveness and limitations. 1

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