Multiple Cloud Service Research Articles

A novel secure privacy-preserving data sharing model with deep-based key generation on the blockchain network in the cloud

Cloud computing is currently emerging as a developing technology in which a Cloud Service Provider (CSP) is a third-party organization that provides effective storage of data and facilities to a large client base. Saving information in a cloud offers users the satisfaction of accessing it without the need for direct knowledge of the distribution and management of an infrastructure. The primary objective is to develop a novel, secure, and privacy-preserving data-sharing model that utilizes deep-based key generation on blockchain in the cloud. Data communication is done using multiple entities. The research aims to develop a collaborative data-sharing method in the cloud for the authentication scheme for cloud security on blockchain and smart contracts. Initialization, registration, key generation, authentication of data sharing, and validation are carried out here. The proposed data-sharing model involves a revenue distribution model that depends on Multiple Services (MS) models to improve multiple cloud services. The security parameters namely passwords, hashing functions, key interpolation, and encryption are used for preserving the Data privacy and here the SpinalNet is used for generating keys. Furthermore, the devised SpinalNet_Genkey obtained a value of 45.001 MB, 0.002, and 0.003 sec for memory usage, revenue, and computation cost.

Workflow scheduling based on asynchronous advantage actor–critic algorithm in multi-cloud environment

Recently, the multi-cloud environment (MCE) has increasingly become the preferred choice of users. As with the cloud environment, efficient workflow scheduling in a MCE remains crucial for identifying the cost efficiency and overall performance of the MCE. In MCE, the resources exhibit heterogeneity, complexity, and dynamism. Simultaneously, the intricate inter-task dependencies among workflow tasks, diverse Quality of Service (QoS) metrics for users, and multiple cloud service providers’ (CSPs) billing mechanisms significantly amplify the workflow scheduling challenge. Motivated by the application of reinforcement learning (RL) in workflow scheduling in a cloud environment, this paper proposes a scheduling algorithm that takes advantage of the asynchronous advantage actor–critic algorithm (A3C) to balance cost, makespan and resource utilization in workflow scheduling in a MCE. By analyzing the elements in the MCE, we design and define multiple agents in the MCE, and each cloud service provider will have an agent to record the state and update the local parameters. For the workflow task submitted by the user, the action is selected according to the initialization policy and submitted to the scheduling action to allocate the task to a designated virtual machine in the MCE so that each agent can more clearly perceive the environment change and adapt to the MCE. In contrast to the traditional A3C algorithm, we design a new critic network according to the data characteristics of real-world scientific workflows so that each agent is more suitable for real-world scientific workflow data. Through multiple sets of simulation experiments, the workflow scheduling algorithm based on the A3C algorithm in the MCE (MCWS-A3C) was compared with three benchmark methods. The experimental results show that the proposed method has better advantages than other methods in terms of cost, makespan, and resource utilization. Specifically, on the Montage_100 dataset, the average cost was reduced by 55.12% compared to other methods. The pioneering introduction of the A3C algorithm that adapts to the dynamic environment into the MCE brings more possibilities to address the issue of workflow scheduling in the MCE.

JCDC: A blockchain-based framework for secure data storage and circulation in JointCloud

JointCloud computing represents a new generation cloud computing paradigm, which deeply integrates the cloud resources of multiple Cloud Service Providers (CSPs) to offer tailored cloud services to users. In contrast to traditional multi-cloud environment, JointCloud environment involve data circulation among multiple CSPs. However, in JointCloud environment, CSPs are not always fully trustworthy and they may illegally infringe upon users’ data privacy and security for their own benefit. Additionally, the heterogeneity arising from different data storage formats, structures, access control, and permission management mechanisms adopted by various CSPs makes achieving unified data management in JointCloud challenging. Therefore, to ensure secure storage and efficient circulation of data within JointCloud, it is essential to prevent violations for user privacy and data ownership, shield the heterogeneity of underlying data management mechanisms across different CSPs, and establish trusted transactions between CSPs. In this paper, we propose a framework called JointCloud Data Chain (JCDC) based on JointCloud computing and blockchain for data storage and circulation, aiming to ensure secure data storage and trustworthy transactions. JCDC utilizes blockchain to record data ownership and control data circulation, while integrating storage resources from various CSPs to construct a distributed off-chain Personal Data Storage (PDS) for expanding system storage capacity. Additionally, JCDC employs Certificateless Public Key Cryptography (CL-PKC) and Proxy Re-encryption technologies for user identity management and secure data transactions. Furthermore, smart contracts are designed to enable automated data storage and sharing. We conduct a security analysis of JCDC and develop a prototype system to validate its performance and practicality. Finally, extensive experimentation and analysis demonstrate that JCDC exhibits low time latency and cost, which makes it practical.

