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.

The deployment and management of Blockchain applications require non-trivial efforts given the unique characteristics of their infrastructure (i.e., immutability) and the complexity of the software systems being executed. The operation of Blockchain applications is still based on ad-hoc solutions that are error-prone, difficult to maintain and evolve, and do not manage their interactions with other infrastructures (e.g., a Cloud backend).

Nowadays a wide range of applications is constrained by low-latency requirements that cloud infrastructures cannot meet. Multiaccess Edge Computing (MEC) has been proposed as the reference architecture for executing applications closer to users and reducing latency, but new challenges arise: edge nodes are resource-constrained, the workload can vary significantly since users are nomadic, and task complexity is increasing (e.g., machine learning inference). To overcome these problems, the paper presents NEPTUNE, a serverless-based framework for managing complex MEC solutions. NEPTUNE i) places functions on edge nodes according to user locations, ii) avoids the saturation of single nodes, iii) exploits GPUs when available, and iv) allocates resources (CPU cores) dynamically to meet foreseen execution times. A prototype, built on top of K3S, was used to evaluate NEPTUNE on a set of experiments that demonstrate a significant reduction in terms of response time, network overhead, and resource consumption compared to three well-known approaches.