Cloud Storage System Research Articles

Nursing students' reflections on vaccine administration during the COVID-19 global pandemic.

BackgroundDuring the COVID-19 pandemic, many nursing schools limited in-person clinical instruction to lower the risk of student exposure to SARS-CoV-2. Some U.S. state boards of nursing authorized virtual learning experiences to attempt to fill this void. The effects of restricting such hands-on training are not fully understood, but we believed it could be detrimental to student development and saw partnering with local COVID-19 vaccination clinic as a promising alternative. Between January and April 2021, second semester pre-licensure nursing students assisted at the clinic and submitted reflections on the experience. The aim of this study was to assess the effectiveness of this educational encounter.MethodsOne hundred seventy-one students submitted reflections on their experience, which were de-identified and uploaded to a HIPAA- and FERPA-compliant cloud storage system using SAFE desktop and coded for thematic analysis.ResultsAnalysis revealed five major themes: community, socializing, perceived confidence, impact, and professional role.ConclusionThis study demonstrated the viability of instruction at a COVID-19 vaccination clinic as an alternative learning experience for nursing students encountering restricted face-to-face clinical training. It suggests that schools can develop other novel clinical experiences to increase students' perceived confidence, provide opportunities to practice skills, and gain insights into nursing practice.

Practical Use of Secret Sharing for Enhancing Privacy in Clouds

The topic of this contribution is the practical use of secret sharing schemes in securing private data. In the first part, we focus on the security model and the overview of the available solutions. In the second part, we describe our solution for storing sensitive data in commercial cloud storage systems, an application called Datachest. The application uploads the data in encrypted form. Cryptographic keys are divided into shares. Each cloud receives one share. This solution improves the security of users’ sensitive data in the cloud. In the final part, we explore the ramifications of secret sharing on the security and management of IoT devices, especially home appliances.

Availability of digital resources and institutional compliance with COVID-19 mitigation measures in a Nigerian university: A descriptive study

The state of the availability of digital resources and institutional compliance to COVID-19 mitigation measures was evaluated by the researchers in this study. Informed by the need to answer two research questions, the study adopted the descriptive survey design. A sample of 409 participants was drawn from a population of 2,410 academic staff at the University of Calabar, leveraging the multistage sampling process. “Availability of digital resources and institutional compliance with COVID-19 mitigation measures questionnaire” was used for data collection. After administration, we recovered 397 copies of the instrument successfully. We used descriptive statistics to answer the research questions. Our findings indicated a great extent in the availability of digital resources such as computers (91.18%), human capacity (63.48%), network printers (67.76%), projectors (59.19%), websites (92.95%), and wireless networks (86.65%). There was a low extent in the availability of digital resources such as local area network (5.79%), cloud storage systems (2.27%), digital libraries (7.56%), firewalls (1.26%), Internet service (48.36%), routers (23.69%), and Zoom videoconferencing platform (23.93%). The unavailability of digital resources such as host/servers (0%) and virtual private networks (0%) in the higher education institution (HEI). Further evidence shows a low extent of institutional compliance with COVID-19 mitigation measures generally and specifically for all indicators. In conclusion, there is a high degree of unreadiness to promote e-learning when flexible learning systems should operate in the institution. Among others, we recommend that the management of the HEI should apportion proceeds from internally generated funds to procure digital resources to increase the institution's capacity to respond to COVID-19 or similar cases in the future.

CORPORATE INFORMATION MANAGEMENT PLATFORM BASED ON E-COMMERCE ENVIRONMENT: IN THE CONTEXT OF ENTREPRENEURSHIP

Addressing the e-commerce environment, this study is designed to validate the feasibility of an enterprise information management platform based on cloud storage technology and system testing. Experimental results show that the platform designed in this article has approximately 195 fluctuating processes per second in the average number of transactions per second, which has a better test effect in terms of business efficiency. In addition, other performance index tests have reached the standard. These include a user authentication time of just 0.96 seconds, a material information input time of 2.54 seconds, a material information query response time of only 0.97 seconds, and a relatively fast response time. This research is expected to provide an efficient information management platform for the entire production process for the sales of the enterprise and encourage the development of business management informatics in Turkey.

Research on Cloud Storage Technology in Digital Campus Big Data Scenario

Realizing campus intelligent management and service mode by combining cloud storage technology is the focus of current research in digital campus construction work. By analyzing the IT development trend of digital campus, the defective problems of traditional storage system, from the architecture design and platform construction of cloud storage system for digital campus, we propose a cloud storage construction scheme for campus big data scenario, and discuss the application of virtualization, streamlined configuration, storage optimization and other technologies in cloud storage. The cloud storage technology is applied in the construction of digital campus, which strongly accelerates the process of digital campus informatization.