Differentially private and truthful auction-based resource procurement for budget-constrained DAG applications in clouds

With the rapid proliferation of emerging scientific computing, more and more cloud users (CUs) with large-scale applications are seeking efficient executing paradigms in clouds. Following this trend, a cloud market with multiple cloud service providers (CSPs) is emerging, which aims to provide efficient cross-cloud services to CUs through the cooperation among CSPs. In the market, CUs can procure different virtual machine resources and their combinations from one or more CSP(s) in a pay-as-you-go way. In such a procurement process, it is challenging to achieve privacy protection and truthfulness of CSPs while enabling flexible resource combinations. As such, we design a differentially private and truthful auction-based resource procurement mechanism (DTARP) in clouds. Specifically, we first propose a budget-aware resource profile determination algorithm (BRPD) to obtain a set of resource profiles, in order to bridge the difference between application tasks and resources under the budget constraint. Next, we design a differential privacy-based winning CSP selection algorithm to choose a set of winning CSPs, and the payment calculation algorithm to calculate the payment for each winning CSP. Strict theoretical analysis proves that the proposed DTARP achieves differential privacy, truthfulness, individual rationality, computational efficiency, and approximate social cost minimization while guaranteeing the efficient completion of application tasks under the budget. Extensive simulation results also demonstrate the effectiveness of the proposed DTARP.

Cloud in Blockchain Technologies

Cloud computing and blockchain technology have become essential ideas in the field of IT, providing new ways to handle data. Cloud computing offers internet-based access to computing resources like servers and storage, while blockchain guarantees the accountability and immutability of data transactions with its distributed ledger system. Combining these technologies can enhance organizations' capabilities in data security and management. Businesses can utilize scalable infrastructure on cloud platforms by implementing blockchain technology, eliminating the need for large investments in physical resources. This integration improves the ability to access blockchain networks, making interactions easier across different locations and devices. Additionally, it enhances data security and privacy measures by enabling encrypted connections and strong authentication methods in cloud-based blockchain systems. Yet, obstacles remain in guaranteeing the authenticity and protection of information kept in multi-cloud settings, requiring effective authentication measures. To tackle these difficulties, this article presents a method for verifying data integrity in multi-cloud storage using blockchain technology, utilizing the decentralized aspect of blockchain to improve transparency and security. The plan automates verification procedures through smart contracts and cryptographic hashing, allowing for integrity checks at both a general and specific level across multiple Cloud Service Providers (CSPs). The proposed scheme has its effectiveness and reliability validated through theoretical analysis and experimental results, highlighting its potential to improve the security and efficiency of multi-cloud storage systems. INDEX TERMS: Cloud Computing, Blockchain Technology, Data Integrity Verification, Multi-Cloud Storage, Smart Contracts, Cryptographic Hashing, Security, Transparency.

