Energy- and Resource-Aware Graph-Based Microservices Placement in the Cloud-Fog-Edge Continuum

The development of Cloud-Fog-Edge computing infrastructures in response to the rapid advance of IoT technologies requires applications to be positioned closer to users at the edge of the network. Characterised by a geographically distributed configuration with numerous heterogeneous nodes, these infrastructures face challenges such as node failures, mobility constraints, resource limitations and network congestion. To address these issues, the adoption of microservices-based application architectures has been encouraged. However, the interdependencies and function calls between services require careful optimisation, as each has unique resource requirements. In this paper, we propose a new model and heuristic for the placement of microservices in the Cloud-Fog-Edge continuum, based on community detection and a greedy algorithm to optimise energy use while taking into account the resource constraints and ensuring that response time is acceptable. This method aims to reduce energy consumption and network load, thereby improving the efficiency and sustainability of the infrastructure. Results have been compared with different scenarios and show that our approach can significantly reduce energy consumption and make efficient use of resources.