论文标题
通过联合任务卸载和能量共享，边缘计算中的碳足迹较少
Less Carbon Footprint in Edge Computing by Joint Task Offloading and Energy Sharing
论文作者
论文摘要
在Sprite中，通信系统中的最先进的碳足迹（CF）大大减少了。我们在边缘计算的背景下应对这一挑战。电力供应的碳强度在空间上和时间上都在很大程度上变化。这与通过电池管理系统（BMS）进行的能源共享通过重新分配了时空和空间的计算任务，证明了面向CF的任务卸载的潜力。在本文中，我们考虑最佳的任务调度和卸载，以及电池充电以最大程度地减少总CF。我们将此CF最小化问题提出为整数线性编程模型。但是，我们证明，通过基于图的重新制定，可以将问题作为最低成本流问题施加。这一发现表明，在多项式时间内可以接收全局最佳。使用现实世界数据的数值结果表明，优化可以降低总CF的83.3％。
In sprite the state-of-the-art, significantly reducing carbon footprint (CF) in communications systems remains urgent. We address this challenge in the context of edge computing. The carbon intensity of electricity supply largely varies spatially as well as temporally. This, together with energy sharing via a battery management system (BMS), justifies the potential of CF-oriented task offloading, by redistributing the computational tasks in time and space. In this paper, we consider optimal task scheduling and offloading, as well as battery charging to minimize the total CF. We formulate this CF minimization problem as an integer linear programming model. However, we demonstrate that, via a graph-based reformulation, the problem can be cast as a minimum-cost flow problem. This finding reveals that global optimum can be admitted in polynomial time. Numerical results using real-world data show that optimization can reduce up to 83.3% of the total CF.
