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PUNCH: An architecture for Web-enabled wide-area network-computing

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Abstract

This paper addresses the architectural issues that arise in the design of a universally accessible wide-area network-computing system that is capable of making automatic cost/performance tradeoff decisions at run-time. The core system is designed around a three-level hierarchically distributed architecture, a choice driven by the dynamic, incremental, and distributed nature of the information associated with run-time cost/performance tradeoff decisions. Support for independent replication of each component in the hierarchy contributes to the overall scalability and reliability of the architecture. Meta-information is managed in a scalable manner by employing self-encoded resource identifiers that allow O(1) access to all managed information. Security and access control across administrative domains are provided by partitioning the infrastructure into independently-managed cells, and by giving administrators the ability to customize user-views directly at the location at which the request is processed. Demand-driven resource management is achieved by predicting the run-specific resource usage characteristics of tools via machine learning techniques. The concepts described in this paper are embodied in the Purdue University Network-Computing Hubs (PUNCH), a demand-based network-computing system that allows users to access and run unmodified tools via standard World Wide Web browsers. Tools do not have to be written in any particular language, and access to the source or object code is not required. The PUNCH infrastructure can be distributed in a manner that allows tools to be (user-transparently) executed wherever they reside. Currently, PUNCH contains over thirty tools developed by eight universities and four vendors, and serves more than five hundred users. During the past three years, PUNCH users have logged more than one million hits and have performed over seventy thousand simulations.

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Kapadia, N.H., Fortes, J.A. PUNCH: An architecture for Web-enabled wide-area network-computing. Cluster Computing 2, 153–164 (1999). https://doi.org/10.1023/A:1019026725028

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