Research on the Application of Big Data and Cloud Computing Technology in Smart Campus

The current education field is experiencing an innovation driven by big data and cloud technologies,and these advanced technologies play a central role in the construction of smart campuses.Big data technology has a wide range of applications in student learning behavior analysis,teaching resource management,campus safety monitoring,and decision support,which improves the quality of education and management efficiency.Cloud computing technology supports the integration,distribution,and optimal use of educational resources through cloud resource sharing,virtual classrooms,intelligent campus management systems,and Infrastructure-as-a-Service(IaaS)models,which reduce costs and increase flexibility.This paper comprehensively discusses the practical application of big data and cloud computing technologies in smart campuses,showing how these technologies can contribute to the development of smart campuses,and laying the foundation for the future innovation of education models.

A Method for Trust Management in Cloud Computing: Data Coloring by Cloud Watermarking

With the development of Internet technology and human computing, the computing environment has changed dramatically over the last three decades. Cloud computing emerges as a paradigm of Internet computing in which dynamical, scalable and often virtuMized resources are provided as services. With virtualization technology, cloud computing offers diverse services （such as virtual computing, virtual storage, virtual bandwidth, etc.） for the public by means of multi-tenancy mode. Although users are enjoying the capabilities of super-computing and mass storage supplied by cloud computing, cloud security still remains as a hot spot problem, which is in essence the trust management between data owners and storage service providers. In this paper, we propose a data coloring method based on cloud watermarking to recognize and ensure mutual reputations. The experimental results show that the robustness of reverse cloud generator can guarantee users＇ embedded social reputation identifications. Hence, our work provides a reference solution to the critical problem of cloud security.

Big Data of Home Energy Management in Cloud Computing

A smart grid is the evolved form of the power grid with the integration of sensing,communication,computing,monitoring,and control technologies.These technologies make the power grid reliable,efficient,and economical.However,the smartness boosts the volume of data in the smart grid.To obligate full benefits,big data has attractive techniques to process and analyze smart grid data.This paper presents and simulates a framework to make sure the use of big data computing technique in the smart grid.The offered framework comprises of the following four layers:(i)Data source layer,(ii)Data transmission layer,(iii)Data storage and computing layer,and(iv)Data analysis layer.As a proof of concept,the framework is simulated by taking the dataset of three cities of the Pakistan region and by considering two cloud-based data centers.The results are analyzed by taking into account the following parameters:(i)Heavy load data center,(ii)The impact of peak hour,(iii)High network delay,and(iv)The low network delay.The presented framework may help the power grid to achieve reliability,sustainability,and cost-efficiency for both the users and service providers.

Cloud Computing: Purpose and Future

Cloud computing is the new norm within business entities as businesses try to keep up with technological advancements and user needs. The concept is defined as a computing environment allowing for remote outsourcing of storage and computing resources. A hybrid cloud environment is an excellent example of cloud computing. Specifically, the hybrid system provides organizations with increased scalability and control over their data and support for a remote workforce. However, hybrid cloud systems are expensive as organizations operate different infrastructures while introducing complexity to the organization’s activities. Data security is critical among the most vital concerns that have resulted from the use of cloud computing, thus, affecting the rate of user adoption and acceptance. This article, borrowing from the hybrid cloud computing system, recommends combining traditional and modern data security systems. Traditional data security systems have proven effective in their respective roles, with the main challenge arising from their recognition of context and connectivity. Therefore, integrating traditional and modern designs is recommended to enhance effectiveness, context, connectivity, and efficiency.

