论文标题
绿色计算网络的碳中和任务计划
Carbon-Neutralized Task Scheduling for Green Computing Networks
论文作者
论文摘要
由于人类活动的碳排放量增加而引起的气候变化已被确定为对地球最关键的威胁之一。碳中和作为扭转气候变化的关键方法，引发了新法规的制定，以实施对低碳解决方案的经济活动。使用户能够处理计算密集型任务的计算网络由于能源消耗的增加而造成了大量的碳排放。为了通过调度策略分析可实现的碳排放量，我们首先提出了一种新颖的虚拟排队网络模型，该模型捕获网络中的通信和计算过程。为了适应通过计算网络使用的可再生能源的高度可变和不可预测的性质（即网格的碳强度随时间和位置而变化），我们提出了一种基于碳强度的新型调度策略，该策略可以通过lyapunov优化中的漂移 - penalty方法来动态地安排计算任务，以使云通过云进行计算。我们使用现实世界数据的数值分析表明，与基于队列长度的策略相比，所提出的策略对AI模型培训任务的累积碳排放量减少了54％。
Climate change due to increasing carbon emissions by human activities has been identified as one of the most critical threat to Earth. Carbon neutralization, as a key approach to reverse climate change, has triggered the development of new regulations to enforce the economic activities toward low carbon solutions. Computing networks that enable users to process computation-intensive tasks contribute huge amount of carbon emissions due to rising energy consumption. To analyze the achievable reduction of carbon emissions by a scheduling policy, we first propose a novel virtual queueing network model that captures communication and computing procedures in networks. To adapt to highly variable and unpredictable nature of renewable energy utilized by computing networks (i.e., carbon intensity of grid varies by time and location), we propose a novel carbon-intensity based scheduling policy that dynamically schedules computation tasks over clouds via the drift-plus-penalty methodology in Lyapunov optimization. Our numerical analysis using real-world data shows that the proposed policy achieves 54% reduction on the cumulative carbon emissions for AI model training tasks compared to the queue-length based policy.
