Edge-centric queries stream management based on an ensemble model

Kolomvatsos, K. and Anagnostopoulos, C. (2020) Edge-centric queries stream management based on an ensemble model. In: Hatzilygeroudis, I., Perikos, I. and Grivokostopoulou, F. (eds.) Advances in Integration of Intelligent Methods. Series: Smart innovation, systems and technologies (170). Springer: Singapore, pp. 27-48. ISBN 9789811519178 (doi:10.1007/978-981-15-1918-5_2)

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Abstract

The Internet of things (IoT) involves numerous devices that can interact with each other or with their environment to collect and process data. The collected data streams are guided to the cloud for further processing and the production of analytics. However, any processing in the cloud, even if it is supported by improved computational resources, suffers from an increased latency. The data should travel to the cloud infrastructure as well as the provided analytics back to end users or devices. For minimizing the latency, we can perform data processing at the edge of the network, i.e., at the edge nodes. The aim is to deliver analytics and build knowledge close to end users and devices minimizing the required time for realizing responses. Edge nodes are transformed into distributed processing points where analytics queries can be served. In this paper, we deal with the problem of allocating queries, defined for producing knowledge, to a number of edge nodes. The aim is to further reduce the latency by allocating queries to nodes that exhibit low load (the current and the estimated); thus, they can provide the final response in the minimum time. However, before the allocation, we should decide the computational burden that a query will cause. The allocation is concluded by the assistance of an ensemble similarity scheme responsible to deliver the complexity class for each query. The complexity class, thus, can be matched against the current load of every edge node. We discuss our scheme, and through a large set of simulations and the adoption of benchmarking queries, we reveal the potentials of the proposed model supported by numerical results.

Item Type:Book Sections
Status:Published
Glasgow Author(s) Enlighten ID:Kolomvatsos, Dr Kostas and Anagnostopoulos, Dr Christos
Authors: Kolomvatsos, K., and Anagnostopoulos, C.
College/School:College of Science and Engineering > School of Computing Science
Publisher:Springer
ISBN:9789811519178
Copyright Holders:Copyright © 2020 Springer Nature Singapore Pte Ltd.
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
301654Intelligent Applications over Large Scale Data StreamsChristos AnagnostopoulosEuropean Commission (EC)745829Computing Science