Section: Overall Objectives

Objective: efficient support for scalable data-intensive computing

Our research activities focus on data-intensive high-performance applications that exhibit the need to handle:

• massive data BLOBs (Binary Large OBjects), in the order of Terabytes,

• stored in a large number of nodes, thousands to tens of thousands,

• accessed under heavy concurrency by a large number of processes, thousands to tens of thousands at a time,

• with a relatively fine access grain, in the order of Megabytes.

Examples of such applications are:

• Massively parallel cloud data-mining applications (e.g., Map-Reduce-based data analysis);

• Advanced Platform-as-a-Service (PaaS) cloud data services requiring efficient data sharing under heavy concurrency;

• Advanced concurrency-optimized, versioning-oriented cloud services for virtual-machine-image storage and management at IaaS (Infrastructure-as-a-Service) level;

• Scalable storage solutions for I/O-intensive HPC simulations for post-Petascale architectures;

• Storage and I/O stacks for big-data analysis in applications that manipulate structured scientific data (e.g. very large multi-dimensional arrays).