SOFTWARE:"Cluster-based Runtime Support for Data-Intensive Online Applications"

University Of California-Santa Barbara, Santa Barbara CA

Investigators

Abstract

Data-intensive applications such as data mining demand significant compute and I/O resources. With the ubiquity of World Wide Web and the advances in clustering technologies, it becomes a reachable goal to bring these data-intensive applications to a large number of users in real-time. Online applications such as Internet services differ from their offline counterpart in the performance sensitivity to highly concurrent workload and the availability requirement. It is challenging to achieve high performance, reliability, and efficient resource management for these applications under highly concurrent workload. This project addresses the system and programming-level issues in building high performance and reliable runtime support for highly concurrent network services and online applications. There are three main research thrusts in this project. First, cluster-based system architectures are studied with a focus on overall system scalability/availability as well as load balancing and data replication support. Secondly, this project investigates programming and runtime support to simplify the development and deployment of online applications that involve concurrent and parallel data manipulation on clusters. Thirdly, a cluster-based resource management framework will be developed with the combined goal of quality-of-service and overall resource utilization efficiency. Throughout this project, efforts will be made to examine data-intensive applications and validate the effectiveness of the proposed work. Additional issues will be investigated as more experience are gained through system development and application evaluation. The developed software will be released in public for potential use of other applications. Since more and more data-intensive services are being made available through intranets and the Internet, it is expected that the developed results will have broad impacts. Research described in this proposal will directly benefit instruction improvement for undergraduate and graduate education at PIs' institutions and enhance students' experience in cluster-based computing.