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The steeply rising demand for mobile data drives the investigation of the transmission backhaul network architecture and cost for the fifth generation (5G) of mobile technologies. The proposed backhaul architecture will facilitate high throughput, low latency, scalability, low cost of ownership, and high capacity backhaul for 5G mobile technologies. This paper presents a transmission backhaul network architecture for 5G technology; the proposed internet protocol (IP) transmission backhauling architecture includes the data center, core network, distribution network, and access or IP random access network. A mathematical model for the data center IP core network, IP distributed network, and the IP access network for capital expenditure (Capex), operational expenditure (Opex), and the total cost of ownership (TCO) are presented, as well as a mathematical model for the entire backhauling architecture. The result shows that the increase in IP sites is positively proportional to the Capex and negatively proportional to the Opex. The selectivity analysis shows that the increase in bandwidth is directly proportional to the Capex, Opex, and TCO in the IP core network. The increase in data centers is directly proportional to the Capex, Opex, and TCO of the entire backhauling architecture. [ABSTRACT FROM AUTHOR]

Virtual machine placement (VMP) is carried out during virtual machine migration to choose the best physical computer to host the virtual machines. It is a crucial task in cloud computing. It directly affects data center performance, resource utilization, and power consumption, and it can help cloud providers save money on data center maintenance. To optimize various characteristics that affect data centers, VMs, and their runs, numerous VMP strategies have been developed in the cloud computing environment. This paper aims to compare the accuracy and efficiency of nine distinct strategies for treating the VMP as a knapsack problem. In the numerical analysis, we test out various conditions to determine how well the system works. We first illustrate the rate of convergence for algorithms, then the rate of execution time growth for a given number of virtual machines, and lastly the rate of development of CPU usage rate supplied by the nine methods throughout the three analyzed conditions. The obtained results reveal that the neural network algorithm performs better than the other eight approaches. The model performed well, as shown by its ability to provide near-optimal solutions to test cases. [ABSTRACT FROM AUTHOR]

Per-user pricing is possible with cloud computing, a relatively new technology. It provides remote testing and commissioning services through the web, and it utilizes virtualization to make available computing resources. In order to host and store firm data, cloud computing relies on data centers. Data centers are made up of networked computers, cables, power supplies, and other components. Cloud data centers have always had to prioritise high performance over energy efficiency. The biggest obstacle is finding a happy medium between system performance and energy consumption, namely, lowering energy use without compromising system performance or service quality. These results were obtained using the PlanetLab dataset. In order to implement the strategy we recommend, it is crucial to get a complete picture of how energy is being consumed in the cloud. Using proper optimization criteria and guided by energy consumption models, this article offers the Capsule Significance Level of Energy Consumption (CSLEC) pattern, which demonstrates how to conserve more energy in cloud data centers. Capsule optimization's prediction phase F1-score of 96.7 percent and 97 percent data accuracy allow for more precise projections of future value. [ABSTRACT FROM AUTHOR]

On-demand computing ability and efficient service delivery are the major benefits of cloud systems. The limitation in resource availability in single data centers causes the extraction of additional resources from the cloud providers group. The federation scheme dynamically increases resource availability in response to service requests. The dynamic increase in resource count leads to excessive energy consumption, maximum cost, and carbon footprints emission. Hence, the reduction of resources is the major requirement to construct the optimized cloud source models for profit maximization without considering energy mix and CO2. This paper proposes the novel migration method to reduce carbon emissions and energy consumption. The initial stage in the proposed work is the categorization of data centers based on the MIPS and cost prior to job allocation offers scalable and efficient services and resources to the cloud user. Then, the job with the maximum size is allotted to the VM only if its capacity is less than the cumulative capacity of data centers. A novel migration based on overutilized and underutilized levels provides the services to the user even if the particular VM fails. The proposed work offers efficient maintenance of resource availability and maximizes the profit of the cloud providers associated with the federated cloud environment. The comparative analysis of the proposed algorithm with the existing methods regarding the response time, accuracy, profit, carbon emission, and energy consumption assures the effectiveness in a confederated cloud environment. [ABSTRACT FROM AUTHOR]

A novel random biased-genetic algorithm (NRB-GA) load-balancing algorithm that exhibits the characteristics of both genetic algorithms and biased random algorithms is designed and developed to improve the processing time and response time metrics of the cloud computing environment. The NRB-GA is designed to discover a virtual machine with fewer loads by applying a genetic algorithm with a fitness function that is inversely proportional to the average load over a period of time for each virtual machine and with biased parent selection to maximize the fitness values of offspring. The developed NRB-GA load-balancing algorithm is evaluated by analysing its performance for various simulated scenarios in a cloud computing environment with different user bases and data center configurations. The analysis of the experimental results of NRB-GA indicates that the average response time is reduced by 27.22%, 21.15%, and 22.34%, and the processing time is reduced by 25.73%, 16.14%, and 18.82% for one, two, and three data centers, respectively. It is evident that the proposed NRB-GA algorithm for load balancing outperforms other existing algorithms significantly. [ABSTRACT FROM AUTHOR]

As the main tool to realize data mining and efficient knowledge acquisition in the era of big data, machine learning is widely used in data center energy-saving research. The temperature prediction model based on machine learning predicts the state of the data center according to the upcoming tasks. It can adjust the refrigeration equipment in advance to avoid temperature regulation lag and set the air conditioning temperature according to the actual demand to avoid excessive refrigeration. Task scheduling and migration algorithm based on temperature prediction can effectively avoid hot spots. However, the choice of hyperparameter of machine learning model has a great impact on its performance. In this study, a hyperparameter optimization algorithm based on MLP is proposed. On the basis of trying certain hyperparameters, the MLP model is used to predict the value of all hyperparameters' space, and then, a certain number of high-quality hyperparameters are selected to train the model repeatedly. In each iteration, the amount of training data decreases gradually, while the accuracy of the model improves rapidly, and finally, the appropriate hyperparameter are obtained. We use the idea of mutation in the genetic algorithm to improve the probability of high-quality solutions and the loss function weighting method to select the solution with the best stability. Experiments are carried out on two representative machine learning models, LSTM and Random Forest, and compared with the standard Gaussian Bayes and Random Search method. The results show that the method proposed in this study can obtain high-precision and high-stability hyperparameter through one run and can greatly improve the operation efficiency. This algorithm is not only effective for LSTM but also suitable for other machine learning models. [ABSTRACT FROM AUTHOR]

