AI augmented Edge and Fog computing: Trends and challenges

卷:216

Review

关键词: FAULT-TOLERANCE;    WORKLOAD PREDICTION;    RESOURCE-MANAGEMENT;    INDUSTRIAL INTERNET;    ENERGY EFFICIENCY;    ANOMALY DETECTION;    LEARNING APPROACH;    CLOUD;    OPTIMIZATION;    IOT;   

DOI  :  10.1016/j.jnca.2023.103648

来源: SCIE

【 摘 要 】

In recent years, the landscape of computing paradigms has witnessed a gradual yet remarkable shift from monolithic computing to distributed and decentralized paradigms such as Internet of Things (IoT), Edge, Fog, Cloud, and Serverless. The frontiers of these computing technologies have been boosted by shift from manually encoded algorithms to Artificial Intelligence (AI)-driven autonomous systems for optimum and reliable management of distributed computing resources. Prior work focuses on improving existing systems using AI across a wide range of domains, such as efficient resource provisioning, application deployment, task placement, and service management. This survey reviews the evolution of data-driven AI-augmented technologies and their impact on computing systems. We demystify new techniques and draw key insights in Edge, Fog and Cloud resource management-related uses of AI methods and also look at how AI can innovate traditional applications for enhanced Quality of Service (QoS) in the presence of a continuum of resources. We present the latest trends and impact areas such as optimizing AI models that are deployed on or for computing systems. We layout a roadmap for future research directions in areas such as resource management for QoS optimization and service reliability. Finally, we discuss blue-sky ideas and envision this work as an anchor point for future research on AI-driven computing systems.

【 授权许可】

Free