Security-aware Deployment Optimization of Cloud-Edge systems in Industrial IoT

Abstract

Cloud computing, Edge computing and IoT are significantly changing from the original architectural models with a pure provisioning of virtual resources (and services) to a transparent and adaptive hosting environment where cloud providers, as well as “on-premise” resources and end-nodes, fully realize the “everything-as-a-service” provisioning concept. The optimal design of these architectures, including the selection of optimal services to acquire, is not-trivial in the Cloud-Edge context, due to the involvement of a variable number and type of available resources offerings and to the impact on cost, performance and other relevant features, such as security, almost never considered. This paper presents a novel formalization of the Cloud-Edge allocation problem for the Industrial IoT context. The proposed optimization process takes explicitly into account two critical aspects that are often overlooked in similar approaches, namely the new cloud-edge on-demand service offerings model for the allocation of resources and the impact on the deployed application, in terms of cost, performance and security policies actually implemented. An efficient, yet suboptimal deterministic solver is also presented, and compared with a linear programming one. Results are the same in 86% of the cases on the considered dataset, while our solver is orders of magnitude faster than the linear one.