An analysis of Genetic Algorithms to support the management of edge computing infrastructures

Edge computing is a novel paradigm aiming to push computation as close as possible to the data sources and to the end users. This paradigm is extremely important in areas such as the mobile applications and IoT. Key characteristics of edge computing are the limited computational resource of the edge nodes and the presence of non-negligible network delays that can affect the performance. Applications are typically designed as a set of inter-operating micro-services, where each service must be placed on an edge node in order to minimize network latency, while balancing the load distribution over the nodes to avoid the violation the the Service Level Agreements. An additional goal is to minimize energy consumption, meaning that the number of powered-on edge nodes must be kept as low as possible. For these reasons, the problem of micro-service placement over an edge computing infrastructure is complex and must be solved by means of heuristics that must reach suitable solutions under a wide set of operating conditions. We propose a mechanism to solve the placement problem based on genetic algorithms to solve the micro-service placement problem and we analyze the behavior of such heuristic for a wide set of problem characteristics. The proposed algorithm can automatically identify the subset of edge nodes to use and can allocate micro-services to reduce network delays and balance the load. Our experiments demonstrate that the proposed GA is a viable tool to allocate micro-services in edge infrastructures as it can find adequate solutions with a limited number of generations and provides stable performance over a wide set of problem characteristics with limited need for tuning.