TomusBlobs: scalable data-intensive processing on Azure clouds

Scalable, Energy-Efficient Data Centers and Clouds

On the Varieties of Clouds for Data Intensive Computing

By a cloud we mean an infrastructure that provides resources or services over a network, often the Internet, usually at the scale and with the reliability of a data center. We distinguish between clouds that provide on-demand computing instances (such as Amazon’s EC2 service) and clouds that provide on-demand computing capacity (such as provided by Hadoop). We give a quick overview of clouds an...

Scheduling Data Intensive Workloads through Virtualization on MapReduce based Clouds

MapReduce has become a popular programming model for running data intensive applications on the cloud. Completion time goals or deadlines of MapReduce jobs set by users are becoming crucial in existing cloudbased data processing environments like Hadoop. There is a conflict between the scheduling MR jobs to meet deadlines and “data locality” (assigning tasks to nodes that contain their input da...

Scalable, Secure Analysis of Social Sciences Data on the Azure Platform

ion and execution model that fits the communication, computation and reliability features of Clouds platforms and offer innovative problems for further research.

On Task Assignment in Data Intensive Scalable Computing

MapReduce and other Data-Intensive Scalable Computing paradigms have emerged as the most popular solution for processing massive data sets, a crucial task in surviving the “Data Deluge”. Recent works have shown that maintaining data locality is paramount to achieve high performance in such paradigms. To this end, suitable task assignment algorithms are needed. Current solutions use round-robin ...