Proactive, uncertainty-driven queries management at the edge

摘要

Research community has already revealed the challenges of data processing when performed at the Cloud that may affect the performance of any desired application. The main challenge is the increased latency observed when the data should 'travel' to the Cloud from the location they are collected and the waiting time for getting the final response. In an Internet of Things (IoT) scenario, this time could be critical for supporting real time applications. A solution to the discussed problem is the adoption of an Edge Computing (EC) approach where data can be processed close to their collection point. IoT devices could report data to a number of edge nodes that behave as distributed data repositories having the capability of processing them and producing analytics. Analytics should match the requirements of queries defined by end users or applications with the collected data and the characteristics of every edge node. However, when a query is defined, we should identify the appropriate edge node(s) to process it. In this paper, we propose an uncertainty management model to efficiently allocate every incoming query to the available edge nodes. Our scheme adopts the principles of the Fuzzy Logic (FL) theory and provides a decision making mechanism for the entity having the responsibility of the envisioned allocations. We combine the proposed uncertainty management scheme with a machine learning model based on a Support Vector Machine (SVM) to enhance the FL reasoning. Our aim is to manage all the hidden aspects of the problem combining two different technologies with different orientations. We also propose a methodology for the automated generation of the Footprint of Uncertainty (FoU) of membership functions involved in our interval Type-2 FL model. Our experimental evaluation aims at revealing the pros and cons of our mechanism presenting the results of extensive simulations adopting datasets found in the literature and a comparative analysis with other efforts in the domain.