Integrated Blockchain and Cloud Computing Systems: A Systematic Survey, Solutions, and Challenges

Cloud computing is a network model of on-demand access for sharing configurable computing resource pools. Compared with conventional service architectures, cloud computing introduces new security challenges in secure service management and control, privacy protection, data integrity protection in distributed databases, data backup, and synchronization. Blockchain can be leveraged to address these challenges, partly due to the underlying characteristics such as transparency, traceability, decentralization, security, immutability, and automation. We present a comprehensive survey of how blockchain is applied to provide security services in the cloud computing model and we analyze the research trends of blockchain-related techniques in current cloud computing models. During the reviewing, we also briefly investigate how cloud computing can affect blockchain, especially about the performance improvements that cloud computing can provide for the blockchain. Our contributions include the following: (i) summarizing the possible architectures and models of the integration of blockchain and cloud computing and the roles of cloud computing in blockchain; (ii) classifying and discussing recent, relevant works based on different blockchain-based security services in the cloud computing model; (iii) simply investigating what improvements cloud computing can provide for the blockchain; (iv) introducing the current development status of the industry/major cloud providers in the direction of combining cloud and blockchain; (v) analyzing the main barriers and challenges of integrated blockchain and cloud computing systems; and (vi) providing recommendations for future research and improvement on the integration of blockchain and cloud systems.

Collaborative Prognostics for Machine Fleets Using a Novel Federated Baseline Learner

Difficulty in obtaining enough run-to-fail datasets is a major barrier that impedes the widespread acceptance of Prognostic and Health Management (PHM) technology in many applications. Recent progress in federated learning demonstrates great potential to overcome such difficulty because it allows one to train PHM models based on distributed databases without direct data sharing. Therefore, this technology can overcome local data scarcity challenges by training the PHM model based on multi-party databases. To demonstrate the ability of federated learning to enhance the robustness and reliability of PHM models, this paper proposes a novel federated Gaussian Mixture Model (GMM) algorithm to build universal baseline models based on distributed databases. A systematic methodology to perform collaborative prognostics is further presented using the proposed federated GMM algorithm. The usefulness and performance are validated through a simulated dataset and the NASA Turbofan Engine Dataset. The proposed federated approach with parameter sharing is shown to perform at par with the traditional approach with data sharing. The proposed model further demonstrates improved robustness of predictions made collaboratively keeping the data private compared to local predictions. Federated collaborative learning can serve as a catalyst for the adaptation of business models based on the servitization of assets in the era of Industry 4.0. The methodology facilitates effective learning of asset health conditions for data-scarce organizations by collaborating with other organizations preserving data privacy. This is most suitable for a servitization model for Overall Equipment Manufacturers who sell to multiple organizations.

Blockchain-based architecture design for personal health record (Preprint)

BACKGROUND The importance of blockchain-based architectures for personal health record (PHR) lies in the fact that they are thought and developed to allow patients to control and at least partly collect their health data. Ideally, these systems should provide the full control of such data for the respective owner. In spite of this importance, most of the works focus more on describing how blockchain models can be used in a PHR scenario than whether these models are in fact feasible and robust enough to support a large number of users. OBJECTIVE Toward a consistent, reproducible and comparable PHR system, we build a novel ledger-oriented architecture out of a permissioned distributed network, providing patients with a manner to securely collect, store, share and manage their health data. We also emphasize the importance of suitable ledgers and smart contracts to operate the blockchain network as well as discuss the necessity of standardizing evaluation metrics to compare related works. METHODS We adopted the Hyperledger Fabric platform to implement our blockchain-based architecture design and the Hyperledger Caliper framework to provide a detailed assessment of our system under workload, ranging from 100 to 2,500 simultaneous record submissions, and using throughput and average latency as primary metrics. We also create a health database, a cryptographic unit and a server to complement the blockchain network. RESULTS Smart contracts that write on the ledger have throughputs, measured in transactions per seconds (tps), in an order of magnitude close to 10^2 tps while those contracts that only read have rates close to 10^3 tps. Smart contracts that write also have latencies, measured in seconds (s), in an order of magnitude close to 10^1 s while that only read have delays close to 10^0 s. In particular, smart contracts that retrieve, list and view history have throughputs varying, respectively, from 1,100 to 1,300 tps, 650 to 750 tps and 850 to 950 tps, impacting the overall system response if they are equally requested under the same workload. CONCLUSIONS To the best of our knowledge, we are the first to evaluate, using Hyperledger Caliper, the performance of a PHR blockchain architecture and also the first to evaluate each smart contract separately. Nevertheless, blockchain systems achieve performances far below the traditional distributed databases achieve, indicating the assessment of blockchain solutions for PHR is a major concern to be addressed before putting them into a real production.

