Overview
Emerging enterprise and grid applications deploy complex, end-to-end application workflows, which connect interacting components and integrate services that are distributed in space and time, on widely distributed environments. Couplings and interactions, e.g., data or parameter exchanges, between components and services in these applications are varied, data intensive and time critical. As a result, high-throughput, low-latency data acquisition, data streaming and in-transit data manipulation become critical requirements. This project studies the problem of autonomic data streaming and addresses these requirements at three levels: (1) at the data acquisition level through support for data extraction from running applications using advanced network capabilities to minimize the overheads, as well as the impact of I/O operations on application execution, (2) at the data sharing level through a virtual shared data space that supports associative accesses from different components/services and flexible data querying and data processing (e.g., reduction, min, max, data redistribution, range querying, etc.), and (3) at the data transport level using efficient data streaming over wide-area networks with in-transit data processing, to satisfy strict end-to-end data coupling constraints.
News
November, 2010:
The Data Management webpage is online!
