Service Deployment Challenges in Cloud-to-Edge Continuum

Abstract

This position paper aims to identify the current and future challenges in application, workload or service deployment mechanisms in Cloud-to-Edge environments. We argue that the adoption of the microservices and unikernels on large scale is adding new entries on the list of requirements of a deployment mechanism, but offers an opportunity to decentralize the associated processes and improve the scalability of the applications. Moreover, the deployment in Cloud-to-Edge environment needs the support of federated machine learning.

A Reference Architecture for Cloud–Edge Meta-Operating Systems Enabling Cross-Domain, Data-Intensive, ML-Assisted Applications: Architectural Overview and Key Concepts

“…These data-intensive applications are required to operate across the cloudto-edge continuum, where cloud, edge, core network, radio-access network, sensors and data itself coexist and collaborate [5][6][7]. While today numerous proprietary and open source technical tools support IoT-to-edge-to-cloud scenarios as a single commodity, solutions that are able to truly bring computation and intelligence closer to the edge nodes, where the data are generated, are still facing several technical, societal and business barriers [8]. The main complication regarding this transition is that current solutions solely focus on the hierarchical orchestration of resources, services and data, necessitating data migration from IoT-to-edge-to-cloud, and vice versa [9][10][11][12].…”