A Serverless Real-Time Data Analytics Platform for Edge Computing

TLDR

Current cloud‑supported edge analytics rely on rigid bottom‑up methods that ignore data origin, forcing developers to craft ad hoc solutions for each edge device. The study proposes a serverless edge‑data analytics platform to enable real‑time analytics in edge computing. The platform is built on a serverless architecture with an application model that addresses design requirements and challenges illustrated by real‑life healthcare use cases.

Abstract

Contemporary solutions for cloud-supported, edge-data analytics mostly apply analytics techniques in a rigid bottom-up approach, regardless of the data's origin. Typically, data are generated at the edge of the infrastructure and transmitted to the cloud, where traditional data analytics techniques are applied. Currently, developers are forced to resort to ad hoc solutions specifically tailored for the available infrastructure (for example, edge devices) when designing, developing, and operating the data analytics applications. Here, a novel approach implements cloud-supported, real-time data analytics in edge-computing applications. The authors introduce their serverless edge-data analytics platform and application model and discuss their main design requirements and challenges, based on real-life healthcare use case scenarios.