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Middleware for enterprise scale data stream management using utility-driven self-adaptive information flows

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Abstract

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.

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Authors and Affiliations

  1. Center for Experimental Research in Computer Science, Georgia Institute of Technology, Atlanta, GA, 30332, USA

    Vibhore Kumar, Brian F. Cooper, Zhongtang Cai, Greg Eisenhauer & Karsten Schwan

Authors
  1. Vibhore Kumar
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  2. Brian F. Cooper
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  3. Zhongtang Cai
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  4. Greg Eisenhauer
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  5. Karsten Schwan
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Correspondence to Vibhore Kumar.

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Kumar, V., Cooper, B.F., Cai, Z. et al. Middleware for enterprise scale data stream management using utility-driven self-adaptive information flows. Cluster Comput 10, 443–455 (2007). https://doi.org/10.1007/s10586-007-0040-9

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