Hierarchical Multi-Agent Optimization for Resource Allocation in Cloud Computing

In cloud computing, an important concern is to allocate the available resources of service nodes to the requested tasks on demand and to make the objective function optimum, i.e., maximizing resource utilization, payoffs and available bandwidth. This paper proposes a hierarchical multi-agent optimization (HMAO) algorithm in order to maximize the resource utilization and make the bandwidth cost minimum for cloud computing. The proposed HMAO algorithm is a combination of the genetic algorithm (GA) and the multi-agent optimization (MAO) algorithm. With maximizing the resource utilization, an improved GA is implemented to find a set of service nodes that are used to deploy the requested tasks. A decentralized-based MAO algorithm is presented to minimize the bandwidth cost. We study the effect of key parameters of the HMAO algorithm by the Taguchi method and evaluate the performance results. When compared with genetic algorithm (GA) and fast elitist non-dominated sorting genetic (NSGA-II) algorithm, the simulation results demonstrate that the HMAO algorithm is more effective than the existing solutions to solve the problem of resource allocation with a large number of the requested tasks. Furthermore, we provide the performance comparison of the HMAO algorithm with the first-fit greedy approach in on-line resource allocation

Virtual network function placement in satellite edge computing with a potential game approach

Satellite networks, as a supplement to terrestrial networks, can provide effective computing services for Internet of Things (IoT) users in remote areas. Due to the resource limitation of satellites, such as in computing, storage, and energy, a computation task from a IoT user can be divided into several parts and cooperatively accomplished by multiple satellites to improve the overall operational efficiency of satellite networks. Network function virtualization (NFV) is viewed as a new paradigm in allocating network resources on-demand. Satellite edge computing combined with the NFV technology is becoming an emerging topic. In this paper, we propose a potential game approach for virtual network function (VNF) placement in satellite edge computing. The VNF placement problem aims to maximize the number of allocated IoT users, while minimizing the overall deployment cost. We formulate the VNF placement problem with maximum network payoff as a potential game and analyze the problem by a game-theoretical approach. We implement a decentralized resource allocation algorithm based on a potential game (PGRA) to tackle the VNF placement problem by finding a Nash equilibrium. Finally, we conduct the experiments to evaluate the performance of the proposed PGRA algorithm. The simulation results show that the proposed PGRA algorithm can effectively address the VNF placement problem in satellite edge computing

Dynamic resource allocation for virtual network function placement in satellite edge clouds

Satellite edge computing has become a promising way to provide computing services for Internet of Things (IoT) users in remote areas, which are out of the coverage of terrestrial networks. Nevertheless, it is not suitable for large-scale IoT users due to the resource limitation of satellites. Cloud computing can provide sufficient available resources for IoT users, but it does not meet delay-sensitive services as high network latency. Satellite edge clouds can facilitate flexible service provisioning for numerous IoT users by incorporating the advantages of edge computing and cloud computing. In this paper, we investigate the dynamic resource allocation problem for virtual network function (VNF) placement in satellite edge clouds. The aim is to minimize the network bandwidth cost and the service end-to-end delay jointly. We formulate the VNF placement problem as an integer non-linear programming problem and then propose a distributed VNF placement (D-VNFP) algorithm to address it. The experiments are conducted to evaluate the performance of the proposed D-VNFP algorithm, where Viterbi and Game theory are considered as the baseline algorithms. The results show that the proposed D-VNFP algorithm is effective and efficient for solving the VNF placement problem in satellite edge clouds

Dynamic resource management for neighbor-based VNF placement in decentralized satellite networks

By introducing software-defined networking (SDN) and network function virtualization (NFV), low-earth-orbit (LEO) satellite networks can facilitate virtual network function (VNF) placement, which will provide computing services for satellite applications on-demand. In this paper, we study the VNF placement problem in a decentralized LEO satellite network due to the requirements for real-time processing and network resilience, where our aim is to jointly optimize end-toend service delay and network bandwidth cost in a dynamic environment. To this end, a decentralized LEO satellite network architecture is first implemented for resource management by establishing the neighboring sub-network for each satellite. Then we formulate the VNF placement problem as an integer non-linear programming problem with multiple constraints of network resources and service requirements. A neighbor-based VNF placement (N-VNFP) approach is proposed to address the optimization problem. Finally, we conduct the experiments to evaluate the performance of the proposed N-VNFP approach in a Walker constellation with 66 LEO satellites. The simulation results show that the proposed N-VNFP approach provides an effective solution for resource management in a decentralized LEO satellite network and also outperforms the two centralized baselines, i.e., Viterbi and Greedy, in terms of end-to-end service delay and network bandwidth cost