In data center networks, how to ensure the quality of service (QoS) of traffic effectively has become an important optimization goal. Traffic classification has been widely studied to guarantee network flow QoS since it provides a basis for the network performance level requirement of different flow types. However, traditional network traffic classification methods, which can only identify traffic type offline, have no ability for online classification and the in-time QoS guarantee. To solve this problem, we propose an online traffic classification for QoS routing (OCQR) framework based on programmable data plane in this paper. Firstly, in order to improve the accuracy of flow classification and reduce the impact on line-rate packet forwarding, the way of machine learning model offline training and online deployment is used to classify the network flow in real time. Based on the hardware foundation of programmable switch and the complexity of machine learning algorithm, a CART decision tree model is used to classify network flows. Secondly, in order to actually deploy and run the decision tree model in the programmable switch with limited computing and storage resources, we prune the decision tree model and optimize the leaf nodes to reduce the size of the decision tree and improve the classification recall rate. Finally, by building a P4 simulation environment, the recall rate of OCQR's classified scheduling of some application flows reaches more than 98%, which is consistent with the evaluation results of the offline dataset. At the same time, compared with the classification method based on SDN, OCQR does not require additional bandwidth occupation, so it has less impact on network performance. [ABSTRACT FROM AUTHOR]

This paper explores resource allocation strategy in the Baidu Over The Edge system to enable mobile edge computing (MEC) datacenters to effectively support cloud‐native services downstream to the network edge. There are many challenges to this issue. First, MEC datacenters are resource‐constrained to fully meet resource demands. Second, previous works regard the resource requirements of each service as an indivisible unit, resulting in idle MEC resources, even if the resources can meet the demands of some microservices decoupled by the service. Third, they are confined to optimize the allocation for a single slot, failing to adapt to the dynamic demands. To improve resource utilization, we propose performability‐aware resource allocation (PARA), a PARA on the edges for cloud‐native services. It takes microservices as the unit of resource allocation and allows services to perform with degraded services when only part of microservices' demands are met. It also considers dependency among microservices, dynamic resource requirements, and resource supply characteristics of MEC and cloud. Performability is a unified performance‐reliability measure for evaluating such degradable systems. To maximize the long‐term overall performability, we model the resource optimization problem and then develop an online greedy heuristic algorithm. The algorithm predicts services' resource demands and then adapts the online allocation. The experimental results show that PARA reduces the reallocation overhead by 47.7%–53.6%, and improves the long‐term overall performability by 23.14%–43.25% of existing state‐of‐the‐art works. [ABSTRACT FROM AUTHOR]

The article titled "Design and Implementation of Software-Defined Data Center (SDDC) for Medical Colleges and Universities" has been retracted by Wiley due to evidence of systematic manipulation of the publication process. The investigation revealed discrepancies in scope, description of the research, availability of data, inappropriate citations, and inclusion of incoherent or irrelevant content. The retraction notice aims to alert readers that the article's content is unreliable, but it does not investigate whether the authors were aware or involved in the manipulation. The corresponding author has been given the opportunity to respond to the retraction. [Extracted from the article]

In the data center environment, meeting the dissimilar requirements of transmission performance for long and short data flows is crucial for guaranteeing the data center service quality. It is well known that optimizing the network transmission performance is the key. Abundant research studies have reported that designing an effective load-balancing scheme can boost the network performance by enhancing the path utilization of multirooted data center networks. Unfortunately, the existing data center load-balancing schemes fail to provide satisfying flow-level transmission performance since they choose to ignore the constant changing path status and blindly assign paths to data flows regardless of their different performance requirements. In order to solve these issues, we propose a data center load-balancing scheme called TAM to meet the performance requirements of long and short flows simultaneously. In detail, TAM leverages the traffic-aware ECN marking mechanism to force long flows to proactively maintain the switch queue length at a low level and release bandwidth to short flows when they compete for the bottleneck link. On the contrary, TAM carries out the coarse-grained routing or rerouting for short and long flows, respectively, in real time by perceiving the path status. In this way, the short flows achieve low delay transmission while the long flows obtain high throughput. The simulation results based on NS2 tests show that TAM achieves up to 60% lower FCTs for short flows and a 40% improvement in goodput for long flows. [ABSTRACT FROM AUTHOR]

Distributed denial of service attacks seriously threatens the availability of highly resilient software-defined networking systems, such as data center networks. A traceback scheme is an effective means of mitigating attacks by identifying the location of the attacker and the attacking path. However, traditional traceback schemes suffer from low traceability success rates, high packet header overheads, and high communication traffic overheads, in addition to the fact that logically centralized traceability schemes make the control plane a prime target for attacks. To overcome the above challenges, we propose the low-overhead and high-precision traceback scheme, which is divided into two stages: packet marking and path reconstruction. The first stage of the traceback scheme utilizes programmable switches in the data plane to selectively mark the actual physical path information that the packet was forwarded on. The marking method is adaptive to the path length, which utilizes a combined Bloom filter so that the packet length does not grow with the length of the attacking path. The proposed probabilistic packet marking algorithm effectively reduces the number of packets collected to reconstruct the attacking path. The second stage of the traceback scheme utilizes the distributed victim host to reconstruct the attacking path without the controller and locate the source of the attacker. Theoretical analysis and experimental results show that the proposed scheme ensures the high accuracy of tracing and minimizes the traffic overhead and storage overhead required for the traceback process. [ABSTRACT FROM AUTHOR]

Cloud services are accessed from different geographical locations where client migration or switching from one server to another based on the loads is a common phenomenon. One of the most critical challenges the cloud data centers face is managing the loads over geographically dispersed data centers and their virtual machines (VMs). VMs need to be balanced with the varied loads or dynamics of traffic. There are possibilities of the highest loads to be tolerated by the VMs over the cloud servers without crashing. Load balancing issues are managed by load balancing algorithms. Load balancing algorithms have varied issues of efficiency due to certain parameters like the capability of the lowest resource utilization, response time, higher overhead while checking the idle or normal nodes, and many others. Throttled load balancing algorithm manages loads of the virtual machines by dividing the virtual machines into two segments, that is, "available" and "free." To do this, the throttled algorithm uses a single component to assign the virtual machines and other tasks. The throttled algorithm utilizes only the first VMs available, the next, and so on. These strategic issues most often degrade the performance of the applied load balancing algorithm. Such issues create a curiosity to enhance this algorithm's performance for efficiently managing the dynamic loads of the cloud VMs. This research paper proposes a component-based throttled load balancing algorithm with VM reader, free VM holder, and free VM manager components. The VM reader component reads all available VMs. The free VM component holds free VMs temporarily until they are moved to the free VM manager component. For the performance test, the cloud analyst simulation tool was used. Based on the comparative analysis with the other five popularly used load balancing algorithms, the component-based algorithm's performance is significantly enhanced. The proposed algorithm resulted in 325.30-microsecond response time and 27.12-microsecond processing time by the closest data center service broker policy. The newly proposed "component-based throttled load balancing algorithm" is found to be better than the existing throttled algorithm and the other five selected algorithms in terms of response time, processing time, and resource utilization. [ABSTRACT FROM AUTHOR]

