碳感知的端到端数据移动
Carbon-Aware End-to-End Data Movement
AI总结：
针对端到端网络数据移动碳排放难以测量且常被忽视的问题，本文提出网络碳足迹测量机制，并基于地理碳强度从时间、空间和覆盖网络三方面优化任务调度以减少碳排放。
摘要：
云、边缘和分布式计算的最新采用趋势，以及 AI/ML 工作负载应用的增加，使得人们需要测量、监控并减少这些计算密集型工作负载的碳排放及相关通信成本。通过网络进行的数据移动会产生大量碳排放，但由于难以测量给定端到端网络路径的碳足迹，这一问题一直被忽视。我们提出了一种新颖的网络碳足迹测量机制，并给出三种方式，使用户能够优化网络密集型任务的调度：依据地理碳强度，在时间、空间和覆盖网络上转移任务，从而实现碳减排。
英文摘要：
The latest trends in the adoption of cloud, edge, and distributed computing, as well as a rise in applying AI/ML workloads, have created a need to measure, monitor, and reduce the carbon emissions of these compute-intensive workloads and the associated communication costs. The data movement over networks has considerable carbon emission that has been neglected due to the difficulty in measuring the carbon footprint of a given end-to-end network path. We present a novel network carbon footprint measuring mechanism and propose three ways in which users can optimize scheduling network-intensive tasks to enable carbon savings through shifting tasks in time, space, and overlay networks based on the geographic carbon intensity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
