Knowledge-centric Analytics Queries Allocation in Edge Computing Environments

Sagkriotis, S. , Kolomvatsos, K., Anagnostopoulos, C. , Pezaros, D. P. and Hadjiefthymiades, S. (2020) Knowledge-centric Analytics Queries Allocation in Edge Computing Environments. In: IEEE ISCC Symposium on Computers and Communications, Barcelona, Spain, 29 June - 03 July 2019, ISBN 9781728129990 (doi: 10.1109/ISCC47284.2019.8969706)

184022.pdf - Accepted Version



The Internet of Things involves a huge number of devices that collect data and deliver them to the Cloud. The processing of data at the Cloud is characterized by increased latency in providing responses to analytics queries defined by analysts or applications. Hence, Edge Computing (EC) comes into the scene to provide data processing close to the source. The collected data can be stored in edge devices and queries can be executed there to reduce latency. In this paper, we envision a case where entities located in the Cloud undertake the responsibility of receiving analytics queries and decide on the most appropriate edge nodes for queries execution. The decision is based on statistical signatures of the datasets of nodes and the statistical matching between statistics and analytics queries. Edge nodes regularly update their statistical signatures to support such decision process. Our performance evaluation shows the advantages and the shortcomings of our proposed schema in edge computing environments.

Item Type:Conference Proceedings
Additional Information:This research has been supported in part by the UK Engineering and Physical Sciences Research Council (EPSRC) projects EP/N033957/1, and EP/P004024/1; by the European Cooperation in Science and Technology (COST) Action CA 15127: RECODIS – Resilient communication and services; and by the EU H2020 GNFUV Project RAWFIE-OC2-EXPSCI (Grant No. 645220) under the EC FIRE+ initiative.
Glasgow Author(s) Enlighten ID:Sagkriotis, Stefanos and Anagnostopoulos, Dr Christos and Pezaros, Professor Dimitrios and Kolomvatsos, Dr Kostas
Authors: Sagkriotis, S., Kolomvatsos, K., Anagnostopoulos, C., Pezaros, D. P., and Hadjiefthymiades, S.
College/School:College of Science and Engineering
College of Science and Engineering > School of Computing Science
Copyright Holders:Copyright © 2019 IEEE
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
709131Network Measurement as a Service (MaaS)Dimitrios PezarosEngineering and Physical Sciences Research Council (EPSRC)EP/N033957/1COM - COMPUTING SCIENCE
722161FRuIT: The Federated RaspberryPi Micro-Infrastructure TestbedJeremy SingerEngineering and Physical Sciences Research Council (EPSRC)EP/P004024/1COM - COMPUTING SCIENCE