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Applications that are constrained by low-latency requirements can hardly be executed on cloud infrastructures, given the high network delay required to reach remote servers. Multi-access Edge Computing (MEC) is the reference architecture for executing applications on nodes that are located close to users (i.e., at the edge of the network). This way, the network overhead is reduced but new challenges emerge. The resources available on edge nodes are limited, workloads fluctuate since users can rapidly change location, and complex tasks are becoming widespread (e.g., machine learning inference). To address these issues, this article presents NEPTUNE , a serverless-based framework that automates the management of large-scale MEC infrastructures. In particular, NEPTUNE provides (i) the placement of serverless functions on MEC nodes according to users’ location, (ii) the resolution of resource contention scenarios by avoiding that single nodes be saturated, and (iii) the dynamic allocation of CPUs and GPUs to meet foreseen execution times. To assess NEPTUNE , we built a prototype based on K3S, an edge-dedicated version of Kubernetes, and executed a comprehensive set of experiments. Results show that NEPTUNE obtains a significant reduction in terms of response time, network overhead, and resource consumption compared with five state-of-the-art solutions.
Baresi et al. (Mon,) studied this question.