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Dive into the research topics where Gary Hoo is active.

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Featured researches published by Gary Hoo.


Lawrence Berkeley National Laboratory | 1999

The NetLogger Methodology for High Performance Distributed Systems Performance Analysis

Brian Tierney; William E. Johnston; Brian Crowley; Gary Hoo; Christopher Brooks; Dan Gunter

The authors describe a methodology that enables the real-time diagnosis of performance problems in complex high-performance distributed systems. The methodology includes tools for generating precision event logs that can be used to provide detailed end-to-end application and system level monitoring; a Java agent-based system for managing the large amount of logging data; and tools for visualizing the log data and real-time state of the distributed system. The authors developed these tools for analyzing a high-performance distributed system centered around the transfer of large amounts of data at high speeds from a distributed storage server to a remote visualization client. However, this methodology should be generally applicable to any distributed system. This methodology, called NetLogger, has proven invaluable for diagnosing problems in networks and in distributed systems code. This approach is novel in that it combines network, host, and application-level monitoring, providing a complete view of the entire system.


high performance distributed computing | 1998

The NetLogger methodology for high performance distributed systems performance analysis

Brian Tierney; William E. Johnston; Brian Crowley; Gary Hoo; Christopher X. Brooks; Dan Gunter

We describe a methodology that enables the real-time diagnosis of performance problems in complex high-performance distributed systems. The methodology includes tools for generating precision event logs that can be used to provide detailed end-to-end application and system level monitoring; a Java agent-based system for managing the large amount of logging data; and tools for visualizing the log data and real-time state of the distributed system. We developed these tools for analyzing a high-performance distributed system centered around the transfer of large amounts of data at high speeds from a distributed storage server to a remote visualization client. However this methodology should be generally applicable to any distributed system. This methodology called NetLogger has proven invaluable for diagnosing problems in networks and in distributed systems code. This approach is novel in that it combines network, host, and application-level monitoring, providing a complete view of the entire system.


acm multimedia | 1994

Distributed parallel data storage systems: a scalable approach to high speed image servers

Brian Tierney; Jason Lee; Ling Tony Chen; Hanan Herzog; Gary Hoo; Guojun Jin; William E. Johnston

We have designed, built, and analyzed a distributed parallel storage system that will supply image streams fast enough to permit multi-user, “real-time”, video-like applications in a wide-area ATM network-based Internet environment. We have based the implementation on user-level code in order to secure portability; we have characterized the performance bottlenecks arising from operating system and hardware issues, and based on this have optimized our design to make the best use of the available performance. Although at this time we have only operated with a few classes of data, the approach appears to be capable of providing a scalable, high-performance, and economical mechanism to provide a data storage system for several classes of data (including mixed multimedia streams), and for applications (clients) that operate in a high-speed network environment.


high performance distributed computing | 1999

QoS as middleware: bandwidth reservation system design

Gary Hoo; W. Johnston; Ian T. Foster; A. Roy

We wish to provide quality of service (QoS) to scientists who are remotely controlling experiments on singular instruments such as the LBL Advanced Light Source (ALS) or who need to harness heterogeneous and distributed computing resources to perform large-scale computation. To ensure the timely availability of the computing, storage, and networking resources required for data collection and/or analysis, use of these resources must be scheduled in advance. The bandwidth reservation system (BRS) reserves time in IP differentiated service classes (S. Blake et al.). These classes have upper bounds on the total bandwidth allocation. The reservation unit is a slot: a well-defined period of time with an associated bandwidth. The sum of the allocated bandwidths in all of the slot allocations never exceeds the maximum bandwidth defined for the service class. The result is that the classes are never oversubscribed.


conference on high performance computing (supercomputing) | 1994

Using high speed networks to enable distributed parallel image server systems

Brian Tierney; William E. Johnston; Hanan Herzog; Gary Hoo; Guojun Jin; Jason Lee; Ling Tony Chen; Doron Rotem

We describe the design and implementation of a distributed parallel storage system that uses high-speed ATM networks as a key element of the architecture. Other elements include a collection of network-based disk block servers, and an associated name server that provides some file system functionality. The implementation is based on user level software that runs on UNIX workstations. Both the architecture and the implementation are intended to provide for easy and economical scalability. This approach has yielded a data source that scales economically to very high speed. Target applications include online storage for both very large images and video sequences. This paper describes the architecture, and explores the performance issues of the current implementation.<<ETX>>


conference on high performance computing (supercomputing) | 1997

High-Speed Distributed Data Handling for On-Line Instrumentation Systems

William E. Johnston; William Greiman; Gary Hoo; Jason Lee; Brian Tierney; Craig E. Tull; D. Olson

The advent (and promise) of shared, widely available, high-speed networks provides the potential for new approaches to the collection, organization, storage, and analysis of high-speed and high-volume data streams from high data-rate, on-line instruments. We have worked in this area for several years, have identified and addressed a variety of problems associated with this scenario, and have evolved an architecture, implementations, and a monitoring methodology that have been successful in addressing several different application areas. We describe a distributed, wide area network-based architecture that deals with data streams that originate from online instruments. Such instruments and imaging systems are a staple of modern scientific, health care, and intelligence environments. Our work provides an approach for reliable, distributed real-time analysis, cataloguing, and archiving of the data streams through the integration and distributed management of a high-speed distributed cache, distributed high performance applications, and tertiary storage systems.


usenix security symposium | 1999

Certificate-based access control for widely distributed resources

Mary R. Thompson; William E. Johnston; Srilekha Mudumbai; Gary Hoo; Keith Jackson; Abdelilah Essiari


HSNS'94 Proceedings of the High-Speed Networking Symposium on USENIX 1994 High-Speed Networking Symposium | 1994

System issues in implementing high speed distributed parallel storage systems

Brian Tierney; Bill Johnston; Hanan Herzog; Gary Hoo; Guojun Jin; Jason Lee


International Journal on Digital Libraries | 1997

Real-Time digital libraries based on widely distributed, high performance management of large data objects

William E. Johnston; Jin Guojun; Case Larsen; Jason Lee; Gary Hoo; Mary R. Thompson; Brian Tierney; Joseph Terdiman


Archive | 1997

Real-Time Generation and Cataloguing of Large Data-Objects in Widely Distributed Environments

William E. Johnston; Guiyue Jin; Christian Larsen; Jason Lee; Gary Hoo; Mary R. Thompson; Brian Tierney; Joseph Terdiman

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William E. Johnston

Lawrence Berkeley National Laboratory

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Brian Tierney

Lawrence Berkeley National Laboratory

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Jason Lee

Lawrence Berkeley National Laboratory

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Mary R. Thompson

Lawrence Berkeley National Laboratory

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Guojun Jin

Lawrence Berkeley National Laboratory

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Hanan Herzog

Lawrence Berkeley National Laboratory

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Ling Tony Chen

Lawrence Berkeley National Laboratory

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Brian Crowley

Lawrence Berkeley National Laboratory

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Case Larsen

Lawrence Berkeley National Laboratory

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Dan Gunter

Lawrence Berkeley National Laboratory

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