Optimizing Dynamic Producer/Consumer Style Applications in Embedded Environments
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Many applications in pervasive computing environments are subject to resource constraints in terms of limited bandwidth and processing power. As such applications grow in scale and complexity, these constraints become increasingly difficult to predict at design and deployment times. Runtime adaptation is hence required for the dynamics in such constraints. However, to maintain the lightweightness of such adaptation it is important to statically gather relevant program information to reduce the runtime overhead of dynamic adaptation. This paper presents methods that use both static program analysis and runtime profiling to support the adaptation of producer/consumer-style pervasive applications. It demonstrates these methods with a network traffic-centric cost model and a program execution time-centric cost model. A communication bandwidth critical application and a computation intensive application are used to demonstrate the significant performance improvement opportunities offered by these methods under the presence of respective resource constraints.