Cloud Service System Research Articles

Construction of super-resolution model of remote sensing image based on deep convolutional neural network

With the continuous improvement of satellite remote sensing technology, using super-resolution image reconstruction technology to reconstruct remote sensing images has important application significance for social development. In the generator model proposed in this paper, the standard convolution layer in the residual network structure is replaced by empty convolution to improve the overall performance of the model while keeping the number of parameters unchanged and the receptive field of convolution at each stage unchanged. By analyzing the advantages of residual network, dense connection network, and cavity convolution in the field of image super resolution, an optimized super-resolution reconstruction model of GAN image with cavity convolution is constructed with dense connection block of cavity residue as a generator component. The cloud computing-based service model is introduced into the image reconstruction system, and the background management module is built through the cloud service system, which is responsible for model training, image transmission, image processing request and database reading. Through experimental analysis, it is proved that the whole automatic data processing from automatic matching data to processing data can be completed, and the performance is better than the traditional service mode, which can produce great economic benefits.

Dynamic Deployment and Scheduling Strategy for Dual-Service Pooling-Based Hierarchical Cloud Service System in Intelligent Buildings

Due to the excessive concentration of computing resources in the traditional centralized cloud service system, there will be three prominent problems of management confusion, construction cost and network delay. Therefore, we propose to virtualize regional edge computing resources in intelligent buildings as edge service pooling, then presents a hierarchical cloud platform with dual-service pooling structure and a dynamic strategy for the proposed model. The analytic hierarchy process (AHP) based quality of service (QoS) evaluation mechanism and the dynamic normal distribution selection method are adopted for service deployment. And the dynamic inertia particle swarm optimization (DI-PSO) algorithm is employed to realize task scheduling. Furthermore, the cloud platform and existing terminal server group are used to conduct platform structure comparison experiments, and the popular task scheduling algorithms are selected for simulation experiments. Experimental results of platform measurement show that the average service response time of different services can be improved by about 17.3% to 37.4%. The average occupancy ratio of computing resources can be reduced by about 5%. The simulation results show that the earliest completion time of single task list can be decreased by 11.3% to 20.9%, and the makespan of 100 task lists can be improved by 0.3 times.

Identity Based Cryptography Using Matrices

A new method of identity (ID) based Elgamal type encryption/decryption is described. The technique uses rectangular integer matrices in finite field Zp, as the private keys. Adoption of modular arithmetic limits the size of the elements of the key matrices while maintaining reasonably high security levels. The principle of the method is extended to design a new ID based Proxy Re-Encryption (PRE) scheme that converts the cipher text (CT) intended for one user into a different CT which can be decrypted by another user. In present work, all mathematical operations like addition, subtraction, multiplication, and inversion of matrices are carried out using modular algebra. Further, ID based encryption/decryption as well as PRE has protection against chosen plain text and chosen CT attacks. Computational expensive techniques like, bilinear maps and modular exponentials are not used in the proposed scheme. Therefore, present technique is computationally less expensive compared to similar existing methods as confirmed by experimental results. Proposed PRE scheme is implemented within a cloud service system is well suited for secure data sharing among multiple end users.

RETRACTED ARTICLE: Research on product decision support system model based on cloud service and embedded system

With the continuous progress of society and the continuous development of science and technology, people's lives have also undergone great changes. In people's daily life, the rapid development of information technology has brought people many new life experiences. Computer network technology takes up an increasing proportion of social development. People are gradually getting used to the convenient life brought by computer network technology, and its development has also changed the way and channels of information dissemination. The development of embedded systems provides a broader space for the construction of computer networks. In the process of development, people also combine cloud service platforms with embedded systems to provide help for product decision-making, but embedded systems themselves have many shortcomings However, the system does not have strong hardware support in the process of storage resources and operation, which will affect the normal operation of the cloud service platform, so the problems existing in the embedded system must be solved. In order to improve the effectiveness and scientificity of product decision-making, this article builds an effective technical framework for the system construction of product decision-making based on the analysis of cloud service decision-making platform construction. This study also evaluated the performance of the product decision-making system by establishing a mathematical model, proving that the construction and use of the system can provide effective help for decision-makers’ decision-making activities.

