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Abstract The rapid advancement of drone technology and digital twin systems has significantly transformed environmental monitoring, particularly in the field of water quality assessment. This paper systematically reviews the current state of research on the application of drones, digital twins, and their integration for water quality monitoring and management. It highlights key themes, insights, research trends, commonly used methodologies, and future directions from existing studies, aiming to provide a foundational reference for further research to harness the promising potential of these technologies for effective, scalable solutions in water resource management, addressing both immediate and long‐term environmental challenges. The systematic review followed PRISMA guidelines, rigorously analysing hundreds of relevant papers. Key findings emphasise the effectiveness of drones in capturing real‐time, high‐resolution spatial and temporal data, as well as the value of digital twins for predictive and simulation‐based analysis. Most importantly, the review demonstrates the potential of integrating these technologies to enhance sustainable water management practices. However, it also identifies a significant research gap in fully integrating drones with digital twins for comprehensive water quality management. In response, the review outlines future research directions, including improvements in data integration techniques, predictive models, and interdisciplinary collaboration. 

During the past few years, serverless computing has changed the paradigm of application development and deployment in the cloud and edge due to its unique advantages, including easy administration, automatic scaling, built-in fault tolerance, etc. Nevertheless, serverless computing is also facing challenges such as long latency due to the cold start. In this paper, we present an in-depth performance analysis of cold start in the serverless framework and propose HotC, a container-based runtime management framework that leverages the lightweight containers to mitigate the cold start and improve the network performance of serverless applications. HotC maintains a live container runtime pool, analyzes the user input or configuration file, and provides available runtime for immediate reuse. To precisely predict the request and efficiently manage the hot containers, we design an adaptive live container control algorithm combining the exponential smoothing model and Markov chain method. Our evaluation results show that HotC introduces negligible overhead and can efficiently improve the performance of various applications with different network traffic patterns in both cloud servers and edge devices.