Carbon-aware Resource Management for Latency-Sensitive Cloud Computing Environments: A Taxonomy and Future Directions

Abstract

The proliferation of cloud-based latency-sensitive workloads, such as autonomous driving, real-time healthcare services, industrial automation, immersive interactive services, and intelligent AI-based applications requires infrastructures tuned to meet stringent latency constraints. Today, these workloads are reshaping the cloud from a general-purpose computing platform into a diverse ecosystem of workload-specific cloud environments. As demand for latency-sensitive workloads grows, cloud providers must scale their infrastructures accordingly, adversely increasing the carbon footprint and challenging net-zero goals. However, the strict performance requirements and rigid deployment patterns used to satisfy latency Service Level Objectives (SLOs) constrains conventional carbon optimization. Therefore, novel carbon-aware techniques are needed to exploit application-specific opportunities for reducing both operational and embodied carbon footprints while preserving latency guarantees. This survey examines carbon-aware resource management for latency-sensitive cloud computing environments. We introduce a holistic taxonomy that organizes existing techniques by carbon management objective, workload management mechanism, control architecture, system tier, latency requirements, and deployment stage. Then, we use this taxonomy to classify and critically analyze existing approaches, revealing the interactions and trade-offs between latency guarantees and operational and embodied carbon reduction. Finally, we identify limitations in the literature and present a structured research agenda for the design of sustainable latency-sensitive cloud systems.