基于QoS的云任务调度算法研究

随着云计算的发展,越来越多的人开始使用"云"来处理他们的业务,这对公有云平台提出了一些重要挑战:如何让公有云平台在不断激增的云业务模式下,既能保证云用户的服务满意度,同时也能稳步提高云服务商(CloudServiceProviders)的收益。首先建立了任务调度算法以及QoS需求约束等相关模型,然后将QoS(Qualityof Service)需求约束分别引入到三种传统任务调度算法(FCFS(RR)、MinMin和MaxMin算法)中对其进行改进,接着将改进后的算法与传统任务调度算法之间进行比较,通过选取在任务完成度、任务最终完成时间(MakeSpan)、任务平均执行时间(这些影响用户的服务满意度),以及云服务商总收益等方面的指标表现,最后确定了一个较好的改进MinMin任务调度算法(I-MinMin算法)。实验通过CloudSim进行模拟,并采用了现有的阿里云ECS云服务器中的虚拟机实例相关数据。结果表明:在任务量不断增加的情况下,I-MinMin算法在用户的服务满意度各方面,以及云服务商总收益等指标表现上要更优于其他算法,更好地实现了用户和云服务商的双重利益。     

基于梯形云模型的装备保障训练效果评估研究

装备保障训练效果评估是检验和提高装备保障能力的重要手段,针对装备保障训练效果综合评估工作中定性定量指标转化计算的问题,提出了基于梯形云理论的评估方法,云理论是一种用于处理不确定性和模糊性的数学工具,从知识水平、岗位能力、装备效能情况等方面建立评估体系对维修保障人员训练效果进行分析研究,运用Delphi法建立了装备保障训练效果评估指标体系,在指标选取原则的基础上,运用AHP与指标属性重要度的综合方法进行指标权重的确定,然后通过运用云模型云发生器、云合并算法和云相似度算法等,较好地解决了装备保障训练效果评估中指标的定性描述与定量综合计算的问题,最终通过实例比较表明,所提出的云评估模型所得出的等级评价结果客观而全面,对定性综合评估研究具有参考价值.     

个性化服务用户模型研究

以数字图书馆为研究对象,提出了一种个性化服务用户模型构架,并对实现过程中的几表示方法、用户模型的建立以及更新算法进行了详细论述,最后在个性化文本过滤算法基础上,得到在实际的数字图书馆中的验证结果.用户兴趣的提取采用支持向量机分类算法和无监督聚类算法相结合的隐式方式获得;在考虑最近到达的兴趣与用户原有兴趣序列的综合影响的基础上,用户兴趣的更新采用最近最少使用淘汰算法.实验结果表明,该模型具有隐式荻取用户兴趣、用户模型更新命中率高等特点.     

三网融合下图书云服务个性化推荐

随着云计算技术的飞速发展,数字图书馆云平台 SaaS 层的图书应用服务数量将会快速增长,为图书用户选择个性化的云服务带来困难。通过建立偏好树,构建了三网融合环境下的图书用户模型和图书云服务模型。为了确定图书云服务对图书用户的推荐度,设计了服务选择算法。经过实验数据分析,该算法可以根据图书用户模型的偏好需求,为用户推荐匹配度较高的图书云服务。     

用户驱动的组合服务选择算法

用户需求准确获取和复杂不确定环境下的最优服务选择是服务组合研究中的难题.首先提出一个接近用户表达习惯的用户权重表达模型获取用户服务质量(QoS)权重,然后提出一种基于层次分析法(AHP)和逼近理想解排序法(TOPSIS)的组合服务选择算法(CWSSA)以解决最优服务选择问题.该算法转换用户的QoS属性两两比较矩阵为用户权重,评估区间数表示的复杂不确定环境下的QoS信息.还介绍了一个QoS属性关系表示的用户权重模型和区间数描述的QoS模型及聚合算法.实验验证了该算法的优越性和有效性.     

基于主客观综合权重的云服务时间序列选择算法

目前已有的云服务时间序列选择算法没有很好地考虑用户的QoS（quality of service）偏好信息,而传统的用户偏好算法只适用于QoS指标值为单一数值的情况,在QoS指标值为时间序列向量的情况下无法进行有效计算.因此,本文提出了一种基于主客观综合权重的云服务时间序列选择算法（Time series of cloud services selection algorithm employing subjective and objective weight,简称TCSOW）.此算法从基于用户QoS偏好层次的主观权重计算方法和基于QoS指标相关性的客观权重计算方法这两个角度进行详细描述,通过结合时间序列QoS模型进行云服务选择.实验分析表明,提出的TCSOW算法在有效解决用户QoS偏好的同时又充分考虑云服务集的QoS指标数据分布特性,使最终的度量结果具有较高的准确性与科学性.     

