Communications of the ACM
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Aneka: Next-Generation Enterprise Grid Platform for e-Science and e-Business Applications
E-SCIENCE '07 Proceedings of the Third IEEE International Conference on e-Science and Grid Computing
The Eucalyptus Open-Source Cloud-Computing System
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
Cost-benefit analysis of Cloud Computing versus desktop grids
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
In cloud, do MTC or HTC service providers benefit from the economies of scale?
Proceedings of the 2nd Workshop on Many-Task Computing on Grids and Supercomputers
High-Performance Cloud Computing: A View of Scientific Applications
ISPAN '09 Proceedings of the 2009 10th International Symposium on Pervasive Systems, Algorithms, and Networks
Cloud Computing and Software Services: Theory and Techniques
Cloud Computing and Software Services: Theory and Techniques
Performance Analysis of Cloud Computing Services for Many-Tasks Scientific Computing
IEEE Transactions on Parallel and Distributed Systems
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We investigate the potential of Google App Engine (GAE) for scientific computing. We designed a generic master-slave framework that enables implementation and integration of new algorithms by instantiating one interface and two abstract classes. Applications are dynamically scheduled, executed, and monitored by the framework on the Google infrastructure, including a fault tolerant resubmission mechanism upon certain failures. We describe the implementation of a parallel rank sort algorithm using our framework and analyse the speedup, the execution overheads, and the cost of executing it on the Google Cloud infrastructure. Experimental results show that important speedup can be obtained from using GAE, especially for embarrassingly parallel Monte Carlo simulations. The two main obstacles that hinder the performance of applications on GAE are the resource quotas upon free use (especially the 30 s sequential execution time limit) and the middleware overheads. Cost-wise, GAE offers opportunities for cheaper computation due to the CPU cycle-based payment granularity, as opposed to the hourly billing intervals of other providers such as Amazon EC2.