Algorithmes de classification répartis sur le cloud

The subjects addressed in this thesis are inspired from research problems faced by the Lokad company. These problems are related to the challenge of designing efficient parallelization techniques of clustering algorithms on a Cloud Computing platform. Chapter 2 provides an introduction to the Cloud Computing technologies, especially the ones devoted to intensive computations. Chapter 3 details more specifically Microsoft Cloud Computing offer : Windows Azure. The following chapter details technical aspects of cloud application development and provides some cloud design patterns. Chapter 5 is dedicated to the parallelization of a well-known clustering algorithm: the Batch K-Means. It provides insights on the challenges of a cloud implementation of distributed Batch K-Means, especially the impact of communication costs on the implementation efficiency. Chapters 6 and 7 are devoted to the parallelization of another clustering algorithm, the Vector Quantization (VQ). Chapter 6 provides an analysis of different parallelization schemes of VQ and presents the various speedups to convergence provided by them. Chapter 7 provides a cloud implementation of these schemes. It highlights that it is the online nature of the VQ technique that enables an asynchronous cloud implementation, which drastically reduces the communication costs introduced in Chapter 5.