Data centers are experiencing unprecedented growth as the fourth industrial revolution’s supporting pillars and the engine for the future digitalized world. However, data centers are carbon-intensive enterprises due to their massive energy consumption, and it is estimated that data center industry will account for 8% of global carbon emissions by 2030. Meanwhile, both technological and policy instruments for reducing or even neutralizing data center carbon emissions have not been thoroughly investigated, despite the fact that several global cloud providers including Google and Facebook, have pledged to achieve carbon neutrality in their hyperscale data centers. To bridge this gap, this survey paper proposes a roadmap towards carbon-neutral data centers that takes into account both policy instruments and technological methodologies. We begin by presenting the carbon footprint of data centers, as well as some insights into the major sources of carbon emissions. Following that, carbon neutrality plans for major global cloud providers are discussed to summarize current industrial efforts in this direction. In what follows, we introduce the carbon market as a policy instrument to explain how to offset data center carbon emissions in a cost-efficient manner. On the technological front, we propose achieving carbon-neutral data centers by increasing renewable energy penetration, improving energy efficiency, and boosting energy circulation simultaneously. A comprehensive review of existing technologies on these three topics is elaborated subsequently. Based on this, a multi-pronged approach towards carbon neutrality is envisioned and a digital twin-powered industrial artificial intelligence (AI) framework is proposed to make this solution a reality. Furthermore, three key scientific challenges for putting such a framework in place are discussed. Finally, several applications for this framework are presented to demonstrate its enormous potential.

The rapid advancements in cloud computing, Big Data and their related applications have led to a skyrocketing increase in data center energy consumption year by year. The prior approaches for improving data center energy efficiency mostly suffer from high system dynamics or the complexity of data centers. In this paper, we propose an optimization framework based on deep reinforcement learning, named DeepEE, to jointly optimize energy consumption from the perspectives of task scheduling and cooling control. In DeepEE, a PArameterized action space based Deep Q-Network (PADQN) algorithm is proposed to tackle the hybrid action space problem. Then, a dynamic time factor mechanism for adjusting cooling control interval is introduced into PADQN (PADQN-D) to achieve more accurate and efficient coordination of IT and cooling subsystems. Finally, in order to train and evaluate the proposed algorithms safely and quickly, a simulation platform is built to model the dynamics of IT and cooling subsystems. Extensive real-trace based experiments illustrate that: 1) the proposed PADQN algorithm can save up to 15% and 10% energy consumption compared with the baseline siloed and joint optimization approaches respectively; 2) the proposed PADQN-D algorithm with dynamic cooling control interval can better adapt to the change of IT workload; 3) our proposed algorithms achieve more stable performance gain in terms of power consumption by adopting the parameterized action space.

As energy-intensive entities, data centers are associated with significant environmental impacts, making their sustainability a subject of growing interest in recent years. In this article, we revisit data center sustainability and propose a forward-looking vision for improving data center sustainability. We argue that data center sustainability encompasses more than just energy efficiency and must be evaluated and optimized through a multi-faceted approach. To this end, we first present an overview of the sustainability metrics from five aspects. After that, we demonstrate the sustainability status of the latest data centers utilizing publicly available data center sustainability ratings. Furthermore, we examine the evolution of data center sustainability standards in Singapore to highlight several trending features. Based on the analysis, we identify several key elements of sustainable data centers. We then propose the Cognitive Digital Twin (CDT) architecture, which incorporates a digital twin engine for system-wide simulation and a decision engine for optimal control to improve data center sustainability. A case study is performed to optimize the chiller plant efficiency of a production data center in Singapore. The results demonstrate that the CDT can improve chiller plant energy efficiency by 5%, indicating around 140 metric tons of annual carbon emission savings.

Predictive Maintenance (PdM) refers to a maintenance paradigm that performs maintenance only after the analytical models predict certain failures or degradations. While traditional PdM has improved maintenance efficiency, its scalability is constrained by network limitations, resource allocation challenges, and the computational demands of AI-driven analytics. With the help of fully connected intelligence, Intelligent Predictive Maintenance (IPdM) overcomes these limitations by leveraging 6G, cloud-edge computing, and AI advancements, enabling a more adaptive and intelligent PdM framework. This survey contributes to the burgeoning IPdM academia from the perspective of communication and networking. Firstly, we explore how communication evolutions and network advancements facilitate IPdM deployment by presenting an integrated cloudedge-device IPdM framework. Under this framework, we analyze the IPdM lifecycle and explore the challenges and solutions within each stage. Particularly, we discuss the novel technical enablers for IPdM in communication an AI. Afterwards, we explore how IPdM helps communication and networking by surveying the applications of IPdM in the regarding fields, such as data centres and sensor networks. Finally, we summarize the crucial future directions for promoting the further applications of IPdM in nextgeneration networking. We believe this work not only contributes to the academic discourse on IPdM but also offers valuable insights for industry professionals navigating the fourth industrial revolution.

Digital twin has been applied in various industrial fields to represent physical systems. However, the design of high-fidelity digital scenes is challenging in that it often requires intensive manual processes and domain expertise to edit the 3D models or description documents. To reduce human efforts, this paper proposes ChatTwin, a conversational system that leverages the power of GPT-4 to automate the generation of scene description documents for digital twins. ChatTwin assists scene generation by i) segmenting user-input prompts, ii) generating scenes with segmented prompts, and iii) optimizing the generated content. Specifically, the Segment-and-Generate (SG) workflow decomposes the long-text generation into several subtasks and reduces the complexity of the original task. The evaluation through our data center digital twin system shows that ChatTwin outperforms other baselines in terms of generation accuracy and efficiency.