Cloud storage cost: a taxonomy and survey

Cloud service providers offer application providers with virtually infinite storage and computing resources, while providing cost-efficiency and various other quality of service (QoS) properties through a storage-as-a-service (StaaS) approach. Organizations also use multi-cloud or hybrid solutions by combining multiple public and/or private cloud service providers to avoid vendor lock-in, achieve high availability and performance, and optimise cost. Indeed cost is one of the important factors for organizations while adopting cloud storage; however, cloud storage providers offer complex pricing policies, including the actual storage cost and the cost related to additional services (e.g., network usage cost). In this article, we provide a detailed taxonomy of cloud storage cost and a taxonomy of other QoS elements, such as network performance, availability, and reliability. We also discuss various cost trade-offs, including storage and computation, storage and cache, and storage and network. Finally, we provide a cost comparison across different storage providers under different contexts and a set of user scenarios to demonstrate the complexity of cost structure and discuss existing literature for cloud storage selection and cost optimization. We aim that the work presented in this article will provide decision-makers and researchers focusing on cloud storage selection for data placement, cost modelling, and cost optimization with a better understanding and insights regarding the elements contributing to the storage cost and this complex problem domain.

An Approach Towards Standardization of Ehr with Efficient Accessibility of Data on Demand: A Review

Standardization of medical terminologies and the centralized electronic medical record (EMR) are the most addressed discussion in the healthcare industries. Patient master records and the treatment statistics records has wide spectrum and dynamic data structure. These records have high growth rate with huge volume over the period and which requires proper structured data base management system. Data science and the analytics could solve the problem of data management, but trust about data storage over the cloud may become major issue. These records may be archived for better storage management and could be replicated over multiple cloud or kept in a distributed manner for extended security. On other hand medical data generated through wearable medical devices or the portable devices used by home care patients may be connected to multiple cloud servers for better accessibility and security. Proposed system is to develop a generic medical record management system over multiple cloud and designing the Body Area Network (BAN) architecture connected to distributed cloud for data accessibility and security.

Leakage-Suppressed Encrypted Keyword Queries Over Multiple Cloud Servers

Leakage-Suppressed Encrypted Keyword Queries Over Multiple Cloud Servers

Privacy-Preserving Image Retrieval Based on Additive Secret Sharing

The rapid growth of digital images motivates individuals and organizations to upload their images to the cloud server. To preserve privacy, image owners would prefer to encrypt the images before uploading, but it would strongly limit the efficient usage of images. Plenty of existing schemes on privacy-preserving Content-Based Image Retrieval (PPCBIR) try to seek the balance between security and retrieval ability. However, compared to the advanced technologies in CBIR like Convolutional Neural Network (CNN), the existing PPCBIR schemes are far deficient in both accuracy and efficiency. With more cloud service providers, the collaborative secure image retrieval service provided by multiple cloud servers becomes possible. In this paper, inspired by additive secret sharing technology, we propose a series of additive secure computing protocols on numbers and matrices with better efficiency, and then show their application in PPCBIR. Specifically, we extract CNN features, decrease the dimension of features and build the index securely with the help of our protocols, which include the full process of image retrieval in the plaintext domain. The experiments and security analysis demonstrate the efficiency, accuracy, and security of our scheme.

Cost-Effective Resource Allocation and Optimization Strategies for Multi-Cloud Environments

Abstract: As organizations increasingly adopt multi-cloud strategies to leverage the benefits of multiple cloud service providers, the need for effective cost optimization becomes paramount. This research paper aims to explore and propose innovative approaches for optimizing costs in multi-cloud environments. The study will investigate resource allocation, workload placement, and performance optimization techniques, taking into account factors such as workload variability, service-level agreements (SLAs), and pricing models across different cloud providers. The objective is to develop cost-effective strategies that balance performance and cost across a heterogeneous cloud ecosystem, ultimately helping organizations make informed decisions in their multi-cloud deployments. This research topic addresses the growing interest in multi-cloud architectures and the associated challenges in managing costs while ensuring high-quality service delivery. It provides a valuable contribution to the field of cloud computing and cost optimization, which is increasingly crucial for businesses and enterprises seeking to maximize the benefits of cloud technology.

Revolutionizing Urban Mobility: IoT-Enhanced Autonomous Parking Solutions with Transfer Learning for Smart Cities.

