The Experts below are selected from a list of 47049 Experts worldwide ranked by ideXlab platform
Aoues Guesmi - One of the best experts on this subject based on the ideXlab platform.
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end to end privacy policy enforcement in Cloud Infrastructure
IEEE International Conference on Cloud Networking, 2013Co-Authors: Stephane Betgebrezetz, Guybertrand Kamga, Mariepascale Dupont, Aoues GuesmiAbstract:Privacy in the Cloud is still a strong issue for the large adoption of Cloud technologies by enterprises which fear to actually put their sensitive data in the Cloud. There is indeed a need to have an efficient access control on the data stored and processed in the Cloud Infrastructure allowing to support the various business and country-based regulation constraints (e.g., on data location and co-location, data retention duration, data processing, node security level, tracing and audit). In this perspective, this paper presents a novel approach of end-to-end privacy policy enforcement over the Cloud Infrastructure and based on the sticky policy paradigm (a policy being bound to each sensitive data). In our approach the data protection is performed within the Cloud nodes (e.g., within the internal file system of a VM or its attached volume) and is completely transparent for the applications (no need to modify the applications). This paper describes the concept and the proposed end-to-end architecture (from the client to the Cloud nodes) as well as an implementation based on the FUSE (Filesystem in Userspace) technology. This implementation is executed on a scenario of data access and transfer control, and is also used to achieve performance evaluations. These evaluations show that, with a reasonable additional computation cost, this approach offers a flexible and transparent way to enforce various privacy constraints within the Cloud Infrastructure.
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CloudNET - End-to-end privacy policy enforcement in Cloud Infrastructure
2013 IEEE 2nd International Conference on Cloud Networking (CloudNet), 2013Co-Authors: Stéphane Betge-brezetz, Guybertrand Kamga, Mariepascale Dupont, Aoues GuesmiAbstract:Privacy in the Cloud is still a strong issue for the large adoption of Cloud technologies by enterprises which fear to actually put their sensitive data in the Cloud. There is indeed a need to have an efficient access control on the data stored and processed in the Cloud Infrastructure allowing to support the various business and country-based regulation constraints (e.g., on data location and co-location, data retention duration, data processing, node security level, tracing and audit). In this perspective, this paper presents a novel approach of end-to-end privacy policy enforcement over the Cloud Infrastructure and based on the sticky policy paradigm (a policy being bound to each sensitive data). In our approach the data protection is performed within the Cloud nodes (e.g., within the internal file system of a VM or its attached volume) and is completely transparent for the applications (no need to modify the applications). This paper describes the concept and the proposed end-to-end architecture (from the client to the Cloud nodes) as well as an implementation based on the FUSE (Filesystem in Userspace) technology. This implementation is executed on a scenario of data access and transfer control, and is also used to achieve performance evaluations. These evaluations show that, with a reasonable additional computation cost, this approach offers a flexible and transparent way to enforce various privacy constraints within the Cloud Infrastructure.
Victor Chang - One of the best experts on this subject based on the ideXlab platform.
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Multi-Objective Data Placement for Workflow Management in Cloud Infrastructure Using NSGA-II
IEEE Transactions on Emerging Topics in Computational Intelligence, 2020Co-Authors: Fei Dai, Honghao Gao, Victor ChangAbstract:The Cloud computing paradigm provides massive storage and rich computing resources for workflow deployment and implementation. Nevertheless, workflow applications (e.g., meteorological prediction and financial analysis) are usually data intensive, and substantial data resources with privacy information tend to be accessed during the workflow implementation. Therefore, it remains challenging to design a data placement method for seeking tradeoffs among multiple performance metrics, i.e., resource usage, data acquisition time, and energy cost, while avoiding privacy conflicts of information-overlapping datasets for workflow implementation of the Cloud Infrastructure. To address this challenge, a multi-objective data placement method for workflow management in the Cloud Infrastructure with privacy protection is proposed in this paper. Technically, the BCube topology is adopted to establish the resource model in the Cloud Infrastructure, and the potential privacy conflicts of datasets required for workflow implementation are analyzed. Then, a non-dominated sorting genetic algorithm II is leveraged to promote the resource usage, reduce the data acquisition time, and optimize the energy cost of the Cloud Infrastructure, while achieving the privacy protection for data placement. Finally, experimental evaluations demonstrate that the performance of the Cloud Infrastructure is optimized for workflow management.
Ross Jeffery - One of the best experts on this subject based on the ideXlab platform.
