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This paper presents a comprehensive taxonomy of carbon-aware resource management strategies tailored for latency-sensitive cloud computing environments, addressing the growing conflict between performance optimization and carbon footprint reduction. By analyzing existing literature, the authors identify critical gaps in current methodologies and propose future research directions to enhance sustainability without compromising latency requirements. The findings underscore the urgent need for innovative approaches that align cloud infrastructure scaling with climate goals, particularly as demand for latency-sensitive applications surges.
Cloud computing's carbon footprint could be significantly reduced without sacrificing latency, but existing strategies fall short of this goal.
Proliferation of cloud-based latency-sensitive workloads requires infrastructures tuned to their workload-specific latency constraints. Today, they shape the cloud from a generalized computing platform to diverse workload-specific cloud environments. As the demand for latency-sensitive workloads increases, cloud service providers continue to scale their infrastructure, adversely increasing the carbon footprint and challenging climate-crisis-driven net-zero emission goals. Due to performance-oriented rigid deployment patterns of latency-optimizations, reducing its carbon footprint is challenging. Therefore, efficient techniques that exploit application specific opportunities are needed in that. To this end, we present a detailed taxonomy of recent literature on carbon-aware resource management in latency-sensitive cloud computing environments. Using the taxonomy, we analyze existing works discussing their optimization aspects, identify the gaps, and highlight future research directions.