Smart cities have emerged as a specialized domain encompassing various technologies, transitioning from civil engineering to technology-driven solutions. The accelerated development of technologies, such as the Internet of Things (IoT), software-defined networks (SDN), 5G, artificial intelligence, cognitive science, and analytics, has played a crucial role in providing solutions for smart cities. Smart cities heavily rely on devices, ad hoc networks, and cloud computing to integrate and streamline various activities towards common goals. However, the complexity arising from multiple cloud service providers offering myriad services necessitates a stable and coherent platform for sustainable operations. The Smart City Operational Platform Ecology (SCOPE) model has been developed to address the growing demands, and incorporates machine learning, cognitive correlates, ecosystem management, and security. SCOPE provides an ecosystem that establishes a balance for achieving sustainability and progress. In the context of smart cities, Internet of Things (IoT) devices play a significant role in enabling automation and data capture. This research paper focuses on a specific module of SCOPE, which deals with data processing and learning mechanisms for object identification in smart cities. Specifically, it presents a car parking system that utilizes smart identification techniques to identify vacant slots. The learning controller in SCOPE employs a two-tier approach, and utilizes two different models, namely Alex Net and YOLO, to ensure procedural stability and improvement.

A Privacy-Preserving Service Framework for Traveling Salesman Problem-Based Neural Combinatorial Optimization Network

The traveling salesman problem (TSP) is one of the classic combinatorial optimization problems, which can be widely used in intelligent transportation and logistics field. Neural network has shown great potential in combinatorial optimization tasks. However, it faces privacy leakage when a TSP neural combinatorial optimization network and user's data are directly outsourced to a cloud platform to provide and request service. In order to address the issue, this paper proposes a privacy-preserving service framework for a TSP-based neural combinatorial optimization network called PPSF. We first protect the service provider's model parameters and users' data by different split methods in multiple cloud servers, providing a secure outsourced mode for the participators in the PPSF framework. Then, in the secure outsourced mode, a series of secure computation protocols are designed for the cloud servers to support performing the secure computing in each service task. Moreover, it can also protect the process that the cloud servers respond to users after data processing and achieve private service result recovery. Finally, we prove that the proposed framework can realize privacy protection for TSP-based combinatorial optimization service and verify its utility and efficiency by experiments.

A Quantitative Assessment of the Impact of Automated Incident Response on Cloud Services Availability

Abstract In the rapidly evolving landscape of cloud computing, ensuring high service availability is paramount for organizations reliant on digital infrastructure. This study conducts a quantitative assessment of the impact of Automated Incident Response Systems (AIRS) on the availability of cloud services. Utilizing a dataset derived from multiple cloud service providers over a twelve-month period, we analyze key performance metrics, including incident response times, resolution rates, and service uptime statistics, both pre- and post-AIRS implementation. The results indicate a statistically significant improvement in service availability following the deployment of AIRS, characterized by reduced incident resolution times and enhanced operational uptime. Furthermore, we present a comparative analysis of traditional incident response methodologies versus automated systems, demonstrating the superior efficiency and reliability of AIRS. The findings underscore the necessity for organizations to embrace automation in incident management to bolster service resilience and optimize customer satisfaction. This study concludes with practical recommendations for organizations considering the integration of AIRS into their operational frameworks. In the rapidly evolving landscape of cloud computing, ensuring high service availability has become a critical concern for organizations that depend heavily on digital infrastructures. This study conducts a comprehensive quantitative assessment of the impact of Automated Incident Response Systems (AIRS) on the availability of cloud services. As cloud environments become increasingly complex, the potential for service disruptions due to incidents—such as hardware failures, security breaches, or configuration errors—poses significant risks to businesses. Through a detailed analysis of incident response metrics obtained from a diverse dataset comprising five leading cloud service providers, this research evaluates key performance indicators including incident response times, resolution rates, and service uptime statistics over a twelve-month period. Employing rigorous statistical methods, the study investigates the relationship between the implementation of AIRS and enhancements in service availability, offering insights into the effectiveness of automation in incident management. The results reveal a statistically significant improvement in service availability post-AIRS deployment, characterized by a dramatic reduction in average incident response times—from 30 minutes to just 10 minutes—and an increase in resolution rates from 75% to 95%. Furthermore, the average service uptime improved from 90% to an impressive 99.5%, demonstrating the potential for organizations to achieve greater operational resilience through the integration of automated systems. The findings also include a comparative analysis of traditional versus automated incident response methodologies, highlighting the superior efficiency and reliability of AIRS. This study emphasizes the necessity for organizations to adopt automated solutions in their incident management frameworks to mitigate the risks associated with downtime and enhance customer satisfaction. The conclusions drawn from this research provide practical recommendations for businesses considering AIRS integration, making a compelling case for the strategic implementation of automated incident response mechanisms in the pursuit of optimal service availability.

