•A hybrid cloud manufacturing system supportive of various cloud deployment modes.•A unified ontology for cloud manufacturing based on ISO standards and existing ontologies.•A Semantic Web-based ...approach to management of dynamic resource-sharing policies.
Cloud manufacturing is emerging as a novel business paradigm for the manufacturing industry, in which dynamically scalable and virtualised resources are provided as consumable services over the Internet. A handful of cloud manufacturing systems are proposed for different business scenarios, most of which fall into one of three deployment modes, i.e. private cloud, community cloud, and public cloud. One of the challenges in the existing solutions is that few of them are capable of adapting to changes in the business environment. In fact, different companies may have different cloud requirements in different business situations; even a company at different business stages may need different cloud modes. Nevertheless, there is limited support on migrating to different cloud modes in existing solutions. This paper proposes a Hybrid Manufacturing Cloud that allows companies to deploy different cloud modes for their periodic business goals. Three typical cloud modes, i.e. private cloud, community cloud and public cloud are supported in the system. Furthermore, it enables companies to set self-defined access rules for each resource so that unauthorised companies will not have access to the resource. This self-managed mechanism gives companies full control of their businesses and boosts their trust with enhanced privacy protection. A unified ontology is developed to enhance semantic interoperability throughout the whole process of service provision in the clouds. A Cloud Management Engine is developed to manage all the user-defined clouds, in which Semantic Web technologies are used as the main toolkit. The feasibility of this approach is verified through a group of companies, each of which has complex access requirements for their resources. In addition, a use case is carried out between customers and service providers. This way, optimal service is delivered through the proposed system.
Data deduplication is one of important data compression techniques for eliminating duplicate copies of repeating data, and has been widely used in cloud storage to reduce the amount of storage space ...and save bandwidth. To protect the confidentiality of sensitive data while supporting deduplication, the convergent encryption technique has been proposed to encrypt the data before outsourcing. To better protect data security, this paper makes the first attempt to formally address the problem of authorized data deduplication. Different from traditional deduplication systems, the differential privileges of users are further considered in duplicate check besides the data itself. We also present several new deduplication constructions supporting authorized duplicate check in a hybrid cloud architecture. Security analysis demonstrates that our scheme is secure in terms of the definitions specified in the proposed security model. As a proof of concept, we implement a prototype of our proposed authorized duplicate check scheme and conduct testbed experiments using our prototype. We show that our proposed authorized duplicate check scheme incurs minimal overhead compared to normal operations.
With the rapid development of cloud computing, the ensuing security issues have become increasingly prominent. In this context, attribute-based encryption (ABE) has gradually attracted widespread ...attentions due to its unique attribute matching mechanism. At the same time, mobile devices have put forward higher requirements for the design and deployment of ABE schemes. Therefore, in this paper, we propose an original hybrid cloud multi-authority ciphertext-policy attribute-based encryption (HCMACP-ABE) scheme. Specifically, we utilize the LSSS (Linear Secret-Sharing Schemes) access structure to realize secure access control, while the private cloud is responsible for maintaining the user’s authorization list and verifying the user. Finally, we prove that our proposal achieves IND-CCA secure and is efficient in a mobile hybrid cloud environment.
Hybrid cloud is a cost-effective way to address the problem of insufficient resources for satisfying its users’ requirements in a private cloud by elastically scaling up or down its service ...capability by combining the private cloud and public clouds. However, it is a challenge to schedule tasks on hybrid resources concerning their performance and security requirements. To address the challenge, this paper aims at improving the number of finished tasks with deadline and security requirements and the resource usage cost in heterogeneous hybrid clouds, based on data protection technologies providing various security levels with different overheads for data transfers and task executions in public clouds. We first formulate the problem as a bi-objective binary nonlinear programming (BOBNP) model which is a NP-hard problem. Then, to solve the problem in polynomial time, we propose a Task Scheduling method concerning Security (TSS). To improve the cost, TSS iteratively assigns the task requiring maximum cost of public resources to the local cluster, and rents the public resource with the best cost-performance ratio first for outsourced tasks. To complete as many tasks as possible, TSS assigns tasks cannot be finished by public clouds to the local cloud at first, and employs the idea of Least Slack Time First (LSTF) with Earliest Deadline First (EDF) in each computing node. Extensive experimental results show the superior performance of TSS in satisfying task requirements, and in resource efficiency when task deadlines are not too tight, compared with four hybrid cloud scheduling methods proposed recently.
•The formulation of the task scheduling problem with performance and security requirements in heterogeneous hybrid clouds.•A Task Scheduling method concerning deadline and Security (TSS) for heterogeneous hybrid clouds.•TSS has superior performance in satisfying task requirements, compared with four hybrid cloud scheduling methods proposed recently.
Increasing numbers of workflow applications are being deployed on cloud platforms. When deploying workflows in a hybrid cloud environment, additional concerns emerge: 1. privacy-sensitive data and ...tasks cannot be exposed to a public cloud platform; 2. security and bandwidth of data transmission across cloud platforms should be guaranteed as it goes through the ‘unpredictable’ Internet. This paper investigates how to effectively reduce monetary cost when deploying a workflow in a hybrid cloud under the deadline and privacy constraints. A three-level data privacy and security model integrating hybrid encryption is devised to guarantee privacy and security of workflow applications. Two scheduling heuristics are then proposed to tackle the considered constrained optimization problem: privacy and security-aware list scheduling (PSLS) and simulated annealing (PSSA). PSLS distributes the user-defined deadline to each task and allocates each task to a resource in the hybrid cloud that meets constraints and incurs a minimum cost. PSSA permutes task lists for iterative improvement based on simulated annealing and PSLS. Simulation experiments are conducted and experimental results demonstrate that PSLS generally performs better than four existing algorithms to optimize monetary costs and PSSA achieves an even higher performance at the expense of runtime.
