Distributed Stream Management using Utility-Driven Self-Adaptive Middleware

We consider pervasive computing applications that process and aggregate data-streams emanating from highly distributed data sources to produce a stream of updates that have an implicit business-value. Middleware that enables such aggregation of data-streams must support scalable and efficient self-management to deal with changes in the operating conditions and should have an embedded business-sense. 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