Integrating cloud computing with wireless sensor networks creates a sensor cloud (WSN). Some real-time applications, such as agricultural irrigation control systems, use a sensor cloud. The sensor battery life in sensor clouds is constrained. The data center's computers consume a lot of energy to offer storage in the cloud. The emerging sensor cloud technology-enabled virtualization. Using a virtual environment has many advantages. However, different resource requirements and task execution cause substantial performance and parameter optimization issues in cloud computing. In this study, we proposed the hybrid electro search with ant colony optimization (HES-ACO) technique to enhance the behavior of task scheduling, for those considering parameters such as total execution time, cost of the execution, makespan time, the cloud data center energy consumption like throughput, response time, resource utilization task rejection ratio, and deadline constraint of the multicloud. Electro search and the ant colony optimization algorithm are combined in the proposed method. Compared to HESGA, HPSOGA, AC-PSO, and PSO-COGENT algorithms, the created HES-ACO algorithm was simulated at CloudSim and found to optimize all parameters. [ABSTRACT FROM AUTHOR]

With the development of green energy, the penetration rate of new energy in the power grid is rising rapidly. Incorporating a large amount of new energy into the power grid will affect the power supply balance of the power grid, and even in severe cases will affect the safe and stable operation of the power grid. The information park has various small and medium data centers. The adjustable load of the data center can be transferred in time and space through the optical fiber and other communication networks, which can enhance the controllability of the power grid to a certain extent and promote the consumption of new energy. This paper proposes a collaborative planning method for information parks that considers the temporal and spatial transfer characteristics of data center loads, builds a load transfer model for data centers and builds a park power grid expansion model with the goal of minimizing the total cost of construction and operation. In this paper, an example of a park is used to verify the calculation example, and it is compared with other schemes to verify the feasibility of the method in this paper. [ABSTRACT FROM AUTHOR]

During the transition from industrial production to intelligent, there are still a large number of manufacturing industries in the traditional production mode. The problems of slow information transmission, difficult integrated production management, and complicated process parameter control have led to disconnect decision-making from actual production. In addition, the current manufacturing industry is more of a traditional management method, and the upgrade and iteration speed are slow, and it is not combined with the technology of the Internet of Things. Therefore, the purpose of this article is to use the Internet of Things monitoring technology to explore the improvement of high-end manufacturing and analyze the problems and solutions in the process of consistency of ecological environment and economic effects. This article will use the research methods of specific problems and specific analysis to compare the data and draw conclusions. This article will discuss the technical mechanism of the Internet of Things monitoring and introduce the collection and data of production status data of each station equipment in the production workshop during the production process: improving the current production model, learning foreign experience at the same time, quickly carrying out load curve and data center data accumulation on the service capabilities of the park, verifying multiple operating models, and simultaneously exploring new management models. Based on comparative advantage analysis, the study draws similarities and differences in the development of high-end manufacturing industries at home and abroad, learns advanced experience, proposes improved methods and paths, combines new environmental protection paths, and finally proposes energy operation in the park. Build a green and intelligent park to provide a theoretical reference for sustainable development. The research results show that computer monitoring technology and methods have effectively improved the production environment of high-end manufacturing, combined with the development status of the existing ecological and intelligent composite production system; the infrastructure construction of industrial parks in the new era, IT support, services, manpower resources, and industries has developed rapidly. [ABSTRACT FROM AUTHOR]

In cloud data center (CDC), reducing energy consumption while maintaining performance has always been a hot issue. In server consolidation, the traditional solution is to divide the problem into multiple small problems such as host overloading detection, virtual machine (VM) selection, and VM placement and solve them step by step. However, the design of host overloading detection strategies and VM selection strategies cannot be directly linked to the ultimate goal of reducing energy consumption and ensuring performance. This paper proposes a learning-based VM selection strategy that selects appropriate VMs for migration without direct host overloading detection, thereby reducing the generation of SLAV, ensuring the performance, and reducing the energy consumption of CDC. Simulations driven by real VM workload traces show that our method outperforms the existing methods in reducing SLAV generation and CDC energy consumption. [ABSTRACT FROM AUTHOR]

Cloud computing has become the most challenging research field in the current information technology scenario. In this, a set of user tasks are scheduled and allocated to numerous kinds of heterogeneous virtual machines (VMs) in cloud data centers (CDCs), and these VMs are hosted by diverse types of heterogeneous physical machines (PMs). It extends several features, encompassing elasticity, safety, sustainability, and even adequate maintenance compared to traditional data centers. There are numerous techniques available for VM scheduling and allocation. However, it still requires the existence of new mechanisms that can reduce the execution time (ET) of the tasks, improve the optimization of energy usage and resource utilization (RU), and reduce time consumption. Along with optimization, VM scheduling (VMS) and VM allocation (VMA) are two-level issues that need to be considered with essential policies to govern these mechanisms. Hence, for executing optimal VMS and VMA in the data center, new optimization methodologies, such as enhanced shark smell optimization algorithm (ESSOA) at the first level and Brownian movement-centered gravitation search algorithm (BMGSA) at the second level, are proposed in this work to define the policies. Firstly, the user requests for VMs are reserved on the most appropriate PM by the proposed ESSOA, which has the lowest execution cost within deadline limits, and the proposed BMGSA decides the allocation of the chosen VM on the most appropriate PM within the resource limitations at the second level. To demonstrate the proposed algorithm's efficiency, the simulations are carried out using the Java language-based CloudSim simulator, and the proposed mechanism outcomes acquired are compared with the existing approaches. The simulation results show that the suggested algorithm is efficient in terms of the execution cost, degree of imbalance (DOI), make span (MS), and resource utilization (RU). [ABSTRACT FROM AUTHOR]

Aiming at the problems of the traditional K-means clustering algorithm, such as the local optimal solution and the slow clustering speed caused by the uncertainty of k value and the randomness of the initial cluster center selection, this paper proposes an improved KMeans clustering method. The algorithm first uses the idea of the elbow rule based on the sum of squares of errors to obtain the appropriate number of clusters k, then uses the variance as a measure of the degree of dispersion of the samples, and selects k data points with the smallest variance and the distance greater than the average distance of the samples as the initial clustering center of the data set. Finally, combined with the "triangular inequality principle," the unnecessary distance calculation of the KMeans algorithm in the iterative process is reduced, and the algorithm operation efficiency is improved. The results show that the improved KMeans clustering algorithm is tested on the UCI data set. Compared with the traditional k-means algorithm and Canopy-KMeans algorithm, the accuracy and speedup ratio of the improved KMeans clustering algorithm are significantly improved, and the clustering quality is improved. [ABSTRACT FROM AUTHOR]

In order to solve the problem that the activities of teachers and students in traditional teaching methods are constrained by lesson plans, the teaching methods are aging, and they do not have the ability to expand students' learning through big data; due to the problem of low self-learning and extracurricular communication ability of students after class, the author proposes the application of a data mining model in smart chemistry education. The author designs an educational wisdom platform based on big data, and its overall structure includes physical layer, virtual resource layer, logic layer, presentation layer, application layer, network layer, and user layer; the big data center module in the platform collects all business data through devices such as networks and sensors and stores the business data in mass data storage devices; the software design part uses the multi-feature fusion acquisition algorithm to collect student data and completes the early warning of student performance through the performance early warning algorithm based on correlation analysis technology. The results showed that only 5.15% and 4.49% of the students who designed the platform could not improve their after-class autonomous learning and extracurricular communication skills, and more than 95% of the students had good feedback after using the platform. Conclusion. It shows that the design platform can effectively improve the students' ability of self-learning and extracurricular communication after class, and the application efficiency is high. [ABSTRACT FROM AUTHOR]

