A Proactive Statistical Model Supporting Services and Tasks Management in Pervasive Applications

The combination of the Internet of Things (IoT) and Edge Computing (EC) can support intelligent pervasive applications that meet the needs of end users. A challenge is to provide efficient inference models for supporting collaborative activities. EC nodes can interact with IoT devices and each other to conclude those activities producing knowledge. In this paper, we propose a proactive scheme to decide upon the efficient management of services and tasks present/reported at EC nodes. Services can be processing modules applied upon local data while being required for the execution of tasks. We monitor the demand for the available services and reason upon their management, i.e., for their local presence/invocation as the demand is updated by the requested processing activities. For each incoming task, an inference process is fired to proactively meet the strategic targets of the envisioned model. We propose a statistical inference process upon the demand for services and the contextual performance data of nodes combining it with a utility aware decision making model. Instead of exclusively focusing on services migration or tasks offloading as other relevant efforts do, we elaborate on the decision making for the selection of one of the aforementioned activities (the most appropriate at a specific time instance). We present our model and evaluate it through a high number of simulations to expose its pros and cons placing it in the respective literature as one of the first attempts to proactively decide the presence of services to an ecosystem of processing nodes.