Dynamic Deduplication Algorithm for Cross-User Duplicate Data in Hybrid Cloud Storage

The escalating growth of distributed big data in hybrid cloud storage architecture introduces a new set of challenges. Constantly, content enrichment puts pressure on capacity. Nonetheless, the explosion of user data places a significant strain on broadband and storage capacity. Consequently, many cloud storage providers will implement deduplication to compress data, reduce transfer bandwidth, and reduce cloud storage space. In cloud storage systems, it is a data compression and storage optimization method. By locating and removing redundant data, it can save storage space and bandwidth. An MTHDedup deduplication strategy based on the Merkle hash tree is presented in a hybrid cloud environment to address the issue of convergent encryption algorithms being susceptible to brute-force attacks and ciphertext computation time overhead. Merkle hash trees are constructed using additional encryption algorithms to generate encryption keys during file- and block-level deduplication, ensuring that generated ciphertexts are unpredictable. The method is effective against both internal and external brute-force attacks, thereby increasing data security. Our method reduces the computational burden of ciphertext generation and the key storage space, and the performance advantage increases with the number of privilege sets.

Review on Artificial Intelligence and its Implementations in Digital Era

Artificial Intelligence (AI) moderns the present society and opens the door for many applications to boom digitally. AI plays a key role in the field of agriculture to space science. The development on cloud storage and wireless communication systems are assisting the AI to reach certain goals in the digital world. Also the improvements made to the electronic processors and other chips are helping the AI to gain its computational efficiency. The design of micro electronic devices are opening the possibility of moving the sensors to keep in distinguished locations though Internet of Things (IoT) communication. The objective of the paper is to review the performances of AI methodologies in various applications for identifying the research gaps.

Blockchain-Based Data Sharing Approach Considering Educational Data

The innovation of cloud computing technologies gained huge interest wherein more individuals started to outsource their data to cloud-based servers. However, open networks and untrusted cloud platforms face privacy and security issues while outsourcing data to the cloud. The proposed Blockchain-based data sharing mechanism effectively solves failure issues of the cloud storage system and provides several benefits by maximizing throughput. This framework embeds the interplanetary file system and blockchain for effective data sharing between cloud owners and the requester. Here, the system comprises five different entities and the proposed method involves six different phases. This model acquires interplanetary file system (IPFS) for effective data sharing between cloud owners and the requester. The proposed method provides superior performance in contrast to other techniques with the highest genuine detection rate of 0.880, minimal communication overhead of 0.642, and maximal private rate of 0.564.

Performance and time improvement of LT code-based cloud storage

Outsourcing data on cloud storage services has already attracted great attention due to the prospect of rapid data growth and storing efficiencies for customers. The coding-based cloud storage approach can offer a more reliable and faster solution with less storage space in comparison with replication-based cloud storage. LT codes are the famous member of the rateless code family that can improve performance of storage systems utilizing good degree distributions. Since degree distribution plays a key role in LT codes performance, recently introduced Poisson robust soliton distribution (PRSD) and combined Poisson robust soliton distribution (CPRSD) motivate us to investigate LT code-based cloud storage system. So, we exploit LT codes with new degree distributions to provide lower average degree and higher decoding efficiency, specifically when receiving fewer encoding symbols, compared with popular degree distribution, robust soliton distribution (RSD). In this paper, we show that proposed cloud storage outperforms traditional ones in terms of storage space and robustness encountering unavailability of encoding symbols, due to compatible properties of PRSD and CPRSD with cloud storage essence. Furthermore, a modified decoding process is presented based on required encoding symbols behavior to reduce data retrieval time. Numerical results confirm improvement of cloud storage performance.

Integrity Auditing for Multi-Copy in Cloud Storage Based on Red-Black Tree

With the rapid development of cloud storage, cloud users are willing to store data in the cloud storage system, and at the same time, the requirements for the security, integrity, and availability of data storage are getting higher and higher. Although many cloud audit schemes have been proposed, the data storage overhead is too large and the data cannot be dynamically updated efficiently when most of the schemes are in use. In order to solve these problems, a cloud audit scheme for multi-copy dynamic data integrity based on red-black tree full nodes is proposed. This scheme uses ID-based key authentication, and improves the classic Merkel hash tree MHT to achieve multi-copy storage and dynamic data manipulation, which improves the efficiency of real-time dynamic data update (insertion, deletion, modification). The third-party audit organization replaces users to verify the integrity of data stored on remote cloud servers, which reduces the computing overhead and system communication overhead. The security analysis proves that the security model based on the CDH problem and the DL problem is safe. Judging from the results of the simulation experiment, the scheme is safe and efficient.

