Market Power, Intertemporal Permits Trading, and Economic Efficiency

Jiang, Minxing; Feng, Xingliang; Liang, Li

doi:10.3389/fenrg.2021.704556

Cited by 6 publications

(1 citation statement)

References 28 publications

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“…Carbon emission rights is proposed as a tradable commodity to achieve emission reduction target [25]. and the research efforts in this area are increasing.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Task Scheduling in Cloud-Assisted Mobile Edge Computing

Zhou

Zhang

et al. 2023

IEEE Trans. on Mobile Comput.

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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-pluspenalty 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.

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“…Carbon emission rights is proposed as a tradable commodity to achieve emission reduction target [25]. and the research efforts in this area are increasing.…”

Section: Introductionmentioning

confidence: 99%