Violation Detection in Service Level Agreement to Ensure the Privacy in Cloud Community using Chicken Spider Monkey Optimization-Based Deep Belief Network

In the cloud service system, the expectation between the customer and the cloud provider for the delivered services like, responsibilities and penalties of the violation arise in the cloud parties are specifically designed in a document named as Service Level Agreement (SLA). However, violation in the Service Level Agreement is a major challenge in the research community of SaaS cloud providers. Hence, an effective violation detection method named Chicken Spider Monkey Optimization-based Deep Belief Network (ChicSMO-based DBN) is proposed in this research to perform the violation detection mechanism in the cloud environment. The proposed Chicken Spider Monkey Optimization-based Deep Belief Network effectively computes the scores, like User Privacy Score (UPS), Service Level Agreement score (SLA), and Cloud Service Provider score (CSP) based on the fitness value. The spider monkeys forage the food by evaluating the distance from the food, and update their position towards the food source. The feature scores are computed by updating the position of local and global leader group members, and perform the violation detection process using the Deep Belief Network classifier. The performance enhancement attained by the proposed algorithm in terms of the evaluation metrics, like accuracy, coverage rate, and F-value which acquire with the values of 0.862, 0.944, and 0.889 with the transaction data of 1000, respectively. It increases the reliability to address the QoS guarantee issues and increase the confidence level of the prediction model.

The QoS evaluation model for cloud resource node

The service quality of resource nodes in a cloud service system can reflect the capability of the system in providing users with services. In this paper, we focus on the evaluation of cloud service...

Design and Application of a Virtual Simulation Teaching System Based on Cloud Service

To satisfy the demand of virtual simulation (VS) teaching system for storage capacity and computing power, it is of great significance to probe deep into the VS system oriented to cloud service (CS). This paper designs a CS-based VS teaching system, drawing on the relevant results on distributed VS system, the formalization of VS system architecture, and the semantic organizability of VS system. Firstly, the functional requirements of the CS-based VS teaching system were analyzed, and decomposed into three layers, namely, goal, function type, and function name. Then, a dynamic model of CS-based VS teaching system was constructed, whose simulation accuracy and precision were verified through experiments. The experimental results demonstrate the excellent simulation performance of the proposed system. The research findings provide reference for applying CS system design in other fields.

A new design of generator set controller in cloud service system based IoT technology

To solve the existing technical problems in quality management, distributed control, maintenance and monitoring management faced by traditional diesel generator set(GenSet), this paper summarizes the development status of traditional diesel generator set technology, and analyzes the business requirements of end users for distributed cloud control. Through the integration of traditional diesel generator cloud technology with internet of things technologies(IOT), a new design scheme of cloud GenSet controller is proposed based IoT technology, which simplifies the logic hierarchy of diesel generator cloud control. Then this paper puts forward the key network hardware design, and applies the new cloud GenSet controller to the enterprise new GenSet system, which verifies the feasibility and stability of the new design.

Resource optimization scheduling and allocation for hierarchical distributed cloud service system in smart city

With the support of 5G system, the hierarchical distributed cloud service network model (HDCSN) is proposed. The model consists of three levels: Access Cloud + Distributed Micro Cloud + Core Cloud, which meets the basic requirements of IMT-2020 (5G) and 3GPP for network system architecture. On the basis of access cloud, the distributed micro-cloud system of Smart City is deployed to migrate the service capabilities of the remote core cloud server to the local area. Users can obtain high-quality low-latency application services from the micro-cloud server in the local area. A resource description model based on resource graph and hierarchical resource vector is established, which enriches the Smart City service mode of IaaS cloud platform and provides a structural basis for optimal scheduling of cluster resources. A multi-node system resource graph scheduling and allocation algorithm VCE-PSO based on particle swarm optimization is proposed to optimize the response speed and resource efficiency of multi-node collaborative scheduling. The example shows that the above key technologies significantly improve 5G. The scheduling optimization and utilization efficiency of various resources in the hierarchical distributed cloud service for the Smart City effectively reduces the response time of the tenant resource request and optimizes the performance of system resource scheduling on the cloud platform.