一种云环境中数据流的高效多目标调度方法

在分布式系统中,云计算作为一种新的服务提供模式出现,其执行科学应用数据流时的优势和缺点得到越来越多的关注,其主要特点为拥有大量同质和并发的任务包,并构成了性能瓶颈的主要因素.在云数据流中调度大规模任务是已被证实的NP难问题.文中专注于解决优化云数据流中的调度过程,并由现实世界启发,从不同角度将优化目标分别划分为用户指标（完工时间和经济成本）和云系统指标（网络带宽、存储约束和系统公平度）,并将该调度问题制定成为一个新的连续的合作博弈,设计出快速收敛的高效Muliti-Objective Game（MOG）调度算法,在优化用户指标的同时,实现系统指标的约束,并保证云资源的效率和公平度.通过综合实验,证实文中方法和其它相关算法相比,在算法复杂度O（l·K·M）（明显改进数量级）,结果质量（一些情况下最佳）,系统级别公平性上具有明显优越性.     

一种基于AHP-BP的工业控制系统风险评估方法

该文提出了一种基于由层次分析法和BP神经网络相结合的混合算法模型来对工业控制系统进行风险评估。首先利用信息安全等级测评标准制定了更加科学全面的初始评估模型,然后用AHP算法算出该模型各指标权重,并根据综合权重挑选出比较重要的指标作为BP神经网络的输入,然后依据历史评估数据对神经网络模型进行训练,最后以某冶金工业控制系统为例对该算法进行了验证。     

基于性能感知预测的云服务推荐模型

互联网上出现越来越多的云服务,面对种类繁多的云服务,如何准确地在众多云服务中把符合用户需求并且性能好价格低的服务推荐给用户成为云服务推荐的研究热点.现有的服务推荐方法往往只是根据当前云服务的历史性能记录为用户进行推荐,并没有充分考虑云服务的性能趋势.针对上述问题,本文提出了一种基于性能预测的服务推荐模型,该模型利用共轭梯度改进人工神经网络对云服务的性能进行预测,使用层次分析法对性能,价格等因素进行综合比较计算,最终为用户推荐最为合适的云服务.实验结果表明,使用改进神经网络对服务性能进行预测能够获得较高的准确度,层次分析法可以综合考虑服务的性能与价格,为用户推荐最为合适的云服务.     

基于用户空间位置评分云模型的Web服务协同过滤推荐算法

Web服务作为无形的产品,不具备真实环境下的空间地理位置坐标,针对服务推荐中无法衡量用户群体与Web服务之间的距离位置关系,造成用户相似度计算失衡,导致推荐不准确等问题,提出了基于用户空间位置评分云模型的Web服务协同过滤推荐算法.首先基于用户群体的行为数据量化Web服务的热度区域,通过空间位置量化评分描述用户对于Web服务的兴趣偏好;其次利用云模型来描述每个用户空间行为评分的整体特征,设计了云模型间相似贴近度的计算方法,基于该方法提出了一种用户差异程度系数评估算法,并作为调控系数优化了皮尔森相似度量;最后通过协同过滤找出用户感兴趣的Web服务.实验结果表明该算法使得用户行为偏好的区域划分更加精确,在推荐准确率上明显提高,为基于位置的Web服务推荐提供新颖的方案.     

Augmented context-based recommendation service framework using knowledge over the Linked Open Data cloud

This research proposes ACARDS (Augmented-Context bAsed RecommenDation Service) framework that is able to utilize knowledge over the Linked Open Data (LOD) cloud to recommend context-based services to users. To improve the level of user satisfaction with the result of the recommendation, the ACARDS framework implements a novel recommendation algorithm that can utilize the knowledge over the LOD cloud. In addition, the noble algorithm is able to use new concepts like the enriched tags and the augmented tags that originate from the hashtags on the SNSs materials. These tags are utilized to recommend the most appropriate services in the user’s context, which can change dynamically. Last but not least, the ACARDS framework implements the context-based reshaping algorithm on the augmented tag cloud. In the reshaping process, the ACARDS framework can recommend the highly receptive services in the users’ context and their preferences. To evaluate the performance of the ACARDS framework, we conduct four kinds of experiments using the Instagram materials and the LOD cloud. As a result, we proved that the ACARDS framework contributes to increasing the query efficiency by reducing the search space and improving the user satisfaction on the recommended services.     

异构云环境下AHP定权的多目标强化学习作业调度方法

随着新型基础设施建设(新基建)的加速,云计算将获得新的发展契机.数据中心作为云计算的基础设施,其内部服务器不断升级换代,这造成计算资源的异构化.如何在异构云环境下,对作业进行高效调度是当前的研究热点之一.针对异构云环境多目标优化调度问题,设计一种AHP定权的多目标强化学习作业调度方法.首先定义执行时间、平台运行能耗、成...     

