A Cloud-Manager-Based Re-Encryption Scheme for Mobile Users in Cloud Environment: a Hybrid Approach

Cloud computing is an emerging computing paradigm that offers on-demand, flexible, and elastic computational and storage services for the end-users. The small and medium-sized business organization having limited budget can enjoy the scalable services of the cloud. However, the migration of the organizational data on the cloud raises security and privacy issues. To keep the data confidential, the data should be encrypted using such cryptography method that provides fine-grained and efficient access for uploaded data without affecting the scalability of the system. In mobile cloud computing environment, the selected scheme should be computationally secure and must have capability for offloading computational intensive security operations on the cloud in a trusted mode due to the resource constraint mobile devices. The existing manager-based re-encryption and cloud-based re-encryption schemes are computationally secured and capable to offload the computationally intensive data access operations on the trusted entity/cloud. Despite the offloading of the data access operations in manager-based re-encryption and cloud-based re-encryption schemes, the mobile user still performs computationally intensive paring-based encryption and decryption operations using limited capabilities of mobile device. In this paper, we proposed Cloud-Manager-based Re-encryption Scheme (CMReS) that combines the characteristics of manager-based re-encryption and cloud-based re-encryption for providing the better security services with minimum processing burden on the mobile device. The experimental results indicate that the proposed cloud-manager-based re-encryption scheme shows significant improvement in turnaround time, energy consumption, and resources utilization on the mobile device as compared to existing re-encryption schemes.     

Challenges in real-time virtualization and predictable cloud computing

Cloud computing and virtualization technology have revolutionized general-purpose computing applications in the past decade. The cloud paradigm offers advantages through reduction of operation costs, server consolidation, flexible system configuration and elastic resource provisioning. However, despite the success of cloud computing for general-purpose computing, existing cloud computing and virtualization technology face tremendous challenges in supporting emerging soft real-time applications such as online video streaming, cloud-based gaming, and telecommunication management. These applications demand real-time performance in open, shared and virtualized computing environments. This paper identifies the technical challenges in supporting real-time applications in the cloud, surveys recent advancement in real-time virtualization and cloud computing technology, and offers research directions to enable cloud-based real-time applications in the future.     

Mobile Fog Computing by Using SDN/NFV on 5G Edge Nodes

Fog computing provides quality of service for cloud infrastructure. As the data computation intensifies, edge computing becomes difficult. Therefore, mobile fog computing is used for reducing traffic and the time for data computation in the network. In previous studies, software-defined networking (SDN) and network functions virtualization (NFV) were used separately in edge computing. Current industrial and academic research is tackling to integrate SDN and NFV in different environments to address the challenges in performance, reliability, and scalability. SDN/NFV is still in development. The traditional Internet of things (IoT) data analysis system is only based on a linear and time-variant system that needs an IoT data system with a high-precision model. This paper proposes a combined architecture of SDN and NFV on an edge node server for IoT devices to reduce the computational complexity in cloud-based fog computing. SDN provides a generalization structure of the forwarding plane, which is separated from the control plane. Meanwhile, NFV concentrates on virtualization by combining the forwarding model with virtual network functions (VNFs) as a single or chain of VNFs, which leads to interoperability and consistency. The orchestrator layer in the proposed software-defined NFV is responsible for handling real-time tasks by using an edge node server through the SDN controller via four actions: task creation, modification, operation, and completion. Our proposed architecture is simulated on the EstiNet simulator, and total time delay, reliability, and satisfaction are used as evaluation parameters. The simulation results are compared with the results of existing architectures, such as software-defined unified virtual monitoring function and ASTP, to analyze the performance of the proposed architecture. The analysis results indicate that our proposed architecture achieves better performance in terms of total time delay (1800 s for 200 IoT devices), reliability (90%), and satisfaction (90%).     

移动云环境中基于节点自适应配置的可靠服务架构

为了解决移动云计算系统中因链接断开和网络分区对服务可靠性的影响问题,提出一种基于服务副本管理和节点自适应配置的服务软件架构。其由通信中间件、监测系统、上下文管理器以及副本管理器组成。根据监测到的服务需求和节点信息,副本管理器通过一种自适应配置算法来激活或休眠节点,使任务能够在当前最适合的节点中执行,以此提高计算服务系统的可靠性。案例结果表明,该方案能够保证动态网络环境中的服务可靠性。     

一种基于云的 Web 应用安全服务

安全云服务的核心思想是借助云计算的高可靠性、弹性扩容、按需定制的特点,将传统硬件网络安全功能虚拟化,以服务的形式对外提供安全防护能力。目前该服务技术和研究处于初期发展阶段,在技术概念、实施架构以及拓展应用方面还没有统一的界定,因此开展安全云服务技术和应用研究具有重要的理论和实践意义。本文以中国科学院信息保障示范工程“Web应用安全云服务平台”为实践背景,首先明确阐述安全云服务的概念、特征、应用领域及国内外发展现状,然后针对Web应用的安全,提出基础架构设计及关键技术,并描述该架构下具体应用的实践效果。     

