A probabilistic model for assigning queries at the edge

Kolomvatsos, K. and Anagnostopoulos, C. (2020) A probabilistic model for assigning queries at the edge. Computing, 102, pp. 865-892. (doi: 10.1007/s00607-019-00767-8)

[img] Text
202371.pdf - Published Version
Available under License Creative Commons Attribution.



Data management at the edge of the network can increase the performance of applications as the processing is realized close to end users limiting the observed latency in the provision of responses. A typical data processing involves the execution of queries/tasks defined by users or applications asking for responses in the form of analytics. Query/task execution can be realized at the edge nodes that can undertake the responsibility of delivering the desired analytics to the interested users or applications. In this paper, we deal with the problem of allocating queries to a number of edge nodes. The aim is to support the goal of eliminating further the latency by allocating queries to nodes that exhibit a low load and high processing speed, thus, they can respond in the minimum time. Before any allocation, we propose a method for estimating the computational burden that a query/task will add to a node and, afterwards, we proceed with the final assignment. The allocation is concluded by the assistance of an ensemble similarity scheme responsible to deliver the complexity class for each query/task and a probabilistic decision making model. The proposed scheme matches the characteristics of the incoming queries and edge nodes trying to conclude the optimal allocation. We discuss our mechanism 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:Articles
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
Journal Name:Computing
ISSN (Online):1436-5057
Published Online:18 November 2019
Copyright Holders:Copyright © 2019 The Authors
First Published:First published in Computing 102:865-892
Publisher Policy:Reproduced under a Creative Commons License

University Staff: Request a correction | Enlighten Editors: Update this record

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