SciCloud project studies the establishment of private clouds, migration and execution of scientific computing applications on the cloud, and adapting scientific computing problem/algorithms to frameworks amicable to the cloud like the MapReduce.

Cloud computing has become quite popular in the distributed computing community and to study the cost of science on the clouds, Scientific Computing Cloud (SciCloud) project has been initiated at the University of Tartu. Its main goal is to study the scope of establishing private clouds at universities. With these, students and researchers can efficiently use the already existing resources of university computer networks, in solving computationally intensive scientific, mathematical, and academic problems. The project targets the development of a framework, including models and methods for establishment, proper selection, state management (managing running state and data), auto scaling and interoperability of private clouds.

SciCloud has been established on our high-performance computing (HPC) clusters using the Eucalyptus technology. Current research in the domain is focused at studying the cost of migrating scientific computing applications to the cloud. SciCloud has several customized machine images with support for several scientific computing tools and simulations like Python with NumPy and SciPy, Scilab tool, MPI. Detailed analysis with several benchmark applications like matrix-vector multiplications and NASA Advanced Supercomputing parallel benchmarks (NAS PB) are performed using the setup.

Moreover, to adapt resource-intensive scientific computing applications for the cloud, the applications must be reduced to frameworks that can successfully exploit the cloud resources. Generally, cloud infrastructure is based on commodity computers, which are cost effective, however are bound to fail regularly. This causes a serious problem as, the software has to adapt to failures and the best solution is to replicate the data and computation. One such framework that is built based on this idea is the MapReduce framework, which has gained popularity as a cloud computing framework on which one can perform automatically scalable distributed applications. SciCloud project studied reducing several scientific computing problems/algorithms to MapReduce and designed a classification, based on how the algorithms are adapted to the MapReduce framework.

SciCloud Infrastructure

SciCloud is also the name of the private cloud infrastructure running on the hardware of the University of Tartu. It is divided into separate smaller clouds built using Eucalyptus/OpenStack platforms. Both Eucalyptus and OpenStack are open source cloud platforms compatible on the API level with Amazon EC2 public cloud.

To work with the SciCloud infrastructure, please meet us in person in Liivi 2 – 311.

Profiling MapReduce




  • Jakovits, Pelle; Srirama, Satish (2013). Adapting scientific applications to cloud by using distributed computing frameworks. In: Cluster, Cloud and Grid Computing (CCGrid), 2013 13th IEEE/ACM International Symposium on: Cluster, Cloud and Grid Computing (CCGrid), 2013 13th IEEE/ACM International Symposium on, Delft; 13-16 May 2013. IEEE, 2013, 164 – 167.
  • Jakovits, Pelle; Srirama, Satish (2013). Clustering on the cloud: reducing CLARA to MapReduce. In: NordiCloud ’13 Proceedings of the Second Nordic Symposium on Cloud Computing & Internet Technologies: Second Nordic Symposium on Cloud Computing & Internet Technologies, Oslo, 2-3 September 2013. ACM, 2013, 64 – 71.


  • S. N. Srirama, C. Willmore, V. Ivanistsev, P. Jakovits et al.: Desktop to Cloud Migration of Scientific Experiments, 2nd International Workshop on Cloud Computing and Scientific Applications (CCSA) @ 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid 2012), May 13-16, 2012.
  • S. N. Srirama, P. Jakovits, E. Vainikko: Adapting Scientific Computing Problems to Clouds using MapReduce, Future Generation Computer Systems Journal, 28(1):184-192, 2012. Elsevier press. DOI 10.1016/j.future.2011.05.025


  • S. N. Srirama, O. Batrashev, P. Jakovits, E. Vainikko: Scalability of Parallel Scientific Applications on the Cloud, Scientific Programming Journal, Special Issue on Science-driven Cloud Computing, 19(2-3):91-105, 2011. IOS Press. DOI 10.3233/SPR-2011-0320.
  • P. Jakovits, I. Kromonov, S. N. Srirama: Monte Carlo linear system solver using MapReduce, 4th IEEE/ACM International Conference on Utility and Cloud Computing (UCC 2011), December 5-7, 2011, pp.293-299. IEEE.
  • P. Jakovits, S. N. Srirama, E. Vainikko: MapReduce for Scientific Computing – Viability for non-embarrassingly parallel algorithms, The 14th International Parallel Computing conference (ParCo 2011), August 30-September 2, 2011. Published in: Advances in Parallel Computing book series, Volume 22: Applications, Tools and Techniques on the Road to Exascale Computing, Edited by: K. Bosschere, E. D’Hollander, G. Joubert, D. Padua, F. Peters, M. Sawyer, 2012, ISSN 0927-5452, pp. 117-124. IOS Press.
  • O. Batrashev, S. N. Srirama, E. Vainikko: Benchmarking DOUG on the Cloud, The 2011 International Conference on High Performance Computing & Simulation (HPCS 2011), July 4-8, 2011, pp. 677-685. IEEE.


  • S. N. Srirama, P. Jakovits, E. Vainikko: Adapting Scientific Computing Problems to Clouds using MapReduce, International Conference on Utility and Cloud Computing (UCC 2010) in conjunction with the International Conference on Advanced Computing (ICoAC 2010), December 14-16, 2010. (Extended in FGCS journal)
  • S. N. Srirama, O. Batrashev, E. Vainikko: SciCloud: Scientific Computing on the Cloud, 10th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid 2010), May 17-20, 2010, pp. 579. IEEE Computer Society.

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