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The advent of the Internet of Things (IoT) gives the opportunity to numerousdevices to interact with their environment, collect and process data. Data aretransferred, in an upwards mode, to the Cloud through the Edge Computing (EC)infrastructure. A high number of EC nodes become the hosts of distributeddatasets where various processing activities can be realized in close distancewith end users. This approach can limit the latency in the provision ofresponses. In this paper, we focus on a model that proactively decides wherethe collected data should be stored in order to maximize the accuracy ofdatasets present at the EC infrastructure. We consider that the accuracy isdefined by the solidity of datasets exposed as the statistical resemblance ofdata. We argue upon the similarity of the incoming data with the availabledatasets and select the most appropriate of them to store the new information.For alleviating processing nodes from the burden of a continuous, complicatedstatistical processing, we propose the use of synopses as the subject of thesimilarity process. The incoming data are matched against the availablesynopses based on an ensemble scheme, then, we select the appropriate host tostore them and perform the update of the corresponding synopsis. We provide thedescription of the problem and the formulation of our solution. Ourexperimental evaluation targets to reveal the performance of the proposedapproach.

An Ensemble Scheme for Proactive Data Allocation in Distributed Datasets