On the Privacy-Preserving Outsourcing Scheme of Reversible Data Hiding over Encrypted Image Data in Cloud Computing

Advanced cloud computing technology provides cost saving and flexibility of services for users.With the explosion of multimedia data,more and more data owners would outsource their personal multimedia data on the cloud.In the meantime,some computationally expensive tasks are also undertaken by cloud servers.However,the outsourced multimedia data and its applications may reveal the data owner’s private information because the data owners lose the control of their data.Recently,this thought has aroused new research interest on privacy-preserving reversible data hiding over outsourced multimedia data.In this paper,two reversible data hiding schemes are proposed for encrypted image data in cloud computing:reversible data hiding by homomorphic encryption and reversible data hiding in encrypted domain.The former is that additional bits are extracted after decryption and the latter is that extracted before decryption.Meanwhile,a combined scheme is also designed.This paper proposes the privacy-preserving outsourcing scheme of reversible data hiding over encrypted image data in cloud computing,which not only ensures multimedia data security without relying on the trustworthiness of cloud servers,but also guarantees that reversible data hiding can be operated over encrypted images at the different stages.Theoretical analysis confirms the correctness of the proposed encryption model and justifies the security of the proposed scheme.The computation cost of the proposed scheme is acceptable and adjusts to different security levels.

A Data Security Framework for Cloud Computing Services

Cyberattacks are difficult to prevent because the targeted companies and organizations are often relying on new and fundamentally insecure cloudbased technologies,such as the Internet of Things.With increasing industry adoption and migration of traditional computing services to the cloud,one of the main challenges in cybersecurity is to provide mechanisms to secure these technologies.This work proposes a Data Security Framework for cloud computing services(CCS)that evaluates and improves CCS data security from a software engineering perspective by evaluating the levels of security within the cloud computing paradigm using engineering methods and techniques applied to CCS.This framework is developed by means of a methodology based on a heuristic theory that incorporates knowledge generated by existing works as well as the experience of their implementation.The paper presents the design details of the framework,which consists of three stages:identification of data security requirements,management of data security risks and evaluation of data security performance in CCS.

Risk Analysis of Big Data Based on Cloud Computing for the Inspection and Testing of Toxic and Hazardous Substances in Meat Products

Large-scale data emerge in food safety inspection and testing industry with the development of Internet technology in China.This paper was aimed at designing toxic and hazardous substance big data risk analysis algorithm in food safety inspection and testing based on cloud computing^([1]).Cloud computing platform was set up to store the massive extensive data with geographical distribution,dynamic and high complexity from the Internet,and MapReduce^([2]) computational framework in cloud computing was applied to process and compute parallel data.The risk analysis results were obtained by analyzing 1000000 meat products testing data collected from the laboratory management information system based on web.The results show that food safety index IFS < 1,which proves that the food safety state is in good condition.

Scalability of the DVFS Power Management Technique as Applied to 3-Tier Data Center Architecture in Cloud Computing

The increase in computing capacity caused a rapid and sudden increase in the Operational Expenses (OPEX) of data centers. OPEX reduction is a big concern and a key target in modern data centers. In this study, the scalability of the Dynamic Voltage and Frequency Scaling (DVFS) power management technique is studied under multiple different workloads. The environment of this study is a 3-Tier data center. We conducted multiple experiments to find the impact of using DVFS on energy reduction under two scheduling techniques, namely: Round Robin and Green. We observed that the amount of energy reduction varies according to data center load. When the data center load increases, the energy reduction decreases. Experiments using Green scheduler showed around 83% decrease in power consumption when DVFS is enabled and DC is lightly loaded. In case the DC is fully loaded, in which case the servers’ CPUs are constantly busy with no idle time, the effect of DVFS decreases and stabilizes to less than 10%. Experiments using Round Robin scheduler showed less energy saving by DVFS, specifically, around 25% in light DC load and less than 5% in heavy DC load. In order to find the effect of task weight on energy consumption, a set of experiments were conducted through applying thin and fat tasks. A thin task has much less instructions compared to fat tasks. We observed, through the simulation, that the difference in power reduction between both types of tasks when using DVFS is less than 1%.

