This thesis tackles the topic of serving IoT applications in the computing continuum. It proposes an approach to place applications in the tiers of the continuum, considering latency and energy as predefined metrics. It presents a system model to represent the computing continuum environment, and then, defines an optimization function that is tailored to meet the specific requirements of the IoT applications. The optimization function addresses the relationship between latency and energy consumption in the framework of IoT service provision, and it is implemented in two different directions: (1) the first direction uses a modified Genetic algorithm, and (2) the second direction utilizes the Machine learning concept. To evaluate the performance of the proposed approach, we incorporate different testbed setups and network configurations. All the setups and configurations are designed to represent the diverse demands of IoT applications. Then, different algorithms (such as Non-dominated Sorting Genetic Algorithm (NSGA), Brute Force, and Machine Learning) are implemented to provide different application placement scenarios. The results highlight the efficiency of the proposed approach in comparison with the Brute Force optimal solution while meeting the application requirements. This thesis proposes an optimized solution for serving IoT applications in the computing continuum environment. It considers two essential metrics (latency and energy consumption) in the applications placement processes while meeting the diverse functional and non-functional requirements of these applications. The study provides insights and ideas for future research to refine strategies that will minimize latency and energy consumption. It also urges researchers to consider more metrics while developing and implementing IoT applications. The requirements related to computing resources and performance levels make the development and implementation of these applications complex and challenging. This study serves as a foundational stepping stone towards addressing those challenges.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:du-48497 |
| Date | January 2024 |
| Creators | Gallage, Malaka, De Silva, Dasith |
| Publisher | Högskolan Dalarna, Institutionen för information och teknik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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