MapReduce distributed data processing architecture has become the de-facto data-intensive analysis mechanism in compute clouds and in commodity clusters, mainly due to its excellent fault tolerance features, scalability, ease of use and the simpler programming model. MapReduceRoles for Azure (MR4Azure) is a decentralized, dynamically scalable MapReduce runtime we developed for Windows Azure Clo...

Windows Azure Storage (WAS) is a cloud storage system that provides customers the ability to store seemingly limitless amounts of data for any duration of time. WAS customers have access to their data from anywhere, at any time, and only pay for what they use and store. To provide durability for that data and to keep the cost of storage low, WAS uses erasure coding. In this paper we introduce a...

Cloud computing technology is recently gaining widespread popularity among business owners and consumers/users for hosting and delivering services over the Internet. This technology offers users on-demand access to shared resources, services, and applications with the Internet access by eliminating the need for tedious installation procedures. Security and privacy issues in cloud computing is o...

Recent advances in data intensive computing for science discovery are fueling a dramatic growth in use of data-intensive iterative computations. The utility computing model introduced by cloud computing combined with the rich set of cloud infrastructure and storage services offers a very attractive environment for scientists to perform data analytics. The challenges to large-scale distributed c...

High Performance Computing (HPC) applications are scientific applications that require significant CPU capabilities. They are also data-intensive applications requiring large data storage. While many researchers have examined the performance of Amazon’s EC2 platform across some HPC benchmarks, an extensive study and their comparison between Amazon’s EC2 and Microsoft’s Windows Azure is largely ...

This paper studies how parallel machine learning algorithms can be implemented on top of Microsoft Windows Azure cloud computing platform. More specifically, we design efficient storage based communication mechanisms that lead to a scalable implementation of the K-means.

Recent advances in data intensive computing for science discovery are fueling a dramatic growth in the use of dataintensive iterative computations. The utility computing model introduced by cloud computing, combined with the rich set of cloud infrastructure and storage services, offers a very attractive environment in which scientists can perform data analytics. The challenges to large-scale di...