A Review of Dynamic Resource Management in Cloud Computing Environments

In a cloud environment, Virtual Machines (VMs) consolidation andresource provisioning are used to address the issues of workload fluctuations.VM consolidation aims to move the VMs from one host to another in order toreduce the number of active hosts and save power. Whereas resource provisioningattempts to provide additional resource capacity to the VMs as needed in order tomeet Quality of Service (QoS) requirements. However, these techniques have aset of limitations in terms of the additional costs related to migration and scalingtime, and energy overhead that need further consideration. Therefore, this paperpresents a comprehensive literature review on the subject of dynamic resourcemanagement (i.e., VMs consolidation and resource provisioning) in cloud computing environments, along with an overall discussion of the closely relatedworks. The outcomes of this research can be used to enhance the developmentof predictive resource management techniques, by considering the awareness ofperformance variation, energy consumption and cost to efficiently manage thecloud resources.

A Resource Management Algorithm for Virtual Machine Migration in Vehicular Cloud Computing

In recent years,vehicular cloud computing(VCC)has gained vast attention for providing a variety of services by creating virtual machines(VMs).These VMs use the resources that are present in modern smart vehicles.Many studies reported that some of these VMs hosted on the vehicles are overloaded,whereas others are underloaded.As a circumstance,the energy consumption of overloaded vehicles is drastically increased.On the other hand,underloaded vehicles are also drawing considerable energy in the underutilized situation.Therefore,minimizing the energy consumption of the VMs that are hosted by both overloaded and underloaded is a challenging issue in the VCC environment.The proper and efcient utilization of the vehicle’s resources can reduce energy consumption signicantly.One of the solutions is to improve the resource utilization of underloaded vehicles by migrating the over-utilized VMs of overloaded vehicles.On the other hand,a large number of VM migrations can lead to wastage of energy and time,which ultimately degrades the performance of the VMs.This paper addresses the issues mentioned above by introducing a resource management algorithm,called resource utilization-aware VM migration(RU-VMM)algorithm,to distribute the loads among the overloaded and underloaded vehicles,such that energy consumption is minimized.RU-VMM monitors the trend of resource utilization to select the source and destination vehicles within a predetermined threshold for the process of VM migration.It ensures that any vehicles’resource utilization should not exceed the threshold before or after the migration.RU-VMM also tries to avoid unnecessary VM migrations between the vehicles.RU-VMM is extensively simulated and tested using nine datasets.The results are carried out using three performance metrics,namely number of nal source vehicles(nfsv),percentage of successful VM migrations(psvmm)and percentage of dropped VM migrations(pdvmm),and compared with threshold-based algorithm(i.e.,threshold)and cumulative sum(CUSUM)algorithm.The comparisons show that the RU-VMM algorithm performs better than the existing algorithms.RU-VMM algorithm improves 16.91%than the CUSUM algorithm and 71.59%than the threshold algorithm in terms of nfsv,and 20.62%and 275.34%than the CUSUM and threshold algorithms in terms of psvmm.

Improving Performance of Cloud Computing and Big Data Technologies and Applications

Cloud computing technology is changing the development and usage patterns of IT infrastructure and applications. Virtualized and distributed systems as well as unified management and scheduling has greatly im proved computing and storage. Management has become easier, andOAM costs have been significantly reduced. Cloud desktop technology is develop ing rapidly. With this technology, users can flexibly and dynamically use virtual ma chine resources, companies＇ efficiency of using and allocating resources is greatly improved, and information security is ensured. In most existing virtual cloud desk top solutions, computing and storage are bound together, and data is stored as im age files. This limits the flexibility and expandability of systems and is insufficient for meetinz customers＇ requirements in different scenarios.

Design and Implementation of a Project Management System Based on Product Data Management on the Baidu Cloud Computing Platform

Aiming at enterprises without commercial project management systems(PMS)in a product data management(PDM)environment,and using a cloud computing platform,this research analyses the business process and function of complex product project management in PDM,and proposes a PMS-based organizational structure for such a project.This model consists of a task view,user view,role view,and product view.In addition,it designs the function structure,E-R model and logical model of a PMS database,and also presents an architecture based on the Baidu cloud platform,describes the functions of the Baidu App Engine(BAE),establishes the overall PMS software architecture.Finally,it realizes a revised product design project by using EasyUI,J2EE and other related technologies.Practice shows that the PMS designed for PDM has availability,scalability,reliability and security with the help of the Baidu cloud computing platform.It can provide a reference for small-and medium-sized enterprises seeking to implement information systems with high efficiency and at low cost in the age of big data.