A truthful dynamic combinatorial double auction model for cloud resource allocation

Dynamic auction-based resource allocation models require little global price information, are decentralized and suitable for the distributed systems like cloud computing. For the cloud computing market, we proposed a Truthful Dynamic Combinatorial Double Auction (TDCDA) model to improve the social welfare and resource utilization. In our model, multiple cloud service providers and cloud users bid for various resources in a dynamic environment. We adopted a payment scheme to ensure truthfulness for all participants, which motivates bidders to reveal their true preferences. Since the combinatorial auction allocation with goal of economic efficiency is NP-hard, we developed a greedy mechanism to achieve the approximately efficient solution. Considering both parties’ interests and the resource scarcity, this model also ensures fairness and balances resource allocation. The proposed model is proven to be approximately efficient, incentive compatible, individually rational and budget-balanced. Simulation results show that the model not only achieves economic efficiency, but also improves resource allocation and meets resource needs for more cloud users.

Skyline-Enhanced Deep Reinforcement Learning Approach for Energy-Efficient and QoS-Guaranteed Multi-Cloud Service Composition

Cloud computing has experienced rapid growth in recent years and has become a critical computing paradigm. Combining multiple cloud services to satisfy complex user requirements has become a research hotspot in cloud computing. Service composition in multi-cloud environments is characterized by high energy consumption, which brings attention to the importance of energy consumption in cross-cloud service composition. Nonetheless, prior research has mainly focused on finding a service composition that maximizes the quality of service (QoS) and overlooks the energy consumption generated during service invocation. Additionally, the dynamic nature of multi-cloud environments challenges the adaptability and scalability of cloud service composition methods. Therefore, we propose the skyline-enhanced deep reinforcement learning approach (SkyDRL) to address these challenges. Our approach defines an energy consumption model for cloud service composition in multi-cloud environments. The branch and bound skyline algorithm is leveraged to reduce the search space and training time. Additionally, we enhance the basic deep Q-network (DQN) algorithm by incorporating double DQN to address the overestimation problem, incorporating Dueling Network and Prioritized Experience Replay to speed up training and improve stability. We evaluate our proposed method using comparative experiments with existing methods. Our results demonstrate that our approach effectively reduces energy consumption in cloud service composition while maintaining good adaptability and scalability in service composition problems. According to the experimental results, our approach outperforms the existing approaches by demonstrating energy savings ranging from 8% to 35%.

The construction of blockchain platform for engineering museums based on vectorized image processing technology

Abstract The characteristics of blockchain such as tamper-evident and distributed can effectively solve the increasingly prominent security and privacy issues in engineering pavilions, however, the current throughput of mainstream blockchain platforms is far from meeting the demand for rapid chain-up of massive data in engineering pavilions, and the high redundancy storage mechanism adopted by traditional blockchain cannot be applied to engineering pavilion scenarios. To address the above problems, based on vectorized image processing technology, a hierarchical blockchain architecture is first proposed to store block data in multiple distributed cloud servers and edge servers in a hierarchical manner to cope with the growing data volume in the industrial Internet. Then based on the topology between edge servers, a blockchain network slicing algorithm is designed based on an improved complex network association partitioning algorithm, which improves the blockchain network throughput while shortening the slicing time. Finally, the impact of block broadcast time on throughput is demonstrated by formalizing the block broadcast process as a spanning tree, based on which a spanning tree-based intra-slice master node selection algorithm is proposed to further improve the throughput of the blockchain network. The analysis and experimental results show that the proposed scheme effectively reduces the partitioning time of the blockchain network and the broadcast time of the blocks within each partition. Compared with the classical association partitioning algorithm, this paper's partitioning algorithm can reduce the partitioning time by about 36% without sacrificing the quality of partitioning when partitioning a large-scale network.

