Reducing Environmental Impact with Sustainable Serverless Computing

Mohammed Akour () and Mamdouh Alenezi
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Mohammed Akour: College of Computer and Information Sciences, Prince Sultan University, Riyadh 12435, Saudi Arabia
Mamdouh Alenezi: The Saudi Technology and Security Comprehensive Control Company (Tahakom), Riyadh 12435, Saudi Arabia

Sustainability, 2025, vol. 17, issue 7, 1-23

Abstract: Serverless computing has emerged as a transformative paradigm in cloud computing, offering scalability, cost efficiency, and potential environmental benefits. By abstracting infrastructure management and enabling on-demand resource allocation, serverless computing minimizes idle resource consumption and reduces operational overhead. This paper critically examines the sustainability implications of serverless computing, evaluating its impact on energy efficiency, resource utilization, and carbon emissions through empirical studies and a survey of cloud professionals. Our findings indicate that serverless computing significantly reduces energy consumption by up to 70% and operational costs by up to 60%, reinforcing its role in green IT initiatives. However, real-world deployments face challenges such as cold-start latency and workload-dependent inefficiencies, which impact overall sustainability benefits. To address these challenges, we propose strategic recommendations, including fine-grained function decomposition, energy-efficient cloud provider selection, and AI-driven resource management. Additionally, we highlight discrepancies between empirical research and practitioner experiences, emphasizing the need for optimized architectures in order to fully harness the sustainability potential of serverless computing. This study provides a foundation for future research, particularly in integrating machine learning and AI-driven optimizations to enhance energy efficiency and performance in serverless environments.

Keywords: serverless computing; sustainability; green IT; energy efficiency; carbon footprint; sustainable software practices; e-waste reduction; AI optimization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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