A Cloud Secure Storage Mechanism Based on Data Dispersion and Encryption

This describes cloud storage system, it support for rapid development of cloud computing data leakage prevention algorithm and it implemented secure storage mechanism. CSSM adopted a hierarchical management approach and combined user password with secret sharing to prevent cryptographic materials leakage. Advanced encryption standard algorithm(AES) is implemented for encryption of data. The proposed scheme ensuring the reduced overhead and latency in system. The objective is to secure the cloud data with reduced leakage system. The main objective of the proposed mechanism is to secure cloud storage against data breach, which may be the result of targeted attack or management negligence (e.g. misconfiguration), in case hackers even some malicious administrator is able to steal user data.

StoreSim: Optimizing Information Leakage in Multi-Cloud Storage Services

Abstract: Many schemes have been recently advanced for storing data on multiple clouds. Distributing data over different cloud storage providers (CSPs) automatically provides users with a certain degree of information leakage control, as no single point of attack can leak all a user’s information. However, unplanned distribution of data chunks can lead to high information disclosure even while using multiple clouds. In this paper, to address this problem, we present Store-Sim, an information leakage-aware storage system in multi cloud. Store-Sim aims to store syntactically similar data on the same cloud, thus minimizing the user’s information leakage across multiple clouds. We design an approximate algorithm to efficiently generate similarity- preserving signatures for data chunks based on Min-Hash and Bloom filters, and design a function to compute the information leakage based on these signatures. Next, we present an effective storage plan generation algorithm based on clustering for distributing data chunks with minimal information leakage across multiple clouds. Finally, we evaluated our scheme using two real datasets from Wikipedia and GitHub. We show that our scheme can reduce information leakage by up to 60-70 Percent. Keywords: Min-Hash, Bloom-Filter, Multi-Cloud, StoreSim, Data Leakage.

PGCE: A distributed storage causal consistency model based on partial geo-replication and cloud-edge collaboration architecture

The current causal consistency model has trouble facing the high synchronization overheads and response delays found in cloud storage systems. This paper proposes a causal consistency model for distributed storage based on partial geographical replication and Cloud-Edge collaboration structure (PGCE). The model is based on the distributed network architecture of Cloud-Edge collaboration, and the cloud dataset is divided into multiple subsets by a hash function to store these subsets in the edge nodes that are close to the user network to realize partial geo-replication. At the same time, the timestamp stabilization mechanism and metadata processing service are set up to implement data consistency between nodes on the premise of ensuring causality, reducing the overhead of metadata processing and data synchronization. The client interacts directly with the edge nodes, which reduces the response delay of interacts with the cloud DC. An evaluation of the PGCE compared with existing models shows that it has better performance in response latency and throughput.

Evolutionary privacy-preserving learning strategies for edge-based IoT data sharing schemes

The fast proliferation of edge devices for the Internet of Things (IoT) has led to massive volumes of data explosion. The generated data is collected and shared using edge-based IoT structures at a considerably high frequency. Thus, the data-sharing privacy exposure issue is increasingly intimidating when IoT devices make malicious requests for filching sensitive information from a cloud storage system through edge nodes. To address the identified issue, we present evolutionary privacy preservation learning strategies for an edge computing-based IoT data sharing scheme. In particular, we introduce evolutionary game theory and construct a payoff matrix to symbolize intercommunication between IoT devices and edge nodes, where IoT devices and edge nodes are two parties of the game. IoT devices may make malicious requests to achieve their goals of stealing privacy. Accordingly, edge nodes should deny malicious IoT device requests to prevent IoT data from being disclosed. They dynamically adjust their own strategies according to the opponent's strategy and finally maximize the payoffs. Built upon a developed application framework to illustrate the concrete data sharing architecture, a novel algorithm is proposed that can derive the optimal evolutionary learning strategy. Furthermore, we numerically simulate evolutionarily stable strategies, and the final results experimentally verify the correctness of the IoT data sharing privacy preservation scheme. Therefore, the proposed model can effectively defeat malicious invasion and protect sensitive information from leaking when IoT data is shared.

Deep CNN based online image deduplication technique for cloud storage system

Deep CNN based online image deduplication technique for cloud storage system

A Study of Failure Recovery and Logging of High-Performance Parallel File Systems

