An intelligent, time-optimized monitoring scheme for edge nodes

Anagnostopoulos, C. and Kolomvatsos, K. (2019) An intelligent, time-optimized monitoring scheme for edge nodes. Journal of Network and Computer Applications, 148, 102458. (doi: 10.1016/j.jnca.2019.102458)

Abstract

Monitoring activities over edge resources and services are essential in today's applications. Edge nodes can monitor their status and end users/applications requirements to identify their ‘matching’ and deliver alerts when violations are present. Violations are related to any disturbance of the desired Quality of Service (QoS). QoS depends on a number of performance metrics and can differ among applications. In this paper, we propose the use of an intelligent mechanism to be incorporated in monitoring tools adopted by edge nodes. The proposed mechanism observes the realizations of performance parameters that result in specific QoS values and decides when it is the right time to ‘fire’ mitigation actions. Hence, edge nodes are capable of changing their configuration to secure the desired QoS levels as dictated by end users/applications requirements. In our work, a mitigation action could involve either upgrades in the current services/resources or offloading tasks by transferring computational load and data to peer nodes or the Cloud. We present our model and provide formulations for the solution of the problem. A high number of simulations reveal the performance of the proposed mechanism. Our experiments show that our scheme outperforms any deterministic model defined for the discussed setting as well as other efforts found in the relevant literature.

• Item Type: Articles
• Additional Information: Funding information: Dr Anagnostopoulos is coordinating (Principal Investigator) the projects: EU H2020/GNFUV and EU H2020 Marie Skłodowska-Curie (MSCA)/INNOVATE, and is a co-PI of the EU PRIMES and UK EPSRC CLDS. He has received funding for his research by the EC/H2020 and the industry.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Kolomvatsos, Dr Kostas and Anagnostopoulos, Dr Christos
• Authors: Anagnostopoulos, C., and Kolomvatsos, K.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: Journal of Network and Computer Applications
• Publisher: Elsevier
• ISSN: 1084-8045
• ISSN (Online): 1095-8592
• Published Online: 05 October 2019
• Copyright Holders: Copyright © 2019 Elsevier
• First Published: First published in Journal of Network and Computer Applications 148:102458
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher