面向延迟敏感型云计算环境的碳感知资源管理：分类与未来方向
Carbon-aware Resource Management for Latency-Sensitive Cloud Computing Environments: A Taxonomy and Future Directions
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- University of Melbourne（墨尔本大学）
- University of Amsterdam（阿姆斯特丹大学）
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中文总结 AI 辅助
本文针对延迟敏感型云计算环境的碳感知资源管理问题，对相关最新文献进行分类分析，指出现有研究差距并提出未来研究方向，助力平衡延迟约束与碳减排目标。
中文摘要 AI 辅助
基于云的延迟敏感型工作负载的激增，要求基础设施针对其特定工作负载的延迟约束进行调整。如今，这些工作负载正将云计算从通用计算平台塑造成各类特定工作负载的云环境。随着延迟敏感型工作负载需求的增长，云服务提供商持续扩展基础设施，这会不利地增加碳足迹，对气候危机驱动的净零排放目标构成挑战。由于延迟优化采用面向性能的刚性部署模式，减少其碳足迹颇具挑战性。因此，需要能利用应用特定机会的高效技术。为此，本文对延迟敏感型云计算环境中碳感知资源管理的最新文献进行了详细分类，利用该分类法分析现有工作的优化方面，识别其中的差距，并强调未来研究方向。
英文摘要
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.
