Lightweight Automation Agents for Serverless Computing: Bridging the Gap Between Cloud and Edge - Kumar , Anant; Jain, Kratika (Journal of Quantum Science and Technology (JQST), 2025)
Serverless computing, or Function-as-a-Service (FaaS), revolutionized cloud computing by enabling developers to concentrate on code development, with the infrastructure management abstracted away. As serverless applications, though, get deployed in more and more hybrid environments, bridging cloud and edge computing, issues like latency, resource scheduling, and workload management arise. The use of lightweight automation agents presents a strong solution to the issues, supporting smooth serverless function execution across cloud and edge infrastructures. These automation agents allow dynamic decision-making, automatically choosing where and when to run functions as a function of factors like network conditions, system load, and data proximity. Recent advances have centered around optimizing function placement, minimizing communication delays, and keeping costs low through workload offloading onto edge nodes where necessary. Over the past decade, research has explored various approaches to improving the efficiency of serverless systems by incorporating lightweight automation agents. These agents not only manage the orchestration of workloads between cloud and edge environments but also leverage machine learning and adaptive algorithms for predictive resource management. The role of federated learning and AI-driven automation is becoming more prominent in these systems, enabling autonomous decision-making and real-time adjustments. Despite these advancements, challenges such as system interoperability, security, and the fine-tuning of agent behaviors remain. As serverless and edge computing continue to evolve, lightweight automation agents will be crucial in addressing these issues, ensuring high performance, scalability, and minimal latency in distributed computing environments.