Towards Energy Efficiency and Power Trading Exploiting Renewable Energy in Cloud Data Centers

Aslam, Shahzad; Herodotou, Herodotos; Mohsin, Syed Muhammad; Aurangzeb, Khursheed

doi:10.1109/aect47998.2020.9194169

Cited by 12 publications

(4 citation statements)

References 18 publications

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“…The coal-based energy, known as brown energy, will emit carbon footprint into the environment. Although RES is assumed to generate 0 carbon emission [22], this paper follows the settings in [23], [24], considering a more realistic scenario where the CFR value of wind is treated as a constant.…”

Section: Carbon Emission Modelmentioning

confidence: 99%

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CFWS: DRL-Based Framework for Energy Cost and Carbon Footprint Optimization in Cloud Data Centers

Zhao,

Zhou,

2024

IEEE Trans. Sustain. Comput.

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The rapid growth and widespread adoption of cloud computing have led to significant electricity costs and environmental impacts. Traditional approaches that rely on static utilization thresholds are ineffective in dynamic cloud environments, and simply consolidating virtual machines (VMs) to minimize energy costs does not necessarily result in the lowest carbon footprints. In this paper, a deep reinforcement learning (DRL) based framework called CFWS is proposed to enhance the energy efficiency of renewable energy sources (RES) supplied data centers (DCs). CFWS incorporates an adaptive thresholds adjustment method TCN-MAD by evaluating the predicted probability of a physical machine (PM) being overloaded to prevent unnecessary VM migrations and mitigate service level agreement (SLA) violations due to imbalanced workload distribution. Additionally, CFWS introduces a novel action space in the DRL algorithm by representing VM migrations among geo-distributed cloud data centers as flattened indices to accelerate its execution efficiency. Simulation results demonstrate that CFWS can achieve a superior optimization of energy costs and carbon footprints, saving 5.67% to 13.22% brown energy with maximized RES utilization. Furthermore, CFWS reduces VM migrations by up to 86.53% and maintains the lowest SLA violations within suboptimal execution time in comparison to the state-of-art algorithms.

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Section: Carbon Emission Modelmentioning

confidence: 99%

“…Among the four data centers, Oregon emits the least carbon footprint when consuming the same amount of energy. For the CFR of wind, it is assumed to be 0.0225 (Ton/MWh) as mentioned in [23]. For the PUEs of geo-distributed cloud data centers, this paper considers their dynamics as modeled in Eq.…”

Section: Carbon Footprint Rate and Puementioning

confidence: 99%

CFWS: DRL-Based Framework for Energy Cost and Carbon Footprint Optimization in Cloud Data Centers

Zhao,

Zhou,

2024

IEEE Trans. Sustain. Comput.

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“…Initiatives to carbonize DE are anticipated to pick up speed in the upcoming years to lessen the consequences of climate change. These models capture the dynamic and complicated energy landscape that is rapidly advancing technology, shifting consumer preferences, and shifting governmental policies to create a reliable and maintainable global energy system; responding to these vicissitudes and addressing the related questions will be essential [13].…”

Section: Figure 2: Global Energy Consumptionmentioning

confidence: 99%

Integrating Renewable Energy Sources into Cloud Computing Data Centers: Challenges and Solutions

Satish,

Meduri

2024

Int. J. Res. Publ. Rev.

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“…ESSs enable energy storage and subsequent discharge when required, which meet energy demand, improve power quality, and augment grid flexibility [7]. Additionally, Demand-Side Management (DSM) is also crucial in ensuring power grid stability and improving energy efficiency [8] [9].…”

Section: Introductionmentioning

confidence: 99%

Multi-Task Learning of the PatchTCN-TST Model for Short-Term Multi-Load Energy Forecasting Considering Indoor Environments in a Smart Building

Cen,

Lim

2024

IEEE Access

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Energy consumption in buildings contributes to over a third of global energy consumption and 28% of greenhouse gas emissions. With urbanization and population growth, rising building energy demand can lead to environmental degradation. While significant renewable resources are used to generate electricity to mitigate environmental problems, demand-side management remains crucial for achieving net-zero emissions and enhancing energy efficiency. Accurate building load forecasting is pivotal in devising optimal demand response schemes to shift or reduce the demand on power grids. Recent studies have achieved progressive breakthroughs in building energy forecasting through machine learning algorithms. However, most studies focused on building-level energy forecasting rather than individual load forecasting, which cannot support controlled demand response programs. In this study, we propose a multi-task learning model incorporating Patch, Temporal Convolutional Network and Time-Series Transformer (PatchTCN-TST) based on the channel-independent strategy for floor-level multiple electricity loads and indoor environmental forecasting. The PatchTCN-TST model is implemented to predict future data ranging from one-step ahead to three-step ahead on a real-world office building in Bangkok, Thailand. The experiment results indicate that the prediction performance of our model outperforms the prevalent methods, including LSTM, GRU, TCN, Transformer, Informer and Autoformer. The PatchTCN-TST model demonstrates superior accuracy in three forecasting scenarios, significantly reducing MAE, MSE, RMSE, and aSMAPE by 34%, 23%, 12%, and 36.4%, respectively, compared to the best baseline model.INDEX TERMS Deep learning, demand response, time-series forecasting, smart buildings.

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Towards Energy Efficiency and Power Trading Exploiting Renewable Energy in Cloud Data Centers

Cited by 12 publications

References 18 publications

CFWS: DRL-Based Framework for Energy Cost and Carbon Footprint Optimization in Cloud Data Centers

CFWS: DRL-Based Framework for Energy Cost and Carbon Footprint Optimization in Cloud Data Centers

Integrating Renewable Energy Sources into Cloud Computing Data Centers: Challenges and Solutions

Multi-Task Learning of the PatchTCN-TST Model for Short-Term Multi-Load Energy Forecasting Considering Indoor Environments in a Smart Building

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