Autonomous Lifecycle Management for Resource-Efficient Workload Orchestration for Green Edge Computing

Edge computing is an important pillar for green computation by bringing the Cloud resources to the Edge, serving real-time applications, and reducing the computing and network resources required to transfer data for processing in the Cloud. 5G brings network densification and enables massive IoT and V2X applications, which triggers the need for edge computing to host network functions and user-facing services in a converged edge platform(s). Several edge computing deployments are being observed by ecosystem players (telco, ISVs, chip vendors, CSPs, … etc.) for IoT or V2X services, however, focusing on converged network functions and services. The point that is still in its early stages is the dynamic workload orchestration across the converged edge platforms running network functions and multi-tenant IoT services with different compute requirements and different Service Level Objectives (SLOs). This paper focuses on autonomous life cycle management for converged edge platform(s) to enable resource-efficient workload orchestration, contributing to the green goal. We present a solution for intelligent dynamic resources configuration on edge computing platforms hosting multi-tenant services while guaranteeing the SLO for each service and helping green communication goal. The presented solution has been deployed in a trial, and we present results on efficient resources configuration.