Edge computing enables developers to deploy their services on compute resources deployed closer to the users. The abstraction requires powerful orchestration capabilities and the resolution of complex optimization problems. While edge computing is a consistently growing trend, the community (research and industry) still largely embraces adaptations and extensions of existing cloud technologies that have been proven ineffective on edge (e.g. Kubernetes). In this work, we present Oakestra, a novel hierarchical orchestration framework specifically designed for supporting service operation over heterogeneous edge infrastructures. In this demonstration, we showcase the various features and operations of Oakestra using our latency-critical augmented reality (AR) application.@en

Edge computing is an attractive platform where applications, previously hosted in the cloud, shift parts of their workload on resources closer to the users. The field is still in its nascent stages with significant ongoing innovation in small form-factor hardware designed to operate at the edge. However, the increased hardware heterogeneity at the edge makes it difficult for application developers to determine if their workloads will operate as desired. Simultaneously, edge providers have to make expensive deployment choices for the "correct" hardware that will remain suitable for the near future. We present ComB, an application-oriented benchmarking suite for edge that assists early adopters in evaluating the suitability of an edge deployment. ComB is flexible, extensible, and incorporates a microservice-based video analytics pipeline as default workload to measure underlying hardware's compute and networking capabilities accurately. Our evaluation on a heterogeneous testbed shows that ComB enables both providers and developers to understand better the runtime capabilities of different hardware configurations for supporting operations of applications designed for the edge.@en