The consumption of energy by data center networks has increased markedly in recent times, becoming a matter of concern for all sections of society. It is essential to develop comprehensive models and optimize energy consumption in data centers. As the Internet of Things (IoT) industry experiences accelerated growth, predictive modeling of data centers utilizing IoT devices to gather real-time data and incorporating datadriven deep learning techniques has demonstrated an enhanced degree of accuracy. The functionality of existing deep learning models is limited in real data center scenarios, where concept drift is a common occurrence. This results in a reduction in the predictive accuracy of the models. In this paper, we propose a time series prediction framework for data center scenarios. Under this framework, online prediction and offline training can be implemented to effectively address the frequent concept drift phenomenon in data center scenarios and enhance the stability of prediction. Extensive experiments are conducted in this study using real datasets obtained from IoT device measurements. The experiment results show that the proposed time series prediction framework can achieve good performance in data center scenarios when facing the concept drift.

Deep learning methods for accurately predicting data center status, which are essential for addressing the exponential growth of energy consumption, have gained significant attention, driven by the vast amounts of data collected through the advancement of Internet of Things (IoT) technologies. However, conventional small models often face data scarcity issues in practical deployment. While large models show promise in addressing this challenge, they encounter obstacles, such as multivariate tasks, computational intensity, and ineffective information capture. Moreover, their applications in data centers remain largely unexplored. In this article, we investigate local semantic capture empowered large model for multivariate time series forecasting in IoT-enabled data centers. We first introduce time series tasks within data centers and propose the Point Lag (Plag)-Llama framework with the Lag-Llama backbone to support zero-shot forecasting and fine-tuning for multivariate point time series forecasting. To address computational intensity and enhance the capabilities of multivariate forecasting, we propose the local semantic capture (LOSEC) for adapter fine-tuning, which captures local semantic information across time and channel dimensions alternately with low-complexity. Specifically, time series are patched into tokens, and channels are clustered together, forming local semantic information that can be captured more effectively. Extensive experiments demonstrate that Plag-Llama exhibits superior zero-shot capability and that the LOSEC empowered adapter fine-tuning achieves state-of-the-art performance on real-world datasets collected from data centers, with ablation studies further validating the effectiveness of each module within the proposed models.

By harnessing advanced Internet-of-Things (IoT) technologies, deep-learning methods have garnered significant attention for accurately predicting data center status, serving as the cornerstone for mitigating the exponential growth of energy consumption in data centers. However, these methods encounter data scarcity issues during practical deployment. While the proliferation of large models holds promise for addressing this challenge, their application in the context of data centers remains largely unexplored. Furthermore, these models encounter diverse obstacles, including multivariate tasks, immersive computation, and so on. In this paper, we investigate multivariate time series forecasting in IoT-enabled data centers by harnessing large models. Specifically, we introduce a multivariate time series forecasting framework tailored for IoT-enabled data centers. We propose the point Lag (Plag)-Llama model, which transfers univariate forecasting knowledge from the Lag-Llama for multivariate forecasting. The Plag-Llama benefits from zero-shot ability and fine-tuning facilitated by our proposed transfer block. To mitigate computational intensity and enhance the Plag-Llama’s performance, we introduce a novel Joint Channel-Time (JCT)-adapter fine-tuning technique. Extensive experiments demonstrate that the transferred Plag-Llama exhibits superior zero-shot ability, while the proposed JCT-adapter fine-tuning achieves state-of-the-art performance and remarkable few-shot ability on real-world datasets collected from data centers.

The increasingly severe energy consumption caused by exploding wireless demands attracts considerable research. Remarkably, base station (BS) sleeping is a promising technique to enable the green network. A disruptive and original fully-decoupled radio access network (FD-RAN) architecture aiming at the next-generation mobile communication networks is developed, which removes the obstacles to achieving BS sleeping, i.e. deficient cooperation between BSs, coupled data-control transmission and coupled uplink-downlink transmission. In this paper, we investigate the joint user association and uplink BS sleeping considering power control in the FD-RAN with the superiority of fully decoupled architectures. Specifically, we propose an energy consumption model for the uplink FD-RAN and tackle the mixed-integer second-order cone problem to minimize the whole network energy consumption by leveraging the many-to-many swap matching theory. Extensive simulation results validate a higher energy efficiency of the uplink FD-RAN compared to the traditional cellular network and cell-free networks and demonstrate the effectiveness of our proposed algorithm.

With the development of the Internet of Everything (IoE), future 6G networks will face the challenge of massive terminal access. However, deploying substantial high-cost, full-function base stations will undoubtedly further increase the cost of mobile network deployment, making it difficult for mobile operators to afford it. In this paper, we tackle the problem of low-cost network deployment for fully-decoupled radio access network (FD-RAN) with personalized service for large-scale terminals. We first propose a techno-economic cost model (TECM) for FD-RAN deployment based on the techno-economic approach. Then, we further formulate a cost-minimization problem for decoupled network deployment. Based on the independence brought by uplink and downlink decoupling in FD-RANs, we decompose the original problem into separate subproblems for uplink and downlink network deployment. In the following, we propose a branch and cut based network deployment (BCND) algorithm to solve two decoupled deployment subproblems, respectively. Finally, simulation results show that FD-RANs have significant cost advantages when facing differentiated service demands, and the main factors affecting network cost are power consumption and rental costs.