Design of cloud service system for enterprise economic management based on hybrid cloud mode

Design of cloud service system for enterprise economic management based on hybrid cloud mode

WebIDE Cloud Server Resource Allocation With Task Pre-Scheduling in IoT Application Development

WebIDE is leveraged for IoT application development, which could adapt to the rapid growth of IoT applications and meanwhile facilitate the rapid development. Resource allocation is of vital significance in the WebIDE cloud service system. Existing resource allocation approaches may encounter issues such as unbalanced resource assignments, which could lead to the reduced system resource utilization or extended system response time. Existing methods are typically on the basis of predetermined resource demands for each task, and not applicable to the case that the resource demands are dynamic and unknown. This article predicts the tasks to be performed by the WebIDE cloud service through task pre-scheduling, and then applies the existing resource allocation methods. Firstly, all tasks are classified, based on the execution state, execution operations and WebIDE cloud server resource requirements. Secondly, the grouped tasks are mapped to different system states, with the Markov state transition probability matrix leveraged to model the transition probability between tasks, followed by the prediction model constructed. Finally, integrating task pre-scheduling with ant colony algorithm, WebIDE cloud server resource allocation is carried out. Experiment results show that adding the task prediction model could significantly not only reduce the task response time, but also improve the cloud server resource utilization.

Dynamic Role-based User Service Authority Control and Management on Cloud Computing

The demands for computing resource allocation, user management, and various contents management are increasing with the proliferation of cloud computing. A cloud service system has to provide the system services that are combined or shared based on the web environment, unlike a single application in the past. It has also evolved to appropriately allocate and combine service resources according to the needs and needs of various users to provide a user-dependent web environment. There is an increasing need for software convergence services in the form of software. In response to these needs, a variety of customized services are provided to a user who promises an optional service environment. Those services can be used in combination according to a user's purpose and needs, instead of providing an environment in which a user purchases and uses software locally. In particular, because cloud computing provides the process that a user gain accessing authority to the system which has been granted a service access right for a certain period of time, and including a process of expire the corresponding right is completed, a various authority access control method is designed. In this paper, we define the roles of users who access cloud computing service, and manage user rights according to each role, provide and expire appropriate service resources according to user's privilege and session information. Moreover we analyzed the procedures for constructing an algorithm for service ripple effects according to the procedure of granting service resources to each user and accessing web services and constructing a new role.

Personalized Product Design and Service System for Cloud Manufacturing

Personalized product design system needs the support from cloud-manufacturing technology in order to promptly achieve. Based on the investigation on the characteristics of cloud-manufacturing system platform, a system architecture applied for the process of personalized product design service built on cloud-manufacturing platform was proposed, which was called Intelligent Cloud Manufacturing and Service System for Personalized Product Design (ICMSS-PPD), to connect both the customer needs and manufacturing resource for a better service.

Business Process Driven Trust-Based Task Scheduling

The demand for agile and flexible business application systems has sparked interest in using cloud computing technology to respond quickly and effectively to a dynamic business environment. The authors classify the appropriate cloud services as a multi-objectives task scheduling problem in a hybrid cloud service system. In this article, the authors propose a business process (BP) driven task scheduling system that supports multiple clouds, including private ones. A trust-based non-dominated sorting genetic algorithm (NSGA2) is developed to solve the multi-objective task scheduling problem. By sorting populations into different hierarchies based on the ordering of Pareto dominance, they identify a Pareto-optimal multi-dimensional frontier that permits managers to reconcile conflicting objectives when scheduling tasks on cloud resources. The authors illustrate the usability and effectiveness of their approach by applying it to a case study conducting simulated experiments.

Cloud Service Security Adaptive Target Detection Algorithm Based on Bio-Inspired Performance Evaluation Process Algebra

Combining the principle of antibody concentration with the idea of biological evolution, this paper proposes an adaptive target detection algorithm for cloud service security based on Bio-Inspired Performance Evaluation Process Algebra (Bio-PEPA). The formal modelling of cloud services is formally modded by Bio-PEPA and the modules are transformed between cloud service internal structures and various components. Then, the security adaptive target detection algorithm of cloud service is divided into two processes, the short-term optimal action selection process which selects the current optimal detective action through the iterative operation of the expected function and the adaptive function, and the long-term detective strategy realized through the updates and eliminations of action planning table. The combination of the two processes reflects the self-adaptability of cloud service system to target detection. The simulating test detects three different kinds of security risks and then analyzes the relationship between the numbers of components with time in the service process. The performance of this method is compared with random detection method and three anomaly detection methods by the cloud service detection experiment. The detection time of this method is 50.1% of three kinds of detection methods and 86.3% of the random detection method. The service success rate is about 15% higher than that of random detection methods. The experimental results show that the algorithm has good time performance and high detection hit rate.

