基于特征迭代的云存储数据即时确定性删除方法

传统数据删除方法易受到云存储环境中大量近似特征的影响,产生冗余数据,导致加解密时间过长、密钥可用率较低,为此提出一种基于特征迭代的云存储数据即时确定性删除方法。首先,提取云存储数据中的冗余数据特征,对云存储下冗余数据进行分类,迭代直至收敛,实现冗余数据高性能删除;其次,采用加密机制实现云存储数据在网络用户间的安全共享,将原始数据密文切分为剩余密文和采样密文;最后,把不完整的剩余数据密文上传至云端,同时引入可信任第三方对取样密文进行保存,通过销毁取样密文实现数据即时确定性删除。实验结果表明,所提方法的数据密文拆分所用时间较短,且密钥可用率较高,可达90%,说明其方法能够有效满足云存储系统中对冗余数据或过期数据的确定性删除要求。     

基于云端协同计算架构的边缘端I/O密集型虚拟机资源分配方案

大型制造业生产车间业务流程复杂,传统固定资源配置方式将产生负载不均衡的问题。针对此问题,设计并实现了一种基于云端协同架构的边缘端I/O密集型虚拟机资源的分配算法,通过定义计算节点中每个I/O密集型虚拟机三个维度的信息计算得出I/O密集型虚拟机的优先级,并按最大优先级队列实时统一分配硬件资源。实验结果表明,该算法在应对工业机械设备高响应和高通量的要求上有较为明显的提升,为实际生产起到了优化资源配置的作用。     

基于多判据的散乱点云特征点提取算法

为了有效获取散乱点云中的尖锐特征点和边界特征点,提出一种利用多判据融合的特征点提取算法。首先利用一种改进的k-d tree构建点云拓扑,搜索样点的K局部邻域;然后利用法向夹角判定准则、核密度判定准则、场力和判定准则分别求取各个样点局部邻域的三个特征参数,最后通过加权计算特征参数得到每个样点的特征值与全局判定阈值,特征值比阈值大的点即为特征点。实验证明,该算法能有效的获取散乱点云中边沿特征点与尖锐特征点。     

云制造环境下面向多目标优化的虚拟资源调度研究

为解决云制造环境下虚拟资源调度存在的算法求解效率不高、模型建立缺乏考虑任务间关系约束和任务间及子任务间的物流时间及成本因素等不足,构建了兼顾交货期时间最小化、服务成本最低化、服务质量最优化为目标的多目标虚拟资源调度模型;采用一种基于项目阶段的双链编码方式进行编码,并提出自适应交叉与变异概率公式,以避免交叉、变异概率始终不变导致算法效率下降与过早收敛的问题;在此基础上利用基于项目阶段的多种交叉变异策略相结合的改进遗传算法进行求解,保证了算法的全局与局部搜索性能。实例结果表明,相比于传统的模型与算法,该模型适用性更强,改进的遗传算法在求解效率、准确度与稳定性方面均有较大提高。     

Unikernel的研究及其进展

Unikernel是一种专门定制、优化的轻量级虚拟机,在云环境中,它能够解决由传统虚拟机带来的资源消耗大、低效率、低安全性的问题,有望成为超越Docker的下一代轻量级微服务系统架构。目前,Unikernel开源项目较多,技术呈现复杂性和多样性,使得研究人员和开发人员难以准确理解和把握。为此,首先介绍了Unikernel的基本概念和基本特点;然后总结出了Unikernel的系统架构,并详细阐述了Unikernel关键技术的研究工作进展,包括应用层、库操作系统层和平台适配层;最后结合已有的研究成果,探讨了Unikernel的研究方向及其面临的机遇和挑战。     

熵权-云模型在土石坝安全综合评价中的应用

提出了一种基于熵权-云模型的方法,借鉴云模型的特点,在构建评价层次结构模型时,采用改进层次分析法结合信息熵确定各评价指标权重,以克服运用模糊层次综合分析法时权重受人为主观判断影响较大和模糊概念会丢失部分不确定性等问题,改进隶属函数对土石坝进行综合评价,并以某均质土石坝为例评价了大坝安全状态,分析表明该方法具有可行性。     

Bandwidth‐aware divisible task scheduling for cloud computing

Task scheduling is a fundamental issue in achieving high efficiency in cloud computing. However, it is a big challenge for efficient scheduling algorithm design and implementation (as general scheduling problem is NP‐complete). Most existing task‐scheduling methods of cloud computing only consider task resource requirements for CPU and memory, without considering bandwidth requirements. In order to obtain better performance, in this paper, we propose a bandwidth‐aware algorithm for divisible task scheduling in cloud‐computing environments. A nonlinear programming model for the divisible task‐scheduling problem under the bounded multi‐port model is presented. By solving this model, the optimized allocation scheme that determines proper number of tasks assigned to each virtual resource node is obtained. On the basis of the optimized allocation scheme, a heuristic algorithm for divisible load scheduling, called bandwidth‐aware task‐scheduling (BATS) algorithm, is proposed. The performance of algorithm is evaluated using CloudSim toolkit. Experimental result shows that, compared with the fair‐based task‐scheduling algorithm, the bandwidth‐only task‐scheduling algorithm, and the computation‐only task‐scheduling algorithm, the proposed algorithm (BATS) has better performance. Copyright © 2012 John Wiley & Sons, Ltd.     

基于改进的投影寻踪-云模型的农业灌溉水质综合评价

提出基于投影寻踪函数和云模型的水质综合评价模型,选取太湖流域20个样本盐度、氯化物、氨氮、溶解性固体4类具有代表性的农业灌溉水质监测数据,在综合其投影值及隶属度基础上,计算农业灌溉水质的等级区分粒度。结果表明,投影寻踪模型计算值平均绝对误差仅为0.125 2级,达到了较好的水质评价精确度,同时利用云模型计算各个监测指标得到的最大综合确定度所属级别与经验等级一致。     

Strategies and methods for cloud migration

Legacy system migration to the cloud brings both great challenges and benefits, so there exist various academic research and industrial applications on legacy system migration to the cloud. By analyzing the research achievements and application status,we divide the existing migration methods into three strategies according to the cloud service models integrally. Different processes need to be considered for different migration strategies, and different tasks will be involved accordingly. The similarities and differences between the migration strategies are discussed, and the challenges and future work about legacy system migration to the cloud are proposed. The aim of this paper is to provide an overall presentation for legacy system migration to the cloud and identify important challenges and future research directions.     

A New Risk Assessment Approach for Cloud Consumer

The cloud computing introduces several changes in technology that have resulted a new ways for cloud providers to deliver their services to cloud consumers mainly in term of security risk assessment, thus, adapting a current risk assessment tools to cloud computing is a very difficult task due to its several characteristics that challenge the effectiveness of risk assessment approaches. Consequently, there is a need of risk assessment approach adapted to cloud computing. With such an approach, the cloud consumers can be guaranteed the effectiveness of data security and the cloud providers can win the trust of their cloud consumers. This paper requires the formalization of risk assessment method for conventional system as fundamental steps towards the development of flexible risk assessment approach regarding cloud consumers.