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Addressing the optimization problem during the simultaneous energy and information transmission stage in the simultaneous wireless information and power transfer (SWIPT), an optimal energy efficiency strategy based on mmWave cooperative communication small cell is proposed to maximize the link energy efficiency. This strategy, under the joint constraints of minimum link transmission rate and minimum collected energy, employs a power splitting mode at the receding end of energy-constrained user devices. By optimizing transmit power control and power splitting factors, it aims to maximize the systems link energy efficiency. Recognizing the original problem as a nonconvex fractional programming problem with NP-hard characteristics, the Dinkelbach method is utilized to transform the objective function into a convex optimization problem that is easier to solve. An iterative algorithm is designed to find the optimal solution through cross iterations. Simulation results demonstrate that the proposed strategy outperforms traditional power control methods and maximum transmit power approaches in optimizing system energy efficiency. [ABSTRACT FROM AUTHOR]

With the continuous expansion of the number and scale of data centers, energy consumption has become a key issue that restricts the cost and reliability of data centers. Moreover, with the iterative updates of the hardware after the data center is put into operation, the heterogeneity of the data center server has further increased, and its energy efficiency has also under gone major changes compared with the initial design and construction. Therefore, according to the structure and hard ware configuration of the data center server, the dynamic energy efficiency simulation and analysis of the entire data center will help to grasp the energy efficiency status of the data center in real time, carry out energy efficiency aware load scheduling, and provide the possibility of energy efficiency optimization. Based on the SPECpower test results of enterprise-level servers, this article first analyzes the energy efficiency development trends and influencing factors of servers in recent years. Secondly, based on the genetic algorithm, the energy efficiency optimization of the data center is simulated, and a prototype system of the energy efficiency simulator of the data center is designed. The simulator can dynamically simulate and adjust the operating status of data center servers according to power supply limits, load conditions, throughput indicators, etc., and simulate the energy efficiency of data center s of different sizes and types of servers. The proposed data center energy efficiency simulation algorithm based on genetic algorithm obtains smaller errors and shorter simulation calculation time in the energy minimization problem. [ABSTRACT FROM AUTHOR]

To overcome the serve energy consumption problem in indoor wireless networks, a deep Q-Learning based transmit power allocation algorithm for home base station is proposed. Firstly, a deep learning network (DLN) is built to optimize the energy efficiency of indoor wireless networks. Then, the energy consumption rating is regarded as the rewards, the batch gradient descent method is used to continuously train the weights of DLN. Finally, the simulation results show that the proposed algorithm can dynamically adjust the transmit power, and is significantly superior to Q-Learning and water-filling algorithms in terms of convergence speed and EC optimization, which can effectively reduce the energy consumption of indoor wireless networks. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) have important applications in many fields such as medical treatment and industry. WSNs typically consists of a large number of sensor nodes that rely on limited power supply in many applications. Therefore, improving the energy efficiency of WSNs has become an important research topic. As a basic service in WSNs, network flooding has the advantage that information can be distributed quickly and reliably in the whole network. However, due to a large number of redundant transmissions in the network, the energy efficiency of network flooding is low. We use the connected domination set (CDS) to improve the energy efficiency of network flooding by reducing the number of transmissions. A flooding protocol based on CDS, named CONE, is proposed. CONE inhibits nodes that are not in the CDS from rebroadcasting packets during the flooding process. In the simulation experiments, CONE performance is evaluated by comparing it with the baseline protocol. The experimental results show that the method improves the end-to-end reliability and reduces the duty ratio of network flooding. It is able to effectively decrease the average energy consumption. [ABSTRACT FROM AUTHOR]

Due to the increasing demand for high performance computing in scientific research and commercial applications, the performance and system scale of high performance computing are developing rapidly. However, the rapid increase in power consumption severely limits the design and use of high-performance computing (HPC) systems, which makes low-power technologies become the key technology in the HPC field. As the core component of the whole system and based on limited system resources, the job scheduling system distributes job resources to the applications submitted by the users. Its energy efficiency plays an important role in the control and regulation of the energy consumption of the whole HPC system. We first introduce the main energy efficiency techniques and common job scheduling strategies, then analyze the energy efficiency of current HPC job scheduling, and discuss the challenges it faces and future development directions. [ABSTRACT FROM AUTHOR]

High energy efficiency with high performance, low energy consumption, and QoS support is one of the research challenges in cloud computing. According to this issue, the current studies trade off the three aspects o£ the issue by fixing one factor and optimizing the others, which lack an efficient energy efficiency calculation method and evaluation model that integrate the three aspects and describe the "degree" of the energy efficiency better. A QoS parameters reduction approach and a weighted energy efficiency model are proposed, which introduce the system performance as a key indicator into QoS, reduce the discrete QoS parameters into a unified dimension, establish the energy efficiency classification levels of the cloud data centers, describe the energy efficiency as a qualitative concept, and realize the energy efficiencys qualitative evaluation in cloud computing environments. Besides, the energy efficiencies of the cloud data centers in single-machine, homogenous, and heterogeneous cloud computing environments are evaluated, and experimental results show that the proposed energy efficiency model and evaluation method are effective for evaluating the QoS level and energy consumption in cloud computing environments. [ABSTRACT FROM AUTHOR]

In the global energy crisis, many researchers begin to pay close attention to the problem of data centers, energy consumption. On the premise of meeting the users, demand, reducing the active nodes of adata center can effectively reduce the whole energy consumption. Virtual machine migration is a traditional way to reduce active nodes, but causes huge system cost. An energy-saving algorithm for MapReduce-based virtual cluster, named Online Time Balance Algorithm (OTBA), is proposed. The proposed algorithm can reduce the number of active physical nodes, reduce the energy consumption effectively, and avoid migrating the virtual machines. The objective function and the variable factors are determined by building the energy consumption model, the queue model and the MapReduce performance model. Since OTBA is based on virtual cloud environment and online MapReduce, it can make a tradeoff between the runtime of virtual machines and the resource utilization so as to minimize the number of the activated physical servers in the data center and the energy consumption. At last, the algorithm is validated through experiments in simulation environment and on Hadoop platform. [ABSTRACT FROM AUTHOR]