Design and Implementation of EFSC Algorithm for Hybrid Representation of Distributed Databases

In distributed database management systems, fragmenting base connections increases concurrency and hence system throughput for query processing. User queries use hybrid fragmentation methods focused on vector bindings, and deductive database implementations lack query-access-rule dependence. As a result, for hierarchical deductive information implementations, a hybrid fragmentation solution is used. The method considers the horizontal partition of base relations based on the bindings placed on user requests, then produces vertical fragments of the horizontally partitioned relations, and finally clusters rules based on attribute affinity and query and rule access frequency. The suggested fragmentation approach makes distributed deductive database structures easier to develop.

Production safety training using networked digital intelligent systems

The preparation and control of the knowledge of those working on industrial safety using networked digital intelligent learning systems containing formalized knowledge in text and graphical representation and simulation models in the form of distributed databases and knowledge is considered as the most important element of the organization of safe production based on digital technologies. When integrating training systems into a specific digital production, educational content, if necessary, is supplemented and developed by those responsible for training and the students themselves as part of the educational resource of digital organizations for training and retraining personnel.

Mathematical Analysis of Parametric Characteristics of the Consensus Algorithms Operation with the Choice of the Most Priority One for Implementation in the Financial Sphere

Blockchain is one of the leading data transfer technologies that eliminate the need for centralized management through consensus algorithms. This article describes the consensus algorithms, their benefits, and their applications within a micropayment system in the financial sector. Preliminary studies have shown that the performance of distributed databases largely depends on the chosen consensus algorithm. The main task of the study is to create a mathematical model to assess their performance. The most popular crypto projects and the consensus algorithms are analyzed to determine their performance. The obtained model was tested by calculating the parameters of the distributed register based on the directed acyclic graph algorithm and calculating the parameters of other algorithms used. The result is a mathematical model for evaluating the parametric characteristics of the work of consensus algorithms with the choice of the most priority one for implementation in the financial sector. The analysis focuses on the mathematical steps taken by each consensus algorithm. The data obtained using the developed mathematical model demonstrates that PoW, PoS, and DAG algorithms depend on various resources, such as computing power, the number of connected nodes, and the speed of receiving transactions.

Secure Data Mobility in Cloud Computing for e-Governance Application

Due to the rapid technological advancement, traditional e-government systems are getting obsolete because of their inherent limitation of interoperability and accessibility to the highly secured and flexible e-governance services. Migration of such systems into highly secured cloud governance architecture will be a long-term viable solution. However, the adoption of distributed cloud computing has created operational and security challenges. This research work aims to bridge the gap between traditional and cloud-based e-Government systems in terms of data security based on confidentiality, interoperability, and mobility of data among distributed databases of cloud computing environments. In this work, we have created two organization databases by the use of AWS EC2 instances and classified the data based on the Risk Impact Level (RIL) of data by the use of the Metadata Attribute Value (MAV) function. To enhance further security on classified data, we take appropriate security action based on the sensitivity of the data. For the analysis purpose, we implemented different security algorithms, i.e. AES, DES, and RSA in the mobility of data between two distributed cloud databases. We measured the encryption and decryption time along with the file size of data before and after classification. AES performed better while considering the encryption time and file size, but the overall performance of RSA was better for smaller file sizes. Finally, the performance of the data mobility between two distributed clouds databases was analyzed while maintaining the sensitivity level of the data.