Provisioning dynamic machine learning (ML) inference as a service for
artificial intelligence (AI) applications of edge devices faces many
challenges, including the trade-off among accuracy loss, carbon emission, and
unknown future costs. Besides, many governments are launching carbon emission
rights (CER) for operators to reduce carbon emissions further to reverse
climate change. Facing these challenges, to achieve carbon-aware ML task
offloading under limited carbon emission rights thus to achieve green edge AI,
we establish a joint ML task offloading and CER purchasing problem, intending
to minimize the accuracy loss under the long-term time-averaged cost budget of
purchasing the required CER. However, considering the uncertainty of the
resource prices, the CER purchasing prices, the carbon intensity of sites, and
ML tasks' arrivals, it is hard to decide the optimal policy online over a
long-running period time. To overcome this difficulty, we leverage the
two-timescale Lyapunov optimization technique, of which the $T$-slot
drift-plus-penalty methodology inspires us to propose an online algorithm that
purchases CER in multiple timescales (on-preserved in carbon future market and
on-demanded in the carbon spot market) and makes decisions about where to
offload ML tasks. Considering the NP-hardness of the $T$-slot problems, we
further propose the resource-restricted randomized dependent rounding algorithm
to help to gain the near-optimal solution with no help of any future
information. Our theoretical analysis and extensive simulation results driven
by the real carbon intensity trace show the superior performance of the
proposed algorithms.

研究如何实现有限碳排放的碳感知机器学习任务卸载和碳排放权购买，提出基于 Lyapunov 优化技术的在线算法，结合资源限制随机依赖舍入算法，以实现绿色边缘 AI。