基于云计算运维服务的配置管理数据库模型

配置管理数据库(CMDB)作为基础设备的数据集散地,在运维服务中处于一个至关重要的位置。但在云计算的大环境下,尚缺乏支持云计算运维服务的CMDB模型。为解决此问题,针对云计算背景下的运维场景的CMDB模型需求进行分析,提出云计算运维服务的CMDB应该提供的基础模型,包括配置项(CI)之间的相互关系以及其具备的高效能的外部查询和分析能力,最终得到一个能为云计算运维工作提供理论及数据支撑的实践型标准模型,并在某电信运营商的云项目中得到应用和验证。     

基于云控制系统的监控系统设计与实现

基于云计算的控制系统(简称云控制系统)由以云节点形式存在的控制器、传感器和执行器组成,各节点之间通过网络交换数据.针对云控制系统中云节点分布式运行和矩阵类型数据被广泛使用使系统运行状态难以观测的问题,设计并实现了一种云监控系统.该系统采用客户端/服务器架构,使用重构技术将性能稳定、功能丰富的控件以API的形式集成在系统中,满足了自定义监控界面的需求,提高了系统的可靠性.基于TCP/IP协议实现同云节点的双向通信,在获得云节点数据的同时支持数据在线调整,以达到连续监控的目的.通过设置线程池,以异步通信的方式实时获取并显示云节点的数据,达到监控与云节点数据同步.针对云计算中被广泛使用的矩阵类型数据,提供全元素的监控支持.最后,以直流电机系统为被控对象,基于阿里云平台的动态仿真和本地云平台的实际实验,验证了所提出云监控系统的可行性和实用性,促进和完善控制系统与云计算结合在实际中的应用.     

基于区块链的边缘移动群智感知声誉更新方案

移动群智感知利用移动用户的智能终端设备以低成本获取大量感知数据,而恶意用户可能上传虚假数据以获取奖励。声誉管理是一种有效的解决办法,但是基于云服务器的移动群智感知系统存在高延迟、单点故障和隐私泄露问题。针对这些问题,结合区块链和边缘计算构建基于区块链的边缘移动群智感知系统,提出一种感知数据隐私保护的声誉更新方案,采用轻量级的隐私保护方法聚合感知数据,根据数据质量和历史任务表现更新声誉。该方案可有效抵抗恶意用户、降低时延,避免单点故障和保护数据隐私。仿真实验证明了所提方案的可行性和高效性,理论分析证明了系统的安全性。     

A secure re‐encryption scheme for data services in a cloud computing environment

Cloud computing as a promising technology and paradigm can provide various data services, such as data sharing and distribution, which allows users to derive benefits without the need for deep knowledge about them. However, the popular cloud data services also bring forth many new data security and privacy challenges. Cloud service provider untrusted, outsourced data security, hence collusion attacks from cloud service providers and data users become extremely challenging issues. To resolve these issues, we design the basic parts of secure re‐encryption scheme for data services in a cloud computing environment, and further propose an efficient and secure re‐encryption algorithm based on the EIGamal algorithm, to satisfy basic security requirements. The proposed scheme not only makes full use of the powerful processing ability of cloud computing but also can effectively ensure cloud data security. Extensive analysis shows that our proposed scheme is highly efficient and provably secure under existing security model. Copyright © 2015 John Wiley & Sons, Ltd.     

A cloud-based recommendation service using principle component analysis–scale-invariant feature transform algorithm

Cloud computing delivers resources such as software, data, storage and servers over the Internet; its adaptable infrastructure facilitates on-demand access of computational resources. There are many benefits of cloud computing such as being scalable, paying only for consumption, improving accessibility, limiting investment costs and being environmentally friendly. Thus, many organizations have already started applying this technology to improve organizational efficiency. In this study, we developed a cloud-based book recommendation service that uses a principle component analysis–scale-invariant feature transform (PCA-SIFT) feature detector algorithm to recommend book(s) based on a user-uploaded image of a book or collection of books. The high dimensionality of the image is reduced with the help of a principle component analysis (PCA) pre-processing technique. When the mobile application user takes a picture of a book or a collection of books, the system recognizes the image(s) and recommends similar books. The computational task is performed via the cloud infrastructure. Experimental results show the PCA-SIFT-based cloud recommendation service is promising; additionally, the application responds faster when the pre-processing technique is integrated. The proposed generic cloud-based recommendation system is flexible and highly adaptable to new environments.