Structured decomposition of adaptive applications

Authors:
Justin Mazzola Paluska;Hubert Pham;Umar Saif;Grace Chau;Chris Terman;Steve Ward
Affiliations:
MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA;MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA;LUMS Computer Science Department, Lahore, Pakistan;MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA;MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA;MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA
Venue:
Pervasive and Mobile Computing
Year:
2008

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Abstract

We describe an approach to automate certain high-level implementation decisions in a pervasive application, allowing them to be postponed until runtime. Our system enables a model in which an application programmer can specify the behavior of an adaptive application as a set of open-ended decision points. We formalize decision points as Goals, each of which may be satisfied by a set of scripts called Techniques. The set of Techniques vying to satisfy any Goal is additive and may be extended at runtime without needing to modify or remove any existing Techniques. Our system provides a framework in which Techniques may compete and interoperate at runtime in order to maintain an adaptive application. Technique development may be distributed and incremental, providing a path for the decentralized evolution of applications. Benchmarks show that our system imposes reasonable overhead during application startup and adaptation.