ISEP/DEE

Describes a virtual file system that allows data to be transferred on demand between storage and computational servers for the duration of a computing session. The solution works with unmodified applications (even commercial ones) running on standard operating systems and hardware. The virtual file system employs software proxies to broker transactions between standard NFS (Network File System) clients and servers; the proxies are dynamically configured and controlled by computational grid middleware. The approach has been implemented and extensively exercised in the context of PUNCH (Purdue University Network Computing Hubs), an operational computing portal that has more than 1,500 users across 24 countries. The results show that the virtual file system performs well in comparison to native NFS: performance analyses show that the proxy incurs mean overheads of 1% and 18% with respect to native NFS for a single-client execution of the Andrew benchmark in two representative computing environments, and that the average overhead for eight clients can be reduced to within 1% of native NFS with concurrent proxies

This paper introduces Archer, a community-based computing infrastructure supporting computer architecture research and education. The Archer system builds on virtualization techniques to provide a collaborative environment that facilitates sharing of computational resources and data among users. It integrates batch scheduling middleware to deliver high-throughput computing services aggregated from resources distributed across wide-area networks and owned by different participating entities in a seamless manner. The paper discusses the motivations that have led to the design of Archer, describes its core middleware components, and presents an analysis of the functionality and performance of the first wide-area deployment of Archer running a representative computer architecture simulation workload.

Profiling the execution phases of an application can lead to optimizing the utilization of the underlying resources. This is the thrust of this paper, which presents a novel system-level application resource demand phase analysis and prediction prototype to support on-demand resource provisioning. The phase profile learned from historical runs is used to classify and predict phase behavior using a set of algorithms based on clustering. The process takes into consideration application's resource consumption patterns, pricing schedules defined by the resource provider, and penalties associated with service-level agreement (SLA) violations.

Computational grids provide computing power by sharing resources across administrative domains. This sharing, coupled with the need to execute untrusted code from arbitrary users, introduces security hazards. This paper addresses the security implications of making a computing resource available to untrusted applications via computational grids. It highlights the problems and limitations of current grid environments and proposes a technique that employs run-time monitoring and a restricted shell. The technique can be used for setting up an execution environment that supports the full legitimate use allowed by the security policy of a shared resource. Performance analysis shows up to 2.14 times execution overhead improvement for shell-based applications. The approach proves effective and provides a substrate for hybrid techniques that combine static and dynamic mechanisms to minimize monitoring overheads.