AI-Enabled Secure Microservices in Edge Computing: Opportunities and Challenges

The paradigm of edge computing has formed an innovative scope within the domain of the Internet of Things (IoT) through expanding the services of the cloud to the network edge to design distributed architectures and securely enhance decision-making applications. Due to the heterogeneous, distributed and resource-constrained essence of edge Computing, edge applications are required to be developed as a set of lightweight and interdependent modules. As this concept aligns with the objectives of microservice architecture, effective implementation of microservices-based edge applications within IoT networks has the prospective of fully leveraging edge nodes capabilities. Deploying microservices at IoT edge faces plenty of challenges associated with security and privacy. Advances in Artificial Intelligence (AI) (especially Machine Learning), and the easy access to resources with powerful computing providing opportunities for deriving precise models and developing different intelligent applications at the edge of network. In this study, an extensive survey is presented for securing edge computing-based AI Microservices to elucidate the challenges of IoT management and enable secure decision-making systems at the edge. We present recent research studies on edge AI and microservices orchestration and highlight key requirements as well as challenges of securing Microservices at IoT edge. We also propose a Microservices-based edge computing framework that provides secure edge AI algorithms as Microservices utilizing the containerization technology to offer automated and secure AI-based applications at the network edge.

Automated summary

The paradigm of edge computing has created new possibilities for the Internet of Things (IoT), expanding cloud services to the network edge. This allows for the design of distributed architectures and the enhancement of decision-making applications. Edge computing faces challenges related to security and privacy, but advancements in artificial intelligence and machine learning provide opportunities for precise models and intelligent applications at the network edge. This study presents a comprehensive survey on securing edge computing-based AI microservices, highlighting key requirements and proposing a secure edge computing framework.