Heuristic edge server placement in Industrial Internet of Things and cellular networks
Article
Kasi, S., Kasi, M., Ali, K., Raza, M., Afzal, H., Lasebae, A., Naeem, B., Islam, S. and Rodrigues, J. 2021. Heuristic edge server placement in Industrial Internet of Things and cellular networks. IEEE Internet of Things Journal. 8 (13), pp. 10308-10317. https://doi.org/10.1109/JIOT.2020.3041805
| Type | Article |
|---|---|
| Title | Heuristic edge server placement in Industrial Internet of Things and cellular networks |
| Authors | Kasi, S., Kasi, M., Ali, K., Raza, M., Afzal, H., Lasebae, A., Naeem, B., Islam, S. and Rodrigues, J. |
| Abstract | Rapid developments in industry 4.0, machine learning, and digital twins have introduced new latency, reliability, and processing restrictions in Industrial Internet of Things (IIoT) and mobile devices. However, using current Information and Communications Technology (ICT), it is difficult to optimally provide services that require high computing power and low latency. To meet these requirements, mobile edge computing is emerging as a ubiquitous computing paradigm that enables the use of network infrastructure components such as cluster heads/sink nodes in IIoT and cellular network base stations to provide local data storage and computation servers at the edge of the network. However, optimal location selection for edge servers within a network out of a very large number of possibilities, such as to balance workload and minimize access delay is a challenging problem. In this paper, the edge server placement problem is addressed within an existing network infrastructure obtained from Shanghai Telecom’s base station the dataset that includes a significant amount of call data records and locations of actual base stations. The problem of edge server placement is formulated as a multi-objective constraint optimization problem that places edge servers strategically to the balance between the workloads of edge servers and reduce access delay between the industrial control center/cellular base-stations and edge servers. To search randomly through a large number of possible solutions and selecting those that are most descriptive of optimal solution can be a very time-consuming process, therefore, we apply the genetic algorithm and local search algorithms (hillclimbing and simulated annealing) to find the best solution in the least number of solution space explorations. Experimental results are obtained to compare the performance of the genetic algorithm against the above-mentioned local search algorithms. The results show that the genetic algorithm can quickly search through the large solution space as compared to local search optimization algorithms to find an edge placement strategy that minimizes the cost function |
| Keywords | Servers; Cloud computing; Optimization; Base stations; Genetic algorithms; Edge computing; Delays; Data mining; edge server placement; genetic search; Industrial Internet of Things (IIoT); mobile-edge computing |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| Journal | IEEE Internet of Things Journal |
| ISSN | 2327-4662 |
| Publication dates | |
| Online | 01 Dec 2020 |
| 01 Jul 2021 | |
| Publication process dates | |
| Deposited | 23 Oct 2020 |
| Accepted | 21 Oct 2020 |
| Output status | Published |
| Accepted author manuscript | |
| Copyright Statement | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| Digital Object Identifier (DOI) | https://doi.org/10.1109/JIOT.2020.3041805 |
| Web of Science identifier | WOS:000665207100009 |
| Language | English |
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