The database of the intelligent science management system includes the exchange of data between the basic data of the school and the various business systems, with centralised storage and centralised management, constituting a unified data centre. The completed intelligent science management system will become a reliable source of data to integrate the system to be developed with existing systems, making the data of the whole system unified, thus providing reliable data for information enquiry and decision analysis within the university, and laying a good foundation for the construction of the digital campus. The NRM_DeepNet model is an application model of the deep learning method of the nonlinear random matrix in the intelligent research management system, mainly in data processing and analysis, using deep learning technology for data classification processing, and then for research data modal feature extraction, the process of modal extraction applies the nonlinear random matrix for feature weighting. In the process of modal extraction, a nonlinear random matrix is applied to weight the features, and finally, different research data features of different research projects can be classified as intelligent and regularised. In this paper, we combine the practice of scientific research management, introduce the nonlinear random matrix and deep learning technology into the intelligent scientific research management system, and update its data and system management so as to realize intelligent and humanised scientific research management, thus reducing the labour intensity and improving the efficiency of scientific research work. [ABSTRACT FROM AUTHOR]

Social creation and life have become progressively unmistakable. Bunch examination is the reason for additional handling of the data. The ideas of data mining, as well as the use of neural networks in data mining, are examined. The two-layer perceptron, back engendering (BP) neural network, and RBF extended premise work network are totally depicted exhaustively in this article data mining, as demonstrated by the associated development of data mining and handling characterization issues, and self-sorting out map (SOM) is a self-assembling neural network for unaided grouping issues. As indicated by the qualities of self-versatile and self-arranging capacities, we learn, plan, and execute data mining grouping optimization techniques using these algorithms. This study isolates the neural network-based data mining technique into three phases: data course of action, rule extraction, and rule evaluation. This work centers around the teaching type and breaking down type rule extraction strategies. The connection approach is used to compute the association between the information and result neurons after researching the BP disintegration type method. After you have planned everything out, it is time to put it all together. The RBF neural network is utilized to pick center points in light of association levels. This can assist with diminishing the quantity of data centers in the neural network; further develop the network geography; decline the quantity of recursive segments in the subnet, in addition to other things; and further develop computation productivity. Accepting the model, for instance, the preparation mistake is determined through the use of data mining and a bunching algorithm. For the most part, the data mining grouping optimization method works on from two perspectives: better model planning and model pruning and rebuilding of the well-known neural network tests that duplicate model complexity, computational intricacy, and blunders. After that, the rate is calculated, and finally, the recreation investigation is carried out. The findings indicate that the proposed differential dispersed data mining algorithm is more exact and effective. More grounded combination capacity defeats the deficiencies and weaknesses of a few unique hereditary algorithms. It can truly work on the algorithm's accessibility and search precision, as well as the usefulness of data mining employing neural network data mining models based on algorithm optimization. Precision and exactness are useful in a variety of situations. The rate is then computed, and ultimately, the reproduction study is conducted. The findings show that the proposed differential allocated data mining method works. The algorithm is more precise and more grounded in intermingling capacity and conquers the inadequacies and weaknesses of a few unique hereditary algorithms. It can successfully further improve the algorithm's inquiry capacity and search precision, as well as work on the productivity of data mining, through algorithm optimization neural network data mining models. Precision and exactness have several applications. [ABSTRACT FROM AUTHOR]

Elevators are highly susceptible to safety incidents in the event of leveling failures, so the ability to monitor related failures must be strengthened. This paper proposed a new elevator leveling failures monitoring device and method in which elevator signals are obtained from the elevator CAN bus interface, transmitted to a remote monitoring platform via NB-IoT, and stored in our private data center. The leveling sensor sensing signal, the door signal, the car call signal, the target floor signal, and the running signal are obtained by analyzing the data extracted from the elevator. Logical analysis could be used to determine the elevator's running status and leveling-related failures. The device and method could identify and also predict leveling-related failures and have advantages in terms of universality, accuracy, and economy. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the traditional talent training mode is difficult to cultivate the intelligent animal husbandry compound talents needed by the implementation of the rural revitalization strategy, a cloud animal husbandry platform is designed to cultivate "Understand Agriculture, devote to Rural Community and Be Sincere to Farmers" compound talents. The platform adopts the two-wing multilevel architecture design of the concept of the cloud side, which is composed of three parts: the data center platform, resource sharing platform, and business cloud service platform. The data center platform is responsible for the collection, processing, integration, and asset management of the four-dimensional real data and provides computing, storage, and data asset services for the cloud livestock platform. The resource sharing platform provides sharing services from resources and development kits to carriers. Livestock cloud service platform is to carry out the integration of industry, education, and science and education, the design and implementation of cloud teaching, cloud scientific research, and cloud breeding business cloud services. Through its practical application in many universities and enterprises, the platform is verified to be safe, effective, and reliable in teaching, scientific research, and social services and has both operational and promotional value. [ABSTRACT FROM AUTHOR]

In view of the shortcomings of big data security and privacy protection in cloud environment, a big data security architecture was proposed in this paper. Based on blockchain technology and trusted data cloud center, data security architecture adopts the ideas of trusted authentication, intrusion detection, data segmentation, and decentralized storage and applies Amazon AWS log processing service, PairHand user authentication protocol, and Hadoop data analysis framework to realize dig data security and privacy protection in the cloud environment. This paper realizes system initialization and user authentication, hierarchical data storage, decentralized storage, and user security access. The experimental results show that the system architecture can ensure data security and data access speed, which can provide necessary reference for cloud security. [ABSTRACT FROM AUTHOR]

Cloud computing is a procedure of stockpiling as well as retrieval of data or computer services over the Internet that allows all its users to remotely access the data centers. Cloud computing provides all required services to the users, but every platform has its share of pros and cons, and another major problem in the cloud is task scheduling or workflow scheduling. Multiple factors are becoming a challenge for scheduling in cloud computing namely the heterogeneity of resources, tasks, and user priority. User priority has been encountered as the most challenging problem during the last decade as the number of users is increasing worldwide. This issue has been resolved by an advanced encryption standard (AES) algorithm, which decreases the response time and execution delay of the user-request. There are multifarious tasks, for instance, deploying the data on the cloud, that will be executed according to first come first serve (FCFS) and not on the payment basis, which provides an ease to the users. These investigated techniques are 30.21%, 25.20%, 25.30%, 30.25%, 24.26%, and 36.9 8% improved in comparison with the traditional FFOA, DE, ABC, PSO, GA, and ETC, respectively. Moreover, during iteration number 5, this approach is 15.20%, 20.22%, 30.56%, 26.30%, and 36.23% improved than that of the traditional techniques FFOA, DE, ABC, PSO, GA, and ETC, respectively. This investigated method is more efficient and applicable in certain arenas where user priority is the primary concern and can offer all the required services to the users without any interruption. [ABSTRACT FROM AUTHOR]

