Geo-distributed data centers (DCs) provide important opportunities to improve operational sustainability through coordinated spatial workload migration and temporal workload shifting. However, stochastic workload arrivals, coupled with server operation, cooling dynamics, and inter-DC network constraints, make cost-optimal scheduling highly challenging. To address this issue, a two-stage stochastic scheduling framework is proposed herein that jointly coordinates inflexible, temporal-shiftable, and spatially migratable workloads across multiple DCs and time slots. Workload uncertainty is efficiently handled through Latin hypercube sampling and fast forward scenario reduction. Numerical experiments on a three-DC system demonstrate that the proposed framework reshapes workload distribution across time and space, reducing total energy consumption by approximately 3.7% and total operating cost by about 6.7% compared with the baseline case without flexibility, while maintaining stable server utilization under increasing workload uncertainty. These results demonstrate that the framework provides an effective and practically implementable approach for economically efficient and energy-efficient scheduling of geo-distributed DCs, thereby supporting sustainable operation through coordinated workload flexibility and more efficient utilization of computing and energy resources.

Considering the limited availability of meteorological data in practice, this paper investigates the short-term photovoltaic output prediction problem based on data decomposition and error correction to further improve prediction accuracy. Firstly, according to the analysis of the variation characteristics of photovoltaic output data, the Seasonal and Trend decomposition using Loess (STL) method is used to decompose the original data into three components: seasonal term, trend term, and residual term. Considering that the variation patterns of different components are different, based on the division of the dataset, temporal convolutional network (TCN)-based prediction models for each component are constructed separately, and the prediction results are superimposed to obtain the predicted value of the photovoltaic output. Secondly, an error dataset is constructed based on the prediction errors of the training set and validation set, and a TCN error prediction model is established. The error prediction value is used as compensation to correct the photovoltaic output prediction value, and the final photovoltaic output prediction value is obtained. Finally, based on the measured photovoltaic output data of a certain region in China, the effectiveness and advancement of the proposed method are demonstrated through the ablation and comparative experiments.