Self-adaptive software: Landscape and research challenges
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
A framework for evaluating quality-driven self-adaptive software systems
Proceedings of the 6th International Symposium on Software Engineering for Adaptive and Self-Managing Systems
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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.