Proactive, uncertainty-driven queries management at the edge

Kolomvatsos, K. and Anagnostopoulos, C. (2021) Proactive, uncertainty-driven queries management at the edge. Future Generation Computer Systems, 118, pp. 75-93. (doi: 10.1016/j.future.2020.12.028)

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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 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.

Item Type:Articles
Additional Information:This research received funding from the European's Union Horizon 2020 research and innovation programme under the grant agreement No. 745829.
Glasgow Author(s) Enlighten ID:Anagnostopoulos, Dr Christos and Kolomvatsos, Dr Kostas
Authors: Kolomvatsos, K., and Anagnostopoulos, C.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Future Generation Computer Systems
ISSN (Online):1872-7115
Published Online:07 January 2021
Copyright Holders:Copyright © 2021 Elsevier B.V.
First Published:First published in Future Generation Computer Systems 118: 75-93
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
301654Intelligent Applications over Large Scale Data StreamsChristos AnagnostopoulosEuropean Commission (EC)745829Computing Science