With the emergence of new vehicle-mounted services, such as autonomous driving and augmented reality, the computing capability at the edge of the Internet of vehicles (IoV) has become increasingly demanding, and the edge computing of the IoV has also emerged. With the rise of the IoV, new vehicle-mounted applications keep emerging. Vehicle-mounted applications need to process the sensor data of themselves and other vehicles around them. The amount of data is huge, and processing these sensor data requires powerful computing power. But typical vehicles have limited computing power, so such computation-intensive tasks need to be uploaded to a data center for processing. The MEC (Mobile Edge Computing) technology is a good choice to solve this problem. This paper advocates reasonable scheduling of parked vehicles to cooperate with edge servers for collaborative edge computing and studies the resource scheduling scheme under the target of user relative satisfaction. This paper describes the main entities and related functions in the network and proposes a secure and reliable protocol for interaction between entities. The serviceability of different parked vehicles in collaborative edge computing was evaluated to select stable task performers and solve the resource scheduling optimization problem between parked vehicles and edge servers. The simulation results show that the proposed scheme has better customer satisfaction and maximum overhead than the existing scheme that only considers using edge server to perform tasks. Therefore, the method of this paper has important theoretical significance and potential practical value. [ABSTRACT FROM AUTHOR]

With the continuous deepening and development of tobacco manufacturing informatization construction, tobacco manufacturing enterprises have the characteristics of strong production capacity, high degree of production automation, and advanced enterprise-level management information system. In order to cooperate with the market and brand strategy of the tobacco industry, the State Tobacco Monopoly Administration has carried out serious strategic thinking on informatization and customized the overall plan and strategic goals. In recent years, China's informatization construction has made great progress, and both industries and enterprises have felt various benefits from the application of information technology. In the deployment and development of mobile services, it is often local and municipal mobile companies that, in order to meet the different needs of the market, immediately carry out corresponding services, resulting in one service for each municipal subsidiary in the same province or one platform for a class of services. Situation is good for quickly meeting user needs at that time. The exchange and communication of enterprise personnel information are becoming more and more frequent, and the collection of enterprise information through mobile has become a new way of enterprise information collection. The work orders, process parameters, documents, notifications, and instructions generated by the planning layer are sent to the production control layer to respond, guide, and trigger the production site events. The ultimate goal of mobile data collection is to gather the collected data into the enterprise production data center, which plays an important role in mobile devices, mobile performance, mobile planning, mobile balancing, mobile prediction, and so on. Based on the unified mobile application data collection method, this paper analyzed and analyzed the design of application architecture in the tobacco enterprise platform and further described the business functions, security performance, and system characteristics of the platform. [ABSTRACT FROM AUTHOR]

With the development of the social economy and the acceleration of economic globalization, human resources of enterprises have become an important factor restricting the development of enterprises. This paper creates an enterprise human resource management platform application based on twin network and mobile edge computing. By allocating computing and storage resources to the network edge close to users or data sources, mobile edge computing supports mobile to complete the computing offload of wireless access network applications. This process significantly reduces the end-to-end delay of the network and effectively reduces the processing load of the core network and data center. The simulation results can see that the average user benefit of all algorithms increases with the increase of network transmission speed and VCPU processing power. In addition to the introduction and popularization of information technology, the construction of enterprise human resources informatization also requires continuous and comprehensive monitoring and analysis of existing internal data, information, and data. The use of network communication technology to build a management information system, through information management, can significantly optimize and improve the efficiency of enterprise human resource management. [ABSTRACT FROM AUTHOR]

Big data entails massive cloud resources for data processing and analysis, which consumes more energy to run. The resources and tasks are increasing exponentially in the cloud environment for the processing of big data, which results in an increment in power consumption to run the cloud data center. So, there is always a scope for optimizing the energy utilization in cloud data centers. This paper presents a visionary architecture in a cloud environment for big data with a proposed energy-efficient strategy based on LSTM-DQN (long-short-term memory-deep Q network) using reinforcement learning (RL). The traditional techniques are not so efficient when the tasks are allocated dynamically, and the generic RL strategies are not able to store the data iterated in the last cycles of processing, so the LSTM is considered for this purpose. In the proposed model, integration of DPSO and DQN is used for better estimation and rectification of the curse of dimensionality. The proposed strategy is compared with different variants of PSO (particle swarm optimization) such as DPSO and QoS-PSO. The improvement in results through proposed model is recoded over the algorithm such as load aware (8.01%), DQN (13.36%), EA-DQN (34.16%), L-No-DEAF (15.62%), DPSO (62.68%), QoS-PSO (72.69%), FFO-EVSM (75.42%), and MIMT (76.39%) on the parameter of energy efficiency, tasks completion time, and energy consumption over the timeline. So, the proposed model is encouraging in the energy-efficient cloud environment for big data with the challenges that the technological world is facing and the emergence of deep learning as one propitious field. [ABSTRACT FROM AUTHOR]

Cache prefetching technology has become the mainstream data access optimization strategy in the Industrial Intelligent Systems (IIS) and the data centers. However, the rapidly increasing of unstructured data generates massive pairwise access relationships. Therefore, researchers have to make a choice between spatial locality and temporal locality to ensure an acceptable computational complexity. We propose cache‐transaction‐based data grouping model (CTDGM) to solve the problems described above by optimizing the feature representation method and grouping efficiency. First, we provide the definition of the cache transaction and propose the method for extracting the cache transaction feature (CTF). Second, we design a data chunking algorithm based on CTF and spatiotemporal locality to optimize the relationship calculation efficiency. Third, we propose CTDGM by constructing a relation graph that groups data into independent groups according to the strength of the data access relation. Based on the results of the experiment, compared with the state‐of‐the‐art and traditional methods, our algorithm achieves an average increase in the cache hit rate of 5%–20% on the MSR, VDI‐LUN, and KC data set, which in turn reduces the number of data I/O accesses by 30%–60%. [ABSTRACT FROM AUTHOR]

AIOps (artificial intelligence for IT operations) has been growing rapidly in recent years. However, it can be seen that the vast majority of AIOps applications are implemented in the IT domain. In contrast, there are few applications in the data center infrastructure domain. Many real-world practices show that a working architecture or algorithm cannot be directly replicated from other domains due to completely different business scenarios and different data characteristics. In this paper, an AIOps architecture for the data center infrastructure monitoring domain is presented. A proven working architecture is given in terms of core modules, such as technical architecture, machine learning algorithms, big data, and business applications, and details are designed in practice. This paper focuses on the technical part and not on each part of the architecture work. In other words, NFRs (nonfunctional requirements), such as performance, availability, manageability, and security, will not be discussed in this paper. [ABSTRACT FROM AUTHOR]