Coaxial foilless diode

A kind of coaxial foilless diode is proposed in this paper, with the structure model and operating principle of the diode are given. The current-voltage relation of the coaxial foilless diode and the effects of structure parameters on the relation are studied by simulation. By solving the electron motion equation, the beam deviation characteristic in the presence of external magnetic field in transmission process is analyzed, and the relationship between transverse misalignment with diode parameters is obtained. These results should be of interest to the area of generation and propagation of radial beam for application of generating high power microwaves

Satellite edge computing with collaborative computation offloading: an intelligent deep deterministic policy gradient approach

Enabling a satellite network with edge computing capabilities can complement the advantages further of a single terrestrial network and provide users with a full range of computing service. Satellite edge computing is a potentially indispensable technology for the future satellite-terrestrial integrated networks. In this paper, a three-tier edge computing architecture consisting of terminal-satellite-cloud is proposed, where tasks can be processed at three planes and inter-satellites can cooperate to achieve on-board load balancing. Facing varying and random task queues with different service requirements, we formulate the objective problem of minimizing the system energy consumption under the delay and resource constraints, and jointly optimize the offloading decision, communication and computing resource allocation variables. Moreover, the distribution of resources is based on the reservation mechanism to ensure the stability of satellite-terrestrial link and the reliability of computation process. To adapt to the dynamic environment, we propose an intelligent computation offloading scheme based on the deep deterministic policy gradient (DDPG) algorithm, which consists of several different deep neural networks (DNN) to output both discrete and continuous variables. Additionally, by setting the selection process of legal actions, the simultaneous decisions on offloading locations and allocating resources under multi-task concurrency is realized. The simulation results show that the proposed scheme can effectively reduce the total energy consumption of the system by ensuring that the task is completed on demand, and outperform the benchmark algorithms

Geographical Distribution and Influencing Factors of Intangible Cultural Heritage in the Three Gorges Reservoir Area

Intangible cultural heritage (ICH) represents the outstanding crystallization of human civilization and it has received extensive attention from scholars in various countries. Studying the spatial distribution and influencing factors of ICH in the Three Gorges Reservoir Area can help to improve the protection and utilization of ICH. Using quantitative statistical analysis methods, GIS spatial analysis methods, and Geodetector, we analyzed the level structure (provincial and national levels), category structure (ten categories), and spatial distribution of 509 national and provincial ICH items in the Three Gorges Reservoir Area and then explored their influencing factors. We concluded that: (1) The structural characteristics of ICH vary significantly, and the level structure is dominated by provincial ICH items; the category structure is complete and mainly includes traditional skill and traditional music. (2) The spatial distribution of ICH in the Three Gorges Reservoir Area is dense in the west and sparse in the east, with a pattern of “one main core, three major cores, and two minor cores”. There are large differences in the degree of concentration of ICH at the county level; different categories of ICH have different distribution densities and concentration areas. Yuzhong District, Shizhu County, and Wanzhou District are dense areas of distribution for different categories of ICH. (3) The influences of different factors on the spatial distribution of ICH in the Three Gorges Reservoir Area vary greatly. Socioeconomic and historical–cultural factors are more influential than natural geographic factors, among which economic development, culture, and ethnicity are the most influential, but the interaction between the two dimensions of natural geography and socioeconomic and historical culture has a more significant influence on the spatial distribution of ICH than single-dimensional factors. (4) Proposals for optimizing the spatial layout, protection, and development of ICH in the Three Gorges Reservoir Area are provided from the perspectives of culture and tourism integration and sustainable development

Comparative Study of Convolutional Neural Network and Conventional Machine Learning Methods for Landslide Susceptibility Mapping

Landslide susceptibility mapping (LSM) is a useful tool to estimate the probability of landslide occurrence, providing a scientific basis for natural hazards prevention, land use planning, and economic development in landslide-prone areas. To date, a large number of machine learning methods have been applied to LSM, and recently the advanced convolutional neural network (CNN) has been gradually adopted to enhance the prediction accuracy of LSM. The objective of this study is to introduce a CNN-based model in LSM and systematically compare its overall performance with the conventional machine learning models of random forest, logistic regression, and support vector machine. Herein, we selected Zhangzha Town in Sichuan Province, China, and Lantau Island in Hong Kong, China, as the study areas. Each landslide inventory and corresponding predisposing factors were stacked to form spatial datasets for LSM. The receiver operating characteristic analysis, area under the curve (AUC), and several statistical metrics, such as accuracy, root mean square error, Kappa coefficient, sensitivity, and specificity, were used to evaluate the performance of the models. Finally, the trained models were calculated, and the landslide susceptibility zones were mapped. Results suggest that both CNN and conventional machine learning-based models have a satisfactory performance. The CNN-based model exhibits an excellent prediction capability and achieves the highest performance but also significantly reduces the salt-of-pepper effect, which indicates its great potential for application to LSM