Iterative Probabilistic Performance Prediction for Multiple IoT Applications in Contention
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Internet of Things (IoT) have become omnipresent in many applications such as health-care, vehicles and precision farming. They sense data from dozens of sensors scheduled periodically in a synchronous fashion on mobile CPUs that are forwarded to the cloud or other IoT devices via an essentially stochastic wireless channel. Hence, the task response time becomes stochastic, preventing optimization at compile time. On the other hand, knowing response time at compile time along with jitter, availability and scalability is crucial to ensure a certain level of Quality of Service. This contribution presents a stochastic framework for performance analyzes of multi-applications on a possibly multi-processor platform. When annotated with (stochastic) execution time, traditional synchronous dataflow (SDF) graph can be transformed into a directed acyclic workflow graph, revealing the timing of individual actors. A generalized version of the rejection sampling Monte Carlo algorithm explores the properties of the workflow graph, to determine the distribution of the response time in a single application as well as a multi-application multiple access scenario. Mean and jitter are the moments of the distribution. An IoT toy-example with a number of distributed smart sensors was deployed in real environments to assess the performance of the proposed framework. Our analysis framework works at compile time of the code, scales with the number of things and has low computational complexity.
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