With the advanced development of data center and network information technology, digital library has become a user sharing platform with a variety of digital information sources, providing users with flexible and diverse personalized services. However, the existing digital library lacks data analysis information and only publishes book information and related borrowing frequency and other information. As an ideal architecture model for library applications, cloud library can integrate distributed digital information sources, optimize digital libraries, and provide users with flexible, diverse, and personalized services. Therefore, based on the related research on cloud library and neural network algorithm, this paper not only focuses on the artificial network algorithm, but also focuses on the concept, features, and method of building the digital library platform based on the artificial network algorithm. Under the support of related theories and technologies of cloud library service platform, it also builds a cloud library intelligent service platform model based on artificial neural network. The research results showed that the time spent by BPNN in processing borrowed books was generally about 6 minutes, while the time required by RNN was 10 minutes. However, the processing of BPNN is not stable. Therefore, when building an intelligent service platform, different algorithms need to be selected according to individual needs. [ABSTRACT FROM AUTHOR]

Energy consumption is the key factor leading to high operating costs in data center. In this paper, considering the data center service level under the premise of using electricity and gas as energy supply of energy supply and energy consumption from two angles, the data center energy scheduling model is built. The lower model is calculation scheduling model for the data center, and the upper model is data center energy supply scheduling model. Then the particle swarm algorithm is used to simulate the schedule. The results show that using natural gas as supplementary energy supply can effectively reduce the overall energy consumption of data center in considering the service delay level of data center. [ABSTRACT FROM AUTHOR]

In order to solve the problems faced in the current management of colleges and universities, this topic integrates data mining technology and traditional statistical analysis technology in the process of College data management, and integrates multisource campus raw data. Take corresponding research models for different research objectives, implement the fine, meticulous, and accurate management concept of colleges and universities for students, build a student information management platform and help the construction of smart campus. The research integrates the school's multi-business system data, opens up the information channel between information systems, establishes a multisystem data center and realizes the whole life cycle management of College Students' data, including data collection, cleaning, storage and file recycling, and records the data of important nodes during students' enrollment, graduation, and departure. Through the analysis of the school's business management needs and the study of the school's data characteristics, the data collected from the system information has been effectively distributed, and the data linking process is only used to delete data features based on data features. Under the action of big data technology, the information platform for college students can be constructed, which can provide more efficient, safe, and comprehensive decision support for college students' management, making all work decisions more scientific. It can be said that the integration of big data will support the management of college students to a new level, which is very important for the modern management of students. [ABSTRACT FROM AUTHOR]

In order to improve the monitoring effect of financial security risk in colleges and universities, this paper studies the financial security risk control of colleges and universities combined with the big data clustering center scheduling algorithm and inverts the multilevel sampling algorithm of quantum potential support. Moreover, this paper considers that the multilevel sampling algorithm is applied to the potential backscattering problem of the stationary Schrödinger equation to invert the support of the potential in the equation. In addition, this paper uses far-field data to invert the generalized linear sampling method of potential support and builds a college financial security risk monitoring model that relies on the big data clustering center scheduling algorithm. The experimental study shows that the financial security risk monitoring system for colleges and universities based on the big data clustering center scheduling algorithm proposed in this paper has good risk clustering and risk identification effects. [ABSTRACT FROM AUTHOR]

In order to make full use of the large amount of teaching data existing in the data center, the author proposes a mining algorithm using classification technology and association rules in data mining technology; the teaching quality evaluation data and student achievement data were mined. By constructing the C4.5 decision tree algorithm, this method taps the potential links between teachers' professional titles, degrees, ages, and teaching quality evaluation results, through correlation analysis theory; the correlation between professional courses was mined from the student's course achievement database, and some reasonable and reliable course association rules were obtained. Experimental results show that the correct rate of C4.5 decision tree algorithm under different sampling times is above 80%. The conclusions drawn have certain guiding significance for college teaching and personnel training. [ABSTRACT FROM AUTHOR]

Because the distributed storage system is based on network technology, it can store data in multiple independent low-cost physical storage devices, and it is also suitable for large-capacity storage, so it has become more and more popular. Today, common applications of distributed storage systems include cloud storage services, data center storage services, and P2P storage services. Typical ones are GFS, HDFS, OceanStore, and Dynamo. Due to regional and economic differences, the development level of global e-commerce (b2c) is very inconsistent. b2c contains the following key tags: buying and selling, which is the core of the website platform. E-commerce provides business users with transparent information and high-quality cheap products. Logistics is the basic guarantee for customers to execute transactions, and it is also a strict indicator of the website platform. There will be many visits during the operation of the e-commerce system, and the number of users in the early stage will increase exponentially. A safe and efficient e-commerce system can provide users with one-stop transaction support and convenient transaction processes. The personalized recommendation system has formulated some rules for certain fields, based on these rules, and defined certain types of knowledge for certain items to meet the needs of certain users and use the defined reasoning rules to generate recommendation results. [ABSTRACT FROM AUTHOR]

The current medical universities culture can reflect their professional nature, showing a strong development trend. The current socialist economy of China is rapidly developing along with cloud computing for data centers. For cultural and educational activities, virtualization and software-defined data center (SDDC) technologies are being used. The most widely used open-source virtualization technologies are SDDC and virtualization. Users may utilize OpenStack to establish private cloud computing environments. The separate control and forwarding architecture of SDDC also make it naturally suitable for the data center's network environment. Ryu controller has become one of the most widely used SDDC controllers because of its lightweight, high efficiency, and modularity features. Due to the large variety of network operations required by the SDDC, appropriate management and control function modules must be developed on Ryu when it is used as the controller. This study investigates the construction of campus culture at medical colleges and universities using SDDC. Our main objective is to improve the efficiency of data construction in medical colleges and universities and creating a positive cultural environment. On the one hand, the addition of functional modules makes the management of the controller itself easier. Ryu lacks an intuitive interactive platform. Although OpenStack provides an interactive interface, it cannot meet the integration requirements of Ryu. The unified control and school scheduling system have some research and application potential. The experimental results show that our proposed approach of designing an SDDC for medical colleges and universities has a significant impact and has vast potential for future studies. [ABSTRACT FROM AUTHOR]

