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Facing environmental pollution and resource waste, the green building becomes more and more popular. This study analyses the advantages and future trends of green buildings from the green building materials, framework, and design. Green buildings were proposed in the 20th century and slowly developed into the dominant architectural model today. The application of green building concepts in architecture focuses on three components of green materials, green frames, and green design. Green materials are the basis for the implementation of the green building concept, more and more high-performance, environmentally friendly green materials have been developed, effectively reducing environmental pollution and harm to the human body. The green framework is an integral part of the implementation of the green building concept, and the green framework composed of green materials can effectively use natural energy to replace or convert it into advanced energy, effectively reducing energy consumption and environmental pollution. Green design is the specific performance of the combination of the green building concept and the building, both improving building beauty and the environmental quality. It indicates that the concept of the green building makes better use of energy to create personalized buildings that are harmless to the environment and better use energy, providing residents with a more comfortable environment.

The explosive growth of mobile devices promotes the prosperity of novel mobile applications, which can be realized by service offloading with the assistance of edge computing servers. However, due to limited computation and storage capabilities of a single server, long service latency hinders the continuous development of service offloading in mobile networks. By supporting multi-server cooperation, Pervasive Edge Computing (PEC) is promising to enable service migration in highly dynamic mobile networks. With the objective of maximizing the system utility, we formulate the optimization problem by jointly considering the constraints of server storage capability and service execution latency. To enable dynamic service placement, we first utilize Lyapunov optimization method to decompose the long-term optimization problem into a series of instant optimization problems. Then, a sample average approximation-based stochastic algorithm is proposed to approximate the future expected system utility. Afterwards, a distributed Markov approximation algorithm is utilized to determine the service placement configurations. Through theoretical analysis, the time complexity of our proposed algorithm is linear to the number of users, and the backlog queue of PEC servers is stable. Performance evaluations are conducted based on both synthetic and real trace-driven scenarios, with numerical results demonstrating the effectiveness of our proposed algorithm from various aspects.

The development of smart vehicles brings drivers and passengers a comfortable and safe environment. Various emerging applications are promising to enrich users’ traveling experiences and daily life. However, how to execute computing-intensive applications on resource-constrained vehicles still faces huge challenges. In this article, we construct an intelligent offloading system for vehicular edge computing by leveraging deep reinforcement learning. First, both the communication and computation states are modelled by finite Markov chains. Moreover, the task scheduling and resource allocation strategy is formulated as a joint optimization problem to maximize users’ Quality of Experience (QoE). Due to its complexity, the original problem is further divided into two sub-optimization problems. A two-sided matching scheme and a deep reinforcement learning approach are developed to schedule offloading requests and allocate network resources, respectively. Performance evaluations illustrate the effectiveness and superiority of our constructed system.