Less Carbon Footprint in Edge Computing by Joint Task Offloading and Energy Sharing

Yu, Zhanwei; Zhao, Yi; Deng, Tao; Li, You; Yuan, Di

doi:10.48550/arxiv.2211.05529

Cited by 2 publications

(2 citation statements)

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“…To reduce the carbon emission in a scheduling policy, Yang et al [21] devoted to leveraging Lyapunov optimization to adapt to renewable energy's nature of variable and unpredictable and design a scheduling policy based on carbon intensity. In [22], Yu et al studied the minimization of the carbon footprint of the task offloading oriented by carbon footprint considering energy sharing as well as battery charging. Savazzi et al [23] aimed at designing green federated learning which is distributed and the proposed framework can quantify the energy footprints and the carbon equivalent emissions.…”

Section: Introductionmentioning

confidence: 99%

“…Lannelongue et al [24] researched a methodological framework to help welly achieve the estimation of the computational task's carbon footprint standardized and reliably. Works of [21] and [22] mainly focused on exploring the advantages of renewable energy and may be overly dependent on green energy, while works of [23] and [24] looked into the quantization of carbon footprints, the results of which may be less accurate and fail to be used in reality.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%