Recent advances of wireless networks and communication technologies, integrated into the considerable development in the field of environmental policies and public participation, have resulted in the development of wireless network-enabled big data environment in the capacity building of Anhui industry association. At the same time, effective task scheduling in the cloud server based big data environment is essential to schedule the tasks accurately and rapidly. There are thousands of jobs to be executed by the resources available on cloud data centers to achieve minimum time, high performance, and the proper utilization of CPU and resources. The necessity to fulfil user requirements is the main reason of having studies for optimizing the cloud computing of big data in terms of latency, bandwidth, execution time, and resource utilization. Therefore, this work presents a crow search optimization-based task scheduling scheme (CSO-TSS) for capacity building of Anhui industry association on wireless network-enabled big data environment. The proposed CSO-TSS technique mainly intends to schedule the tasks exist in the wireless network-enabled big data environment. Besides, the CSO-TSS technique is executed on a MapReduce environment in order to proficiently handle the big data. In addition, the CSO-TSS technique derives an objective function intending to maximize resource utilization and minimize execution time of the tasks. For examining the improved task scheduling performance of the CSO-TSS technique, a wide range of simulations were carried out and the results are investigated under several aspects. The comparative result analysis stated the better outcomes of the CSO-TSS technique over the recent approaches in terms of different measures. [ABSTRACT FROM AUTHOR]

In order to solve the library's demand for computer network technology, a research on the library's personal system service is proposed. A library-based self-service model was originally designed and developed. The system is developed from six aspects: resource association analysis and mining, reading interest analysis, data collection, personal service, personal service scheduling, and data warehouse. Secondly, it shows that this study has done some research on library application itself and completed some research. Finally, libraries can accomplish personal services in a variety of ways, such as distributing modifications, smart administrators, vertical portals, pushers, and more. In foreign libraries and universities represented by North Carolina State University and the Data and Information Center of the Chinese Academy of Sciences, the construction of personal data in digital libraries has become an important part of future development. According to the law, 57.9% and 65.0% of users ranked search engines as second only to e-mail in the China Internet Improvement Data released by the China Internet Network Information Center in 2000 and 2005, respectively. [ABSTRACT FROM AUTHOR]

Erasure coding has been widely deployed in today's data centers for it can significantly reduce extra storage costs while providing high storage reliability. However, erasure coding introduced more network traffic and computational overhead in the data update process. How to improve the efficiency and mitigate the system imbalance during the update process in erasure coding is still a challenging problem. Recently, most of the existing update schemes of erasure codes only focused on the single stripe update scenario and ignored the heterogeneity of the node and network status which cannot sufficiently deal with the problems of low update efficiency and load imbalance caused by the multistripe concurrent update. To solve this problem, this paper proposes a Load-Aware Multistripe concurrent Update (LAMU) scheme in erasure-coded storage systems. Notably, LAMU introduces the Software-Defined Network (SDN) mechanism to measure the node loads and network status in real time. It selects nonduplicated nodes with better performance such as CPU utilization, remaining memory, and I/O load as the computing nodes for multiple update stripes. Then, a multiattribute decision-making method is used to schedule the network traffic generated in the update process. This mechanism can improve the transmission efficiency of update traffic and make LAMU adapt to the multistripe concurrent update scenarios in heterogeneous network environments. Finally, we designed a prototype system of multistripe concurrent updates. The extensive experimental results show that LAMU could improve the update efficiency and provide better system load-balancing performance. [ABSTRACT FROM AUTHOR]

With the accelerating pace of China's electricity market reform, the construction of the electricity spot market has been put on the agenda. However, as the number and scope of market participants gradually expand, the market-oriented transaction power continues to rise, and the intraprovincial and interprovincial transaction varieties are increasingly abundant. How to design an intelligent and powerful system that can meet the performance requirements of high concurrency and high-frequency transactions in the future market is a major problem in power reform. Based on the research of theoretical research results, this paper builds the front-end interaction platform of the southern spot electricity system based on the regional center to provide a data declaration interface for market users, including market management, market declaration, market release, market evaluation, intelligent analysis, front-end data interface, security protection, and other functional modules, provide declaration information and some market evaluation results to the southern regional spot power system platform, and obtain clearance results and published information from this platform. [ABSTRACT FROM AUTHOR]

As the cloud data centers size increases, the number of virtual machines (VMs) grows speedily. Application requests are served by VMs be located in the physical machine (PM). The rapid growth of Internet services has created an imbalance of network resources. Some hosts have high bandwidth usage and can cause network congestion. Network congestion affects overall network performance. Cloud computing load balancing is an important feature that needs to be optimized. Therefore, this research proposes a 3-tier architecture, which consists of Cloud layer, Fog layer, and Consumer layer. The Cloud serves the world, and Fog analyzes the services at the local edge of network. Fog stores data temporarily, and the data is transmitted to the cloud. The world is classified into 6 regions on the basis of 6 continents in consumer layer. Consider Area 0 as North America, for which two fogs and two cluster buildings are considered. Microgrids (MG) are used to supply energy to consumers. In this research, a real-time VM migration algorithm for balancing fog load has been proposed. Load balancing algorithms focus on effective resource utilization, maximum throughput, and optimal response time. Compared to the closest data center (CDC), the real-time VM migration algorithm achieves 18% better cost results and optimized response time (ORT). Realtime VM migration and ORT increase response time by 11% compared to dynamic reconFigure with load (DRL) with load. Realtime VM migration always seeks the best solution to minimize cost and increase processing time. [ABSTRACT FROM AUTHOR]

The amount of energy required by Cloud Data Centers (CDCs) has increased significantly in this digital age, and as a result, there is a pressing need to reduce CDC energy ingesting. Consolidation of virtual machines (VMs) and effective virtual machine placement (VMP) techniques are commonly employed in large data middles to reduce energy consumption. The VMP is an NP-hard subject with infeasible optimum explanations even for tiny data middles, and it is dealt with using the Metaheuristic Optimization Algorithm, which is an experiential approach to optimization. With this in mind, this study introduces a novel energy-aware VMP technique for CDCs that is founded on the Disordered Salp Swarm Optimization Algorithm (EAVMP-CSSA) and is enhanced for energy efficiency (EAVMP-CSSA). The EAVMP-CSSA technique attempts to reduce CDC energy ingesting by dropping the quantity of active servers supporting virtual machines. The recommended EAVMP-CSSA strategy also aims to balance the resource operation of active servers (i.e., CPU, RAM, and Bandwidth), hence reducing waste and increasing efficiency. Furthermore, by combining the ideas of chaotic maps with the standard Salp Swarm Optimization Algorithm (SSA), the CSSA is intended to improve overall performance and reduce computational costs (SSA). A comprehensive range of experimental analyses are performed to ensure that the EAVMP-CSSA technique performs better, and the findings are compared to current VMP techniques. The EAVMP-CSSA approach achieves an effective outcome with a maximum service rate of 98.12%, whereas the Random, FFD, ACO, and AP-ACO procedures achieve a minimum service rate of 74.40%, 78.80%, 90.70%, and 96.31%, respectively. The experimental results demonstrate that the EAVMP-CSSA approach outperforms other assessment metrics. [ABSTRACT FROM AUTHOR]

