Middleware for enterprise scale data stream management using utility-driven self-adaptive information flows
| Autor: | Greg Eisenhauer, Karsten Schwan, Brian F. Cooper, Zhongtang Cai, Vineet Kumar |
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| Rok vydání: | 2007 |
| Předmět: |
Data stream
Scheme (programming language) Computer Networks and Communications business.industry Data stream mining Computer science Control reconfiguration computer.software_genre Resource (project management) Middleware Middleware (distributed applications) Message oriented middleware Scalability business computer Software computer.programming_language Computer network |
| Zdroj: | Cluster Computing. 10:443-455 |
| ISSN: | 1573-7543 1386-7857 |
| DOI: | 10.1007/s10586-007-0040-9 |
| Popis: | We consider enterprise-wide information flows that are responsible for acquiring, processing and delivering operational information across the business units. Middleware that enables such aggregation of data-streams must not only support scalable and efficient self-management to deal with changes in the operating conditions, but should also have an embedded business-sense to appreciate the business critical nature of some updates. In this paper, we present a novel self-adaptation algorithm that has been designed to scale efficiently for thousands of streams and aims to maximize the overall business utility attained from running middleware-based applications. The outcome is that the middleware not only deals with changing network conditions or resource requirements, but also responds appropriately to changes in business policies. An important feature of the algorithm is a hierarchical node-partitioning scheme that decentralizes reconfiguration and suitably localizes its impact. Extensive simulation experiments and benchmarks attained with actual enterprise operational data corroborate this paper's claims. |
| Databáze: | OpenAIRE |
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