GDPR compliance verification through a user-centric blockchain approach in multi-cloud environment

With cloud-hosted web applications becoming ubiquitous, the security risks presented for user personal data that is migrated to the cloud are at an all-time high. When using a cloud-hosted web application, users only ever interact with web interfaces of the web applications and are usually completely unaware of how their data is distributed amongst the multiple cloud service providers that the web application uses, making it difficult to verify the lawful use and ownership of personal data. The General Data Protection Regulation (GDPR) seeks to empower users to gain better control over their personal data. Blockchain-based approaches have risen in popularity over the recent years to tackle the challenge of verifying GDPR compliance in multi-cloud environments. By deploying smart contracts on the blockchain, we can create transparent and immutable logs of data processes in the hopes of automating GDPR compliance verification. However, the existing works are still limited to provide a user-centric compliance verification. To this end, we propose a user-centric, blockchain-based framework for data management in a cloud environment where all GDPR-relevant data operations take place on the blockchain through well-defined smart contracts.

Smart City IoT Data Management with Proactive Middleware

With the increased emergence of cloud-based services, users are frequently perplexed as to which cloud service to use and whether it will be beneficial to them. The user must compare various services, which can be a time-consuming task if the user is unsure of what they might need for their application. This paper proposes a middleware solution for storing Internet of Things (IoT) data produced by various sensors, such as traffic, air quality, temperature, and so on, on multiple cloud service providers depending on the type of data. Standard cloud computing technologies become insufficient to handle the data as the volume of data generated by smart city devices grows. The middleware was created after a comparative study of various existing middleware. The middleware uses the concept of the federal cloud for the purpose of storing data. The middleware solution described in this paper makes it easier to distribute and classify IoT data to various cloud environments based on its type. The middleware was evaluated using a series of tests, which revealed its ability to properly manage smart city data across multiple cloud environments. Overall, this research contributes to the development of middleware solutions that can improve the management of IoT data in settings such as smart cities.

MORPHEMIC - Optimization of the Deployment and Life-cycle Management of Data-intensive Applications in the Cloud Computing Continuum

In their Cloud strategy companies are choosing more and more multi-cloud computing giving them the opportunity to distribute its assets, redundancies, software, applications, and anything it deems worthy not only on one Cloud-hosting environment, but rather across several. The model of using multiple Cloud services to host the business's functions and features has a list of advantages that can provide security, flexibility, cost-effectiveness and more to increase business's efficiency and ensure it stays up and running 24 hours a day. The paper presents the MORPHEMIC H2020 project and its unique way of adapting and optimising Cloud computing applications. The Morphemic project covers several features from modelling cross-cloud applications, continuous and autonomous optimization and deployment and providing access to several cloud capabilities for data intensive applications. The outcome of the project will be implemented in the form of an open-source platform covering all the data intensive applications deployment phases starting from modelling, through profiling, optimization, runtime reconfiguration and monitoring.

Making your Own NAS

This project aims to deploy the personal NAS system for remote and centralized storage solutions for all types of handsets and systems. Modern network-attached storage architectures suffer from poor performance, resource management, and inflexibility. We examine the problems of current architectures and propose another format for doing network storage at the user level. Nowadays, we have multiple cloud service providers, so there will always be an issue with data protection and backups. So, to solve all these issues, we came up with the new pre-existing idea of NAS, to leverage the data storage over the network using a GUI or dedicated devices. NAS will act as a public or private cloud storage service, which can be accessed from anywhere on the globe through internet facilities.