The Internet consists of distributed and interconnected autonomous systems (ASs). The flexibility afforded by the Internet architecture ensures that timely changes to the network topology can occur without centralized control. The Border Gateway Protocol (BGP) is an Internet protocol that routes the Internet traffic and exchanges the information between AS. However, BGP version 4 (BGP-4) currently suffers from a limitation called "high convergence delay" while doing routing updates, which is a very common limitation, damaging the performance of contemporary IP networks. Software-defined networking (SDN) was conceived at the beginning of the 21st century, and over the intervening years, it has gained traction. This technology has significantly enhanced traffic control, management, and monitoring, particularly in data centres and enterprise networks. Challenges have been found including how to provide administrative control, security, management, and monitoring at domain boundaries while introducing the SDN paradigm. In multidomain SDN, BGP-4 is used to exchange the information and route the traffic between domains or diverse AS. In this review, an investigation is provided on using the SDN paradigm to enhance multidomain traffic management and control and to optimize BGP operation. A detailed insight is provided into the penetration of the SDN paradigm into modern networking architectures and how it may help in facilitating future research to improve BGP-4. [ABSTRACT FROM AUTHOR]

With the rise of cloud computing technology, the whole information technology industry has undergone dramatic changes. Large-scale computing and the growing demand for computing power from large data have made it impossible for traditional data center network topology construction methods and network layer operation control methods with good stability to meet the requirements of technological development. The topology of a data center network is usually characterized as an undirected graph, and graph algorithms are used to implement communication patterns in complex network structures. However, the routing algorithms proposed earlier for graphs cannot be applied to all topological graphs. Based on the comprehensive interactive trust evaluation network security model, this study analyzes the advantages and disadvantages of relevant algorithms and proposes a fault-tolerant routing algorithm suitable for all networks. The algorithm can dynamically call different algorithms according to the number of current nodes and links. From the simulation results, it can be found that the algorithm not only improves the accuracy and fault tolerance but also effectively reduces the loss of time and memory. [ABSTRACT FROM AUTHOR]

In order to solve the problem with the expansion of the industrial scale, the contradiction between energy resources and development of printing and dyeing enterprises must implement refined management and optimize the allocation of resources such as production technology, energy consumption types, and metering instruments. As a printing and dyeing textile industry with high energy consumption and high pollution, energy conservation and emission reduction have become difficult problem to be solved in this industry. The premise of optimizing resource allocation is to have an objective and scientific evaluation of the current energy resource allocation of printing and dyeing enterprises. In view of this, through the investigation of printing and dyeing enterprises, this paper puts forward the index system of enterprise energy consumption optimization evaluation. Based on the application of data warehouse and combined with historical data, a new energy consumption optimization evaluation method is proposed. This survey has basically understood the current situation of energy and water resources management of a printing and dyeing enterprise and pointed out the direction for the development of enterprise energy optimization project in the next step. By means of multimedia informatization, with the help of Internet of Things sensing technology, building a new generation of energy management system, improving the refined management of energy measurement, and improving the energy assessment system, enterprises can achieve significant economic and social benefits in terms of energy conservation and emission reduction. On this basis, a low-power scheduling strategy for typical data center applications is designed and implemented. The algorithm uses the input data of different calculation parts, performs matching to determine the redundant part of the calculation process, and schedules the algorithm. Experimental data show that the mean square error of the limit tree is 0.0004248 and the mean square error of the decision tree is 0.01581; through calculation, the algorithm can achieve 23% and 17% energy saving. [ABSTRACT FROM AUTHOR]

With the application of cloud computing services in more and more fields, it will undertake more computing tasks and storage tasks. The problem of high energy consumption in data centers will become more serious. Virtualization technology is very important in cloud computing, which can improve the utilization rate of resources. At the same time, it has flexibility in resource scheduling and can integrate multiple virtual machines to achieve power efficiency. Using online virtual machine migration technology for energy-saving planning in cloud environment is a hot research topic in academic circles. The scheduling strategy proposed in this paper can reduce the server downtime and the number of server hosts, so as to achieve the maximum use of resources. The main research work of this paper includes the following aspects: Firstly, the energy consumption in the process of virtual machine energy-saving design is modeled, and the relationship between energy consumption and resource usage under different load conditions is analyzed, and the problems are abstracted, e.g., packaging box problem. Secondly, this paper uses genetic algorithm to solve the problem of high energy consumption. Finally, based on the target allocation scheme of virtual machines obtained by the above method, the migration problem of virtual machines is abstracted as the problem of finding the maximum weighted independent set of graphs. And a greedy algorithm is designed to solve this problem. In this paper, CloudSim simulation platform is used to verify the effectiveness of the proposed algorithm. Experiments show that the proposed algorithm can reduce data energy consumption and avoid frequent migration of virtual machines. [ABSTRACT FROM AUTHOR]

The sizes of individual data files have steadily increased along with rising demand for customized services, leading to issues such as low efficiency of web-based geographical information system (WebGIS)-based data compression, transmission, and rendering for rich Internet applications (RIAs) in complicated visualization systems. In this article, a WebGIS-based technical solution for the efficient transmission and visualization of meteorological big data is proposed. Based on open-source technology such as HTML5 and Mapbox GL, the proposed scheme considers distributed data compression and transmission on the server side as well as distributed requests and page rendering on the browser side. A high-low 8-bit compression method is developed for compressing a 100 megabyte (MB) file into a megabyte-scale file, with a compression ratio of approximately 90%, and the recovered data are accurate to two decimal places. Another part of the scheme combines pyramid tile cutting, concurrent domain name request processing, and texture rendering. Experimental results indicate that with this scheme, grid files of up to 100 MB can be transferred and displayed in milliseconds, and multiterminal service applications can be supported by building a grid data visualization mode for big data and technology centers, which may serve as a reference for other industries. [ABSTRACT FROM AUTHOR]

Research Problem. Aiming at the problems such as fast update and iteration speed, high development cost, and isolated enterprise data in hydrogen equipment manufacturing industry, a low code programming system based on enterprise data center and business center was proposed and designed. Research Method. This paper designs the data center service and business center architecture of hydrogen equipment manufacturing industry. The data center collects, mines, and integrates massive data into unified standard structured data and stores them. The business center provides architectural support for the establishment of enterprise resource management system. With the data center service as the system data interaction center and the business center as the model to build the system architecture, the hydrogen equipment manufacturing industry resource management system is constructed through the steps of data interaction, process modeling, and UI editing. Research Results. According to the specific requirements of hydrogen equipment manufacturing industry, the business process of hydrogen enterprise resource management system is constructed, and the construction and development of low code system are studied and discussed by integrating data center and business center. Conclusions. Low code is not just a tool to increase productivity, it has the potential to be a catalyst for technology and culture to promote enterprise innovation and business agility, and ERP systems for hydrogen equipment manufacturing based on low coding technology are needed for the industry of the future. This article contributes to the hydrogen equipment manufacturing industry of low code programming system, users can according to their own needs, through the combination of components, quickly generate a complete management system, update system iteration, greatly reduce the development cost, solve the problem of enterprise data isolation, reduce the development cost, and realize the seamless integration of modern software development practice, so as to promote enterprise innovation and business agility. [ABSTRACT FROM AUTHOR]