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On understanding the economics and elasticity challenges of deploying business applications on public Cloud Infrastructure
Journal of Internet Services and Applications, 2012Co-Authors: Basem Suleiman, Sherif Sakr, Ross JefferyAbstract:The exposure of business applications to the web has considerably increased the variability of its workload patterns and volumes as the number of users/customers often grows and shrinks at various rates and times. Such application characteristics have increasingly demanded the need for flexible yet inexpensive computing Infrastructure to accommodate variable workloads. The on-demand and per-use Cloud computing model, specifically that of public Cloud Infrastructure Service Offerings (CISOs), has quickly evolved and adopted by majority of hardware and software computing companies with the promise of provisioning utility-like computing resources at massive economies of scale. However, deploying business applications on public Cloud Infrastructure does not lead to achieving desired economics and elasticity gains, and some challenges block the way for realizing its real benefits. These challenges are due to multiple differences between CISOs and application’s requirements and characteristics. This article introduces a detailed analysis and discussion of the economics and elasticity challenges of business applications to be deployed and operate on public Cloud Infrastructure. This includes analysis of various aspects of public CISOs, modeling and measuring CISOs’ economics and elasticity, application workload patterns and its impact on achieving elasticity and economics, economics-driven elasticity decisions and policies, and SLA-driven monitoring and elasticity of Cloud-based business applications. The analysis and discussion are supported with motivating scenarios for Cloud-based business applications. The paper provides a multi-lenses overview that can help Cloud consumers and potential business application’s owners to understand, analyze, and evaluate important economics and elasticity capabilities of different CISOs and its suitability for meeting their business application’s requirements.
Liqiang Zhang - One of the best experts on this subject based on the ideXlab platform.
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ACM Conference on Computer and Communications Security - POSTER: An E2E Trusted Cloud Infrastructure
Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, 2014Co-Authors: Juan Wang, Bo Zhao, Huanguo Zhang, Fei Yan, Liqiang ZhangAbstract:In this paper, a framework of end to end (E2E) trusted Cloud Infrastructure is proposed. On one end of the Cloud provider, the trusted chain is extended to VMM and VM by trusted measurement and remote attestation, which can assure the trust of VMM and VM. On another end of the Cloud terminal, the trusted mechanism is used to protect the terminal security. For the trust of Cloud network, trusted network connect (TNC) is leveraged to protect the security of communication between the loud provider and the Cloud terminal. The E2E trusted Cloud Infrastructure provides an E2E trusted protection for Cloud computing. In addition, it can support the Chinese cryptographic algorithm (SMx) based on TPM 2.0.
Paulo Maciel - One of the best experts on this subject based on the ideXlab platform.
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A Modeling Approach for Cloud Infrastructure Planning Considering Dependability and Cost Requirements
IEEE Transactions on Systems Man and Cybernetics: Systems, 2015Co-Authors: Erica Sousa, Fernando Lins, Eduardo Tavares, Paulo Cunha, Paulo MacielAbstract:Cloud computing is a model in which resources such as storage, applications, and networking Infrastructures can be offered as services over the internet. Cloud applications are becoming even bigger and more complex with a high availability requirement. This paper presents a modeling strategy based on a hierarchical and heterogeneous modeling for Cloud Infrastructure planning. This modeling strategy allows the selection of Cloud Infrastructures according to dependability and cost requirements. Additionally, a stochastic model generator for Cloud Infrastructure planning provides automatic generation of dependability and cost models for representing Cloud Infrastructures. A case study based on Moodle hosted on a Eucalyptus platform is adopted to demonstrate the feasibility of the proposed solution (modeling strategy and tooling).
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IEEE Cloud - Performance and Cost Modeling Strategy for Cloud Infrastructure Planning
2014 IEEE 7th International Conference on Cloud Computing, 2014Co-Authors: Erica Sousa, Eduardo Tavares, Fernando Antonio Aires Lins, Paulo MacielAbstract:Cloud computing is a computational paradigm in which resources such as storage, applications and networking Infrastructures can be offered as services over the internet. Due to dynamic and virtualized nature of Cloud environments and diversity of client requests, providing the expected service quality while avoiding over-provisioning is not a simple task. To ensure that the provisioned service is acceptable, providers must exploit techniques and mechanisms that guarantee a minimum level of service quality. This performance evaluation of Cloud Infrastructures has been receiving considerable attention by providers as a prominent activity for improving service quality. This paper presents a modeling strategy for Cloud Infrastructure planning with different software and hardware configurations, according to performance and cost requirements. A case study based on Moodle hosted on Eucalyptus platform is adopted to demonstrate the feasibility of the proposed modeling strategy.
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SMC - Stochastic Model Generation for Cloud Infrastructure Planning
2013 IEEE International Conference on Systems Man and Cybernetics, 2013Co-Authors: Erica Sousa, Paulo Maciel, Lais Medeiros, Eduardo Tavares, Fernando Antonio Aires Lins, Erico MedeirosAbstract:Over the last years, the evaluation of Cloud computing Infrastructures has received considerable attention as a prominent activity for improving service quality as well as planning modifications in an existing Infrastructure. This paper presents Stochastic Model Generator for Cloud Infrastructure Planning (SMG4CIP), a automatic generation system of stochastic models for Cloud Infrastructure planning. This system indicates feasible Cloud Infrastructures according to dependability and cost requirements. The proposed system adopts a technique based on GRASP metaheuristic in order to recommend optimized Cloud Infrastructures. Furthermore, SMG4CIP adopts stochastic models, such as Petri Net and Reliability Block Diagram, to evaluate dependability and cost metrics. A case study based on Electronic Funds Transfer (EFT) system is adopted to demonstrate the feasibility of the proposed system.
