Spatio-Temporal Coordinated Operation Strategy of Data Centers Considering Virtual Storage System via Two-Stage Distributionally Robust Optimization

IF 7.3 2区计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY

IEEE Transactions on Network Science and Engineering Pub Date : 2026-02-17 DOI:10.1109/TNSE.2026.3665604

Xuexue Qin;Zening Li;Zhengmao Li;Yixun Xue;Xinyue Chang;Jia Su;Xiaolong Jin;Peng Wang;Hongbin Sun

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引用次数: 0

Abstract

Energy consumption in data centers is growing rapidly in recent years. To realize the economic and flexible operation of data centers, this paper proposes a spatio-temporal coordinated operation strategy of data centers considering virtual storage system (VSS) via two-stage distributionally robust optimization. First, based on the dynamic thermal network of buildings and differentiated demands of cloud users in data centers, a comprehensive operation model with VSS is proposed. Then, considering uncertainties of general load demands from data centers and outdoor temperatures, a fuzzy set is deduced, using an imprecise Dirichlet model derived from historical data. This ensures confidence level satisfaction for uncertain sets of general load and outdoor temperatures. In addition, the spatio-temporal coordinated operation strategy of data centers considering virtual storage system via two-stage distributionally robust optimization that flexibly adjusts conservativeness through uncertainty regulation parameters. Finally, a Dynamic Accuracy Column and Constraint Generation (DA-C&CG) algorithm is developed for solving the proposed strategy. The results indicate that the proposed strategy can effectively enhance operational economics, computing efficiency and resilience by leveraging flexibility in cooling and cloud user loads within suitable server temperatures. Meanwhile, the DA-C&CG algorithm exhibits excellent solution performance.

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基于两阶段分布鲁棒优化的虚拟存储数据中心时空协同运行策略

近年来，数据中心的能耗增长迅速。为实现数据中心的经济灵活运行，通过两阶段分布鲁棒优化，提出了一种考虑虚拟存储系统（VSS）的数据中心时空协同运行策略。首先，基于建筑动态热网络和数据中心云用户的差异化需求，提出了一种基于VSS的综合运营模型。然后，考虑数据中心一般负荷需求和室外温度的不确定性，利用历史数据导出的不精确狄利克雷模型推导出模糊集。这确保了一般负载和室外温度的不确定设置的置信度满意。此外，通过两阶段分布鲁棒优化，通过不确定性调节参数灵活调整保守性，提出了考虑虚拟存储系统的数据中心时空协同运行策略。最后，提出了一种动态精度列和约束生成（DA-C&CG）算法来求解该策略。结果表明，该策略通过在适当的服务器温度下利用冷却和云用户负载的灵活性，可以有效地提高运营经济性、计算效率和弹性。同时，DA-C&CG算法表现出优异的求解性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Network Science and Engineering Engineering-Control and Systems Engineering

CiteScore

12.60

自引率

9.10%

发文量

393

期刊介绍： The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.