Generative adversarial networks (GANs) have recently become a hot research topic; however, they have been studied since 2014, and a large number of algorithms have been proposed. Nevertheless, few comprehensive studies explain the connections among different GAN variants and how they have evolved. In this paper, we attempt to provide a review of the various GAN methods from the perspectives of algorithms, theory, and applications. First, the motivations, mathematical representations, and structures of most GAN algorithms are introduced in detail, and we compare their commonalities and differences. Second, theoretical issues related to GANs are investigated. Finally, typical applications of GANs in image processing and computer vision, natural language processing, music, speech and audio, the medical field, and data science are discussed.

Heating, Ventilation, and Air Conditioning (HVAC) are extremely energy-consuming, accounting for 40% of total building energy consumption. It is crucial to design some energy-efficient building thermal comfort control strategy which can reduce the energy consumption of the HVAC while maintaining the comfort of the occupants. However, implementing such a strategy is challenging, because the changes of the thermal states in a building environment are influenced by various factors. The relationships among these influencing factors are hard to model and are always different in different building environments. To address this challenge, we propose a deep reinforcement learning based framework, DeepComfort, for thermal comfort control in buildings. We formulate the thermal comfort control as a cost-minimization problem by jointly considering the energy consumption of the HVAC and the occupants’ thermal comfort. We first design a deep Feedforward Neural Network (FNN) based approach for predicting the occupants’ thermal comfort, and then propose a Deep Deterministic Policy Gradients (DDPG) based approach for learning the optimal thermal comfort control policy. We implement a building thermal comfort control simulation environment and evaluate the performance under various settings. The experimental results show that our approaches can improve the performance of thermal comfort prediction by 14.5% and reduce the energy consumption of HVAC by 4.31% while improving the occupants’ thermal comfort by 13.6%.

Data center (DC) plays an important role to support services, such as e-commerce and cloud computing. The resulting energy consumption from this growing market has drawn significant attention, and noticeably almost half of the energy cost is used to cool the DC to a particular temperature. It is thus an critical operational challenge to curb the cooling energy cost without sacrificing the thermal safety of a DC. The existing solutions typically follow a two-step approach, in which the system is first modeled based on expert knowledge and, thus, the operational actions are determined with heuristics and/or best practices. These approaches are often hard to generalize and might result in suboptimal performances due to intrinsic model errors for large-scale systems. In this paper, we propose optimizing the DC cooling control via the emerging deep reinforcement learning (DRL) framework. Compared to the existing approaches, our solution lends itself an end-to-end cooling control algorithm (CCA) via an off-policy offline version of the deep deterministic policy gradient (DDPG) algorithm, in which an evaluation network is trained to predict the DC energy cost along with resulting cooling effects, and a policy network is trained to gauge optimized control settings. Moreover, we introduce a de-underestimation (DUE) validation mechanism for the critic network to reduce the potential underestimation of the risk caused by neural approximation. Our proposed algorithm is evaluated on an EnergyPlus simulation platform and on a real data trace collected from the National Super Computing Centre (NSCC) of Singapore. The resulting numerical results show that the proposed CCA can achieve up to 11% cooling cost reduction on the simulation platform compared with a manually configured baseline control algorithm. In the trace-based study of conservative nature, the proposed algorithm can achieve about 15% cooling energy savings on the NSCC data trace. Our pioneering approach can shed new light on the application of DRL to optimize and automate DC operations and management, potentially revolutionizing digital infrastructure management with intelligence.

Data centers (DCs), owing to the exponential growth of Internet services, have emerged as an irreplaceable and crucial infrastructure to power this ever-growing trend. A DC typically houses a large number of computing and storage nodes, interconnected by a specially designed network, namely, DC network (DCN). The DCN serves as a communication backbone and plays a pivotal role in optimizing DC operations. However, compared to the traditional network, the unique requirements in the DCN, for example, large scale, vast application diversity, high power density, and high reliability, pose significant challenges to its infrastructure and operations. We have observed from the premium publication venues (e.g., journals and system conferences) that increasing research efforts are being devoted to optimize the design and operations of the DCN. In this paper, we aim to present a systematic taxonomy and survey of recent research efforts on the DCN. Specifically, we propose to classify these research efforts into two areas: 1) DCN infrastructure and 2) DCN operations. For the former aspect, we review and compare the list of transmission technologies and network topologies used or proposed in the DCN infrastructure. For the latter aspect, we summarize the existing traffic control techniques in the DCN operations, and survey optimization methods to achieve diverse operational objectives, including high network utilization, fair bandwidth sharing, low service latency, low energy consumption, high resiliency, and etc., for efficient DC operations. We finally conclude this survey by envisioning a few open research opportunities in DCN infrastructure and operations.

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.