Research on data pre-deployment in information service flow of digital ocean cloud computing

Data pre-deployment in the HDFS （Hadoop distributed file systems） is more complicated than that in traditional file systems. There are many key issues need to be addressed, such as determining the target location of the data prefetching, the amount of data to be prefetched, the balance between data prefetching services and normal data accesses. Aiming to solve these problems, we employ the characteristics of digital ocean information service flows and propose a deployment scheme which combines input data prefetching with output data oriented storage strategies. The method achieves the parallelism of data preparation and data processing, thereby massively reducing I/O time cost of digital ocean cloud computing platforms when processing multi-source information synergistic tasks. The experimental results show that the scheme has a higher degree of parallelism than traditional Hadoop mechanisms, shortens the waiting time of a running service node, and significantly reduces data access conflicts.

Reversible Data Hiding for Medical Images in Cloud Computing Environments Based on Chaotic Hénon Map

Reversible data hiding techniques are capable of reconstructing the original cover image from stego-images. Recently, many researchers have focused on reversible data hiding to protect intellectual property rights. In this paper, we combine reversible data hiding with the chaotic Henon map as an encryption technique to achieve an acceptable level of confidentiality in cloud computing environments. And, Haar digital wavelet transformation （HDWT） is also applied to convert an image from a spatial domain into a frequency domain. And then the decimal of coefficients and integer of high frequency band are modified for hiding secret bits. Finally, the modified coefficients are inversely transformed to stego-images.

Development of Crisis Management Models Combined with Cloud Computing

Three monitor models of enterprise crisis were introduced,i.e.,the monitoring model of enterprise crisis based on intelligent Meta search,the enterprise crisis management model based on artificial neural network and the combined early-warning model.Combined with the advantages of cloud computing,the prominent crisis management models are improved and more efficient,comprehensive and accurate in enterprise crisis management.Through the empirical study of the models,cloud computing makes the early warning structures of enterprise crisis tend to be more simple and efficient,cloud computing can effectively enhance the recognition ability and learning ability of the crisis management,and cloud computing can keep data information updating and realize the dynamic management of enterprise joint early-warning.At the same time,according to the comparative analysis and the experimental result,the crisis management models based on cloud computing also need some improvements.

Towards Developing Privacy-Preserved Data Security Approach(PP-DSA)in Cloud Computing Environment

In the present scenario of rapid growth in cloud computing models,several companies and users started to share their data on cloud servers.However,when the model is not completely trusted,the data owners face several security-related problems,such as user privacy breaches,data disclosure,data corruption,and so on,during the process of data outsourcing.For addressing and handling the security-related issues on Cloud,several models were proposed.With that concern,this paper develops a Privacy-Preserved Data Security Approach(PP-DSA)to provide the data security and data integrity for the out-sourcing data in Cloud Environment.Privacy preservation is ensured in this work with the Efficient Authentication Technique(EAT)using the Group Signature method that is applied with Third-Party Auditor(TPA).The role of the auditor is to secure the data and guarantee shared data integrity.Additionally,the Cloud Service Provider(CSP)and Data User(DU)can also be the attackers that are to be handled with the EAT.Here,the major objective of the work is to enhance cloud security and thereby,increase Quality of Service(QoS).The results are evaluated based on the model effectiveness,security,and reliability and show that the proposed model provides better results than existing works.

