Container-Based Load Balancing and Monitoring Approach in Fog Computing System
2022 (English)In: MELECON 2022 - IEEE Mediterranean Electrotechnical Conference, Proceedings, Institute of Electrical and Electronics Engineers Inc. , 2022, p. 1159-1164Conference paper, Published paper (Refereed)
Abstract [en]
The Internet of Things has become a fast-growing area and has attracted considerable attention from the research communities. To provide satisfactory performance and efficiency in smart applications, it is inevitable to apply proper and efficient load balancing mechanisms. This paper presents a container-based load balancing and monitoring approach in fog-cloud environments. The presented architecture is composed of application services, message queuing system and online monitoring tools to address fault-detection, reliability, higher efficiency and scalability that are essential requirements of smart applications. In addition, an implementation of the proposed approach by using well-known technologies is presented, and the system is evaluated. Results indicate that using this model satisfies the requirements and can be considered as a practical solution to fog computing applications.
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2022. p. 1159-1164
Keywords [en]
Container-Based infrastructure, Fog computing, Load Balancing, Message Queuing System, Monitoring, Containers, Efficiency, Fault detection, Fog, Queueing networks, Computing system, Load monitoring, Load-Balancing, Monitoring approach, Performance, Queuing systems, Research communities, Smart applications
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:ri:diva-60088DOI: 10.1109/MELECON53508.2022.9843096Scopus ID: 2-s2.0-85136402239ISBN: 9781665442800 (electronic)OAI: oai:DiVA.org:ri-60088DiVA, id: diva2:1697655
Conference
21st IEEE Mediterranean Electrotechnical Conference, MELECON 2022, 14 June 2022 through 16 June 2022
Note
Funding details: 101007273, 876038; Funding details: Horizon 2020 Framework Programme, H2020; Funding text 1: This work was partially supported by EU KDT project InSecTT and DAIS that has received funding from the KDT Joint Undertaking (JU) under grant agreement No.876038 and No.101007273. InSecTT (www.insectt.eu) has received funding from the KDT Joint Undertaking (JU) under grant agreement No 876038. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Austria, Sweden, Spain, Italy, France, Portugal, Ireland, Finland, Slovenia, Poland, Netherlands, Turkey. The document reflects only the author’s view and the Commission is not responsible for any use that may be made of the information it contains.
2022-09-212022-09-212022-09-21Bibliographically approved