Scientific Workflow Applications on Amazon EC2

The proliferation of commercial cloud computing providers has generated significant interest in the scientific computing community. Much recent research has attempted to determine the benefits and drawbacks of cloud computing for scientific applications. Although clouds have many attractive features, such as virtualization, on-demand provisioning, and "pay as you go" usage-based pricing, it is not clear whether they are able to deliver the performance required for scientific applications at a reasonable price. In this paper we examine the performance and cost of clouds from the perspective of scientific workflow applications. We use three characteristic workflows to compare the performance of a commercial cloud with that of a typical HPC system, and we analyze the various costs associated with running those workflows in the cloud. We find that the performance of clouds is not unreasonable given the hardware resources provided, and that performance comparable to HPC systems can be achieved given similar resources. We also find that the cost of running workflows on a commercial cloud can be reduced by storing data in the cloud rather than transferring it from outside

Performance Evaluation of Virtualization with Cloud Computing

Cloud computing has been the subject of many researches. Researches shows that cloud computing permit to reduce hardware cost, reduce the energy consumption and allow a more efficient use of servers. Nowadays lot of servers are used inefficiently because they are underutilized. The uses of cloud computing associate to virtualization have been a solution to the underutilisation of those servers. However the virtualization performances with cloud computing cannot offers performances equal to the native performances. The aim of this project was to study the performances of the virtualization with cloud computing. To be able to meet this aim it has been review at first the previous researches on this area. It has been outline the different types of cloud toolkit as well as the different ways available to virtualize machines. In addition to that it has been examined open source solutions available to implement a private cloud. The findings of the literature review have been used to realize the design of the different experiments and also in the choice the tools used to implement a private cloud. In the design and the implementation it has been setup experiment to evaluate the performances of public and private cloud.The results obtains through those experiments have outline the performances of public cloud and shows that the virtualization of Linux gives better performances than the virtualization of Windows. This is explained by the fact that Linux is using paravitualization while Windows is using HVM. The evaluation of performances on the private cloud has permitted the comparison of native performance with paravirtualization and HVM. It has been seen that paravirtualization hasperformances really close to the native performances contrary to HVM. Finally it hasbeen presented the cost of the different solutions and their advantages