A Secure Method for Data Storage and Transmission in Sustainable Cloud Computing

Cloud computing is a technology that provides secure storage space for the customer’s massive data and gives them the facility to retrieve and transmit their data efficiently through a secure network in which encryption and decryption algorithms are being deployed.In cloud computation,data processing,storage,and transmission can be done through laptops andmobile devices.Data Storing in cloud facilities is expanding each day and data is the most significant asset of clients.The important concern with the transmission of information to the cloud is security because there is no perceivability of the client’s data.They have to be dependent on cloud service providers for assurance of the platform’s security.Data security and privacy issues reduce the progression of cloud computing and add complexity.Nowadays;most of the data that is stored on cloud servers is in the form of images and photographs,which is a very confidential form of data that requires secured transmission.In this research work,a public key cryptosystem is being implemented to store,retrieve and transmit information in cloud computation through a modified Rivest-Shamir-Adleman(RSA)algorithm for the encryption and decryption of data.The implementation of a modified RSA algorithm results guaranteed the security of data in the cloud environment.To enhance the user data security level,a neural network is used for user authentication and recognition.Moreover;the proposed technique develops the performance of detection as a loss function of the bounding box.The Faster Region-Based Convolutional Neural Network(Faster R-CNN)gets trained on images to identify authorized users with an accuracy of 99.9%on training.

Construction and Application of Cloud Computing Model for Reciprocal and Collaborative Knowledge Management

Promoting the co-constructing and sharing of organizational knowledge and improving organizational performance have always been the core research subject of knowledge management.Existing research focuses on the construction of knowledge management systems and knowledge sharing and transfer mechanisms.With the rapid development and application of cloud computing and big data technology,knowledge management is faced with many problems,such as how to combine with the new generation of information technology,how to achieve integration with organizational business processes,and so on.To solve such problems,this paper proposes a reciprocal collaborative knowledge management model(RCKMmodel)based on cloud computing technology,reciprocity theory,and collaboration technology.RCKM model includes project group management and cloud computing technology,which can realize management,finance,communication,and quality assurance of multiple projects and solve the problem of business integration with knowledge management.This paper designs evaluation methods of tacit knowledge and reciprocity preference based on the Bayesian formula and analyzes their effect with simulation data.The methods can provide quantitative support for the integration of knowledge management and business management to realize reciprocity and collaboration in the RCKM model.The research found that RCKM model can fully use cloud computing technology to promote the integration of knowledge management and organizational business,and the evaluation method based on the Bayesian formula can provide relatively accurate data support for the evaluation and selection of project team members.

Research on Electronic Document Management System Based on Cloud Computing

With the development of information technology,cloud computing technology has brought many conveniences to all aspects of work and life.With the continuous promotion,popularization and vigorous development of e-government and e-commerce,the number of documents in electronic form is getting larger and larger.Electronic document is an indispensable main tool and real record of e-government and business activities.How to scientifically and effectively manage electronic documents?This is an important issue faced by governments and enterprises in improving management efficiency,protecting state secrets or business secrets,and reducing management costs.This paper discusses the application of cloud computing technology in the construction of electronic file management system,proposes an architecture of electronic file management system based on cloud computing,and makes a more detailed discussion on key technologies and implementation.The electronic file management system is built on the cloud architecture to enable users to upload,download,share,set security roles,audit,and retrieve files based on multiple modes.An electronic file management system based on cloud computing can make full use of cloud storage,cloud security,and cloud computing technologies to achieve unified,reliable,and secure management of electronic files.

Advanced Authentication Mechanisms for Identity and Access Management inCloud Computing

Identity management is based on the creation and management of useridentities for granting access to the cloud resources based on the user attributes.The cloud identity and access management (IAM) grants the authorization tothe end-users to perform different actions on the specified cloud resources. Theauthorizations in the IAM are grouped into roles instead of granting them directlyto the end-users. Due to the multiplicity of cloud locations where data resides anddue to the lack of a centralized user authority for granting or denying cloud userrequests, there must be several security strategies and models to overcome theseissues. Another major concern in IAM services is the excessive or the lack ofaccess level to different users with previously granted authorizations. This paperproposes a comprehensive review of security services and threats. Based on thepresented services and threats, advanced frameworks for IAM that provideauthentication mechanisms in public and private cloud platforms. A threat modelhas been applied to validate the proposed authentication frameworks with different security threats. The proposed models proved high efficiency in protectingcloud platforms from insider attacks, single sign-on failure, brute force attacks,denial of service, user privacy threats, and data privacy threats.