Large-scale parallel file systems (PFSs) play an essential role in high-performance computing (HPC). However, despite their importance, their reliability is much less studied or understood compared with that of local storage systems or cloud storage systems. Recent failure incidents at real HPC centers have exposed the latent defects in PFS clusters as well as the urgent need for a systematic analysis. To address the challenge, we perform a study of the failure recovery and logging mechanisms of PFSs in this article. First, to trigger the failure recovery and logging operations of the target PFS, we introduce a black-box fault injection tool called PFault , which is transparent to PFSs and easy to deploy in practice. PFault emulates the failure state of individual storage nodes in the PFS based on a set of pre-defined fault models and enables examining the PFS behavior under fault systematically. Next, we apply PFault to study two widely used PFSs: Lustre and BeeGFS. Our analysis reveals the unique failure recovery and logging patterns of the target PFSs and identifies multiple cases where the PFSs are imperfect in terms of failure handling. For example, Lustre includes a recovery component called LFSCK to detect and fix PFS-level inconsistencies, but we find that LFSCK itself may hang or trigger kernel panics when scanning a corrupted Lustre. Even after the recovery attempt of LFSCK, the subsequent workloads applied to Lustre may still behave abnormally (e.g., hang or report I/O errors). Similar issues have also been observed in BeeGFS and its recovery component BeeGFS-FSCK. We analyze the root causes of the abnormal symptoms observed in depth, which has led to a new patch set to be merged into the coming Lustre release. In addition, we characterize the extensive logs generated in the experiments in detail and identify the unique patterns and limitations of PFSs in terms of failure logging. We hope this study and the resulting tool and dataset can facilitate follow-up research in the communities and help improve PFSs for reliable high-performance computing.

Optimization of Network Furniture Management System Based on Big Data

In order to make the traditional household equipment can be remotely controlled, wireless networking technology is introduced into the traditional furniture equipment to achieve the effect of access and remote control. In view of the problem that the traditional cloud storage system lacks flexibility, opacity, weak robustness and cannot effectively store, manage, and maintain big data in data storage, a big data oriented cloud storage system is designed and implemented to intelligently process the business requests of the furniture system through the logic control module. The user access module is used to provide users with various functions realized at the bottom, the storage module is used to provide transparent data access function for the furniture system, and the file read/write module is used to isolate the upper logic processing from the lower storage, so as to make the designed system more robust. In the process of software design, the program code of data cloud storage is given to realize big data cloud storage. The experimental results show that the new generation of network furniture cloud storage systems has high feasibility and practicability.

Efficient Load Balancing for Cloud Computing by Using Content Analysis

Nowadays, computer networks have grown rapidly due to the demand for information technology management and facilitation of greater functionality. The service provided based on a single machine cannot accommodate large databases. Therefore, single servers must be combined for server group services. The problem in grouping server service is that it is very hard to manage many devices which have different hardware. Cloud computing is an extensive scalable computing infrastructure that shares existing resources. It is a popular option for people and businesses for a number of reasons including cost savings and security. This paper aimed to propose an efficient technique of load balance control by using HA Proxy in cloud computing with the objective of receiving and distributing the workload to the computer server to share the processing resources. The proposed technique applied round-robin scheduling for an efficient resource management of the cloud storage systems that focused on an effective workload balancing and a dynamic replication strategy. The evaluation approach was based on the benchmark data from requests per second and failed requests. The results showed that the proposed technique could improve performance of load balancing by 1,000 request /6.31 sec in cloud computing and generate fewer false alarms

A Noninteractive Multireplica Provable Data Possession Scheme Based on Smart Contract

With the explosive growth of data, cloud storage has become a widely used storage method. To protect the integrity and availability of data in cloud storage systems, multireplica provable data possession has gradually become a research hotspot. This paper uses smart contracts to replace traditional third-party auditor (TPA) and proposes a noninteractive multireplica provable data possession scheme based on smart contracts, making the verification process public, immutable, traceable, and able to be carried out periodically and automatically. This paper introduces the concept of noninteractivity to reduce the transaction fees caused by the frequent operation of blockchain in the verification process. By stipulating payment rules in the smart contract, we can ensure the fairness of all parties. Finally, we give the correctness proof of the scheme and the security proof in the random oracle model, comparing it with other schemes and verifying the practicability of our scheme through experiments.

V3Geo: a cloud-based repository for virtual 3D models in geoscience

Abstract. V3Geo is a cloud-based repository for publishing virtual 3D models in geoscience. The system allows storage, search and visualisation of mesh models typically acquired using techniques such as photogrammetry and laser scanning. The platform has been developed to handle models at the range of scales typically used by geoscientists from microscopic, hand samples and fossils through to outcrop sections or terrain covering metres to tens of kilometres. The cloud storage system serves the models to a purpose-built 3D web viewer. Models are tiled to ensure efficient streaming over the Internet. The web viewer allows 3D models to be interactively explored without the need for specialist software to be installed. A measurement tool enables users to gauge simple dimensions, such as widths, thicknesses, and fault throws. V3Geo allows very large models comprising multiple sections and is designed to include additional interpretation layers. The specific focus on geoscience data is supported by defined metadata and a classification schema. Public and private storage is available, and public models are assigned Creative Commons licenses to govern content usage. This paper presents V3Geo as a sustainable resource for the geoscience community, including the motivation and main characteristics and features. Example usage scenarios are highlighted: from undergraduate geology teaching, supporting virtual geoscience education and preparing virtual field trips based on V3Geo models. Finally, best practice guidelines for preparing 3D model contributions for publication on V3Geo are included as the Appendix.