•Cloud workflow scheduling based on a three-level data privacy and security model.•Privacy and security-aware list scheduling method PSLS for constraint optimization.•Simulated annealing-based scheduling method PSSA for iterative improvement.•Evaluation results show PSLS and PSSA are competitive compared with existing ones.
•Organizations align IT-based capabilities with cloud delivery options to meet performance objectives.•Managerial, technical and relational IT capability positively affect cloud success.•Cloud ...success positively affects firm performance.•The moderating effect of cloud strategy (public, private and hybrid) was partially significant.
Our study examines the effect of relational, managerial and technical IT-based capabilities on cloud computing success; and analyzes how this success impacts firm performance with respect to the processes and operations supported by cloud computing. Additionally, we investigated the complex relationships that exist between IT capabilities and the public, private and hybrid cloud delivery models. Data from a sample of 302 organizations were collected to empirically test our model. The results indicate that a relational IT capability is the most influential factor to facilitate cloud success compared to technical and managerial IT capabilities. Furthermore, an evaluation of the interrelationships indicates that the public and hybrid cloud delivery models may be more dependent on relational IT capabilities for cloud success while the flexibility and agility of the firm's internal IT (technical IT capability) facilitates the public cloud. We discuss how IT-based capabilities may be used to leverage cloud delivery models to positively influence the successful implementation of cloud computing, and ultimately, firm performance for the processes and operations supported by the cloud.
Purpose: The primary objective of this study was to develop and implement a Hybrid Cloud Environment for Telerehabilitation (HCET) to enhance patient care and research in the Physical Medicine and ...Rehabilitation (PM&R) domain. This environment aims to integrate advanced information and communication technologies to support both traditional in-person therapy and digital health solutions. Background: Telerehabilitation is emerging as a core component of modern healthcare, especially within the PM&R field. By applying digital health technologies, telerehabilitation provides continuous, comprehensive support for patient rehabilitation, bridging the gap between traditional therapy, and remote healthcare delivery. This study focuses on the design, and implementation of a hybrid HCET system tailored for the PM&R domain. Methods: The study involved the development of a comprehensive architectural and structural organization for the HCET, including a three-layer model (infrastructure, platform, service layers). Core components of the HCET were designed and implemented, such as the Hospital Information System (HIS) for PM&R, the MedRehabBot system, and the MedLocalGPT project. These components were integrated using advanced technologies like large language models (LLMs), word embeddings, and ontology-related approaches, along with APIs for enhanced functionality and interaction. Findings: The HCET system was successfully implemented and is operational, providing a robust platform for telerehabilitation. Key features include the MVP of the HIS for PM&R, supporting patient profile management, and rehabilitation goal tracking; the MedRehabBot and WhiteBookBot systems; and the MedLocalGPT project, which offers sophisticated querying capabilities, and access to extensive domain-specific knowledge. The system supports both Ukrainian and English languages, ensuring broad accessibility and usability. Interpretation: The practical implementation, and operation of the HCET system demonstrate its potential to transform telerehabilitation within the PM&R domain. By integrating advanced technologies, and providing comprehensive digital health solutions, the HCET enhances patient care, supports ongoing rehabilitation, and facilitates advanced research. Future work will focus on optimizing services and expanding language support to further improve the system's functionality and impact.
This paper presents a novel emotion modeling methodology for incorporating human emotion into intelligent computer systems. The proposed approach includes a method to elicit emotion information from ...users, a new representation of emotion (AV-AT model) that is modelled using a genetically optimized adaptive fuzzy logic technique, and a framework for predicting and tracking user’s affective trajectory over time. The fuzzy technique is evaluated in terms of its ability to model affective states in comparison to other existing machine learning approaches. The performance of the proposed affect modeling methodology is tested through the deployment of a personalised learning system, and series of offline and online experiments. A hybrid cloud intelligence infrastructure is used to conduct large-scale experiments to analyze user sentiments and associated emotions, using data from a million Facebook users. A performance analysis of the infrastructure on processing, analyzing, and data storage has been carried out, illustrating its viability for large-scale data processing tasks. A comparison of the proposed emotion categorizing approach with Facebook’s sentiment analysis API demonstrates that our approach can achieve comparable performance. Finally, discussions on research contributions to cloud intelligence using sentiment analysis, emotion modeling, big data, and comparisons with other approaches are presented in detail.
The purpose of this research is to provide cloud service providers (CSP) a secure platform that can be accessed for configuring secure, silent software deployment on the hybrid cloud using ...load-balanced Docker Containers (LBDC) that are configured with different PAAS software solutions. As many researchers in the past have worked on providing PASS platforms using either a public cloud or private cloud less research is carried out on the hybrid cloud systems and researchers who have worked on the hybrid cloud have followed a different approach to providing PAAS to organizations in need but lack in securing the PAAS platform. This research will provide cloud service providers a secure deployment of the PAAS platform for the developers on the go and will reduce the overhead of CSP to deploy the PAAS services from scratch. Compared to the other researcher’s contribution this research work provides a load-balanced containerized secure PASS platform that can be scaled on-demand and be secure while scaling the resources for peak workloads. The admins of the hybrid cloud can access and deploy the Docker containers based on the requirement laid down by the developers. Once the Admin configures the Docker container with the required PAAS services he can then easily invoke the LBDC in the hybrid cloud and deploy the PAAS platform for the developers to work on in seconds. LBDC’s will help in scalable deployment of Secure PAAS services for different locations and machines simultaneously thus reducing the cost and time required for hybrid virtual machines to get up and running for the development process by the developers of the company. CSP’s can achieve less turnaround time for deployment of secure PAAS environments for organizations.
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