Mean-field Macro Computation in Large-scale Cloud Service Systems with Resource Management and Job Scheduling

Service computing is an emerging and distributed computing mode in cloud service systems, and has become an interesting research direction for both academia and industry. Note that the cloud service systems always display new characteristics, such as stochasticity, large scale, loose coupling, concurrency, non-homogeneity and heterogeneity, thus their load balancing investigation has been more interesting, difficult and challenging until now. By using resource management and job scheduling, this paper proposes an integrated, real-time and dynamic control mechanism for large-scale cloud service systems and their load balancing through combining supermarket models with not only work stealing models but also scheduling of public reserved resource. To this end, this paper provides a novel stochastic model with weak interactions by means of nonlinear Markov processes. To overcome theoretical difficulties growing out of the state explosion in high-dimensional stochastic systems, this paper applies the mean-field theory to develop a macro computational technique in terms of an infinite-dimensional system of mean-field equations. Furthermore, this paper proves the asymptotic independence of the large-scale cloud service system, and show how to compute the fixed point by virtue of an infinite-dimensional system of nonlinear equations. Based on the fixed point, this paper provides effective numerical computation for performance analysis of this system under a high approximate precision. Therefore, we hope that the methodology and results given in this paper can be applicable to the study of more general large-scale cloud service systems.

A Distributed Dynamic Adaptive and Fast Balancing SDN Controller Management

Tradition Software Defined Network (SDN) suffers from single point failure and uneven load distribution problems. Multiple SDN controllers work together to construct a distributed dynamic adaptive cluster with dynamic load balancing for large scale SDN switch stream requesting. Introducing such features as CPU, memory and network load, a Dynamic Adaptive and Fast algorithm (DAF) can select source, target and destination SDN controllers to balance load dynamically. The results of experiments show that DAF works well and improve the cloud service system throughput.

A Secure High-Order CFS Algorithm on Clouds for Industrial Internet of Things

To uncover latent data structures in big data, a high-order clustering algorithm by fast search and find of density peaks has emerged recently, and will bring great application values in industrial Internet-of-Things data management and analysis. With the popularity of cloud computing, it provides users with the convenience of outsourcing calculations while bringing the risk of privacy disclosure. Aiming at the problem above, from the characteristics of the secure cloud service system, this paper proposes a secure high-order clustering algorithm by fast search and find of density peaks on hybrid cloud. In the proposed scheme, the client first builds the encrypted object tensors with user data using homomorphic encryption, then uploads them to the cloud to completely implement the proposed protocols. In the end, clustering results perturbed with random numbers are returned to client for remove perturbation. The performance of the proposed method is evaluated on a smart grid dataset in terms of clustering accuracy, efficiency, and speedup ratio. Experimental results reveal that the proposed approach can accurately and effectively cluster big data without disclosing user privacy while ensuring that the client is very lightweight. Therefore, the proposed scheme with high security and scalability is suitable for clustering industrial Internet-of-Things big data.

A cloud service adaptive framework based on reliable resource allocation

In the process of resource allocation in the cloud service system, the failure of the resource node could cause the failure of cloud service. It not only reduced the quality of service, but produced a lot of “extra” energy consumption. Based on this problem, the paper proposed a reliable resource supply scheme for cloud service, on the basis of reliable resource supply, constructed the cloud service adaptive frame Cloud_RRSSF. The framework can provide adaptive adjustment of cloud service according to specific conditions when the user supply or service abnormality occurs. The experimental results showed that the Cloud_RRSSF proposed in this paper can provide reliable resource supply, it can also efficiently reduce the consumption of the data center.

Trust-Based Multi-Objectives Task Assignment Model in Cloud Service System

Trust-Based Multi-Objectives Task Assignment Model in Cloud Service System