A field-based service management and discovery method in multiple clouds context

In diverse and self-governed multiple clouds context, the service management and discovery are greatly challenged by the dynamic and evolving features of services. How to manage the features of cloud services and support accurate and efficient service discovery has become an open problem in the area of cloud computing. This paper proposes a field model of multiple cloud services and corresponding service discovery method to address the issue. Different from existing researches, our approach is inspired by Bohr atom model. We use the abstraction of energy level and jumping mechanism to describe services status and variations, and thereby to support the service demarcation and discovery. The contributions of this paper are threefold. First, we propose the abstraction of service energy level to represent the status of services, and service jumping mechanism to investigate the dynamic and evolving features as the variations and re-demarcation of cloud services according to their energy levels. Second, we present user acceptable service region to describe the services satisfying users’ requests and corresponding service discovery method, which can significantly decrease services search scope and improve the speed and precision of service discovery. Third, a series of algorithms are designed to implement the generation of field model, user acceptable service regions, service jumping mechanism, and user-oriented service discovery.We have conducted an extensive experiments on QWS dataset to validate and evaluate our proposed models and algorithms. The results show that field model can well support the representation of dynamic and evolving aspects of services in multiple clouds context and the algorithms can improve the accuracy and efficiency of service discovery.     

Contract RBAC in cloud computing

Cloud computing is a fast growing field, which is arguably a new computing paradigm. In cloud computing, computing resources are provided as services over the Internet and users can access resources based on their payments. The issue of access control is an important security scheme in the cloud computing. In this paper, a Contract RBAC model with continuous services for user to access various source services provided by different providers is proposed. The Contract RBAC model extending from the well-known RBAC model in cloud computing is shown. The extending definitions in the model could increase the ability to meet new challenges. The Contract RBAC model can provide continuous services with more flexible management in security to meet the application requirements including Intra-cross cloud service and Inter-cross cloud service. Finally, the performance analyses between the traditional manner and the scheme are given. Therefore, the proposed Contract RBAC model can achieve more efficient management for cloud computing environments.     

一种云计算数字签名技术的研究与实现

由于云计算具有可靠性高、成本低、性能高等特点,已经成为了新一代信息技术变革的核心。为了能够有效推动互联网安全架构的可持续性发展。本文提出了一种云计算数字签名技术,该技术有机结合了云计算技术与数字签名技术。首先阐述了数字签名系统中应用云计算技术的价值,其次,分析了云计算数字签名技术的实现模型,并且设计了云计算数字签名协议,最后,开展了实例分析,将云计算数字签名技术应用到移动营业厅的业务信息交易中。结果表明：所有业务均可在网上完成,那么既可大大方便用户,又可降低移动营业厅的工作量,还可确保业务信息交易的可靠性、唯一性、真实性、安全性与不可抵赖性。通过结果分析得出结论：云计算数字签名技术既可保障签名文件的完整性与真实性,又可跨平台操作签名文件,还可让数字签名模型利用互联网来对密码运算基础设施进行便捷化、可靠化地访问。     

Resource scheduling approach for multimedia cloud content management

With the development of multimedia application and services, the multimedia technology has already permeated each aspect of our life. Multimedia cloud is used for processing multimedia services. However due to huge data volume, high concurrency, strict real-time, resource scheduling for content dissemination in multimedia cloud still remain challenges. In order to increase the user satisfaction and decrease completion time of content dissemination, the resource scheduling for content dissemination in multimedia cloud is proposed in this paper. The multimedia jobs are clustered according to user expectation and job complexity. The job with highest priority will be executed first. Moreover, considered multimedia task types and the impact of stragglers, the multimedia task scheduling based on task types and node workload is presented, which is a time-efficient scheduling approach. The experiments are conducted and the experiment results show that the job clustering algorithm-based user expectation and job complexity in multimedia cloud has better user satisfaction and shorter completion time, while the multimedia task scheduling based on task types and node workload can reduce completion time and achieve load-balancing.     

面向个性化云服务基于用户类型和隐私保护的信任模型

针对云用户难以获得个性化、高质量服务的问题,提出一种面向个性化云服务基于用户类型和隐私保护的信任模型。该模型先根据节点间的历史交易,将用户节点分为亲情节点、陌生节点及普通节点三种类型;其次,为了保护节点反馈的隐私信息,引入信任评估代理作为信任评估的主体,并且设计了基于用户类型的信任值评估方法;最后,鉴于信任的动态性,结合交易时间和交易额度提出一种新的基于服务质量的信任更新机制。实验结果表明,与AARep模型及PeerTrust模型相比,该模型不仅在恶意节点比例较低的场景中具有优势,而且在恶意节点比例超过70%的恶劣场景中,其交互成功率也分别提高了10%和16%,克服了云环境下用户节点和服务节点交互成功率低的缺点,具有较强的抵抗恶意行为的能力。     

A cloud-based intelligent TV program recommendation system

In recent years, cloud computing technology has matured significantly, as has the development of digital TV services. This, therefore, has led to an increased demand for improved quality TV services. In this paper, cloud computing technology is used to build a program recommendation system for digital TV programs, and the Hadoop Fair Scheduler is utilized to improve processing performance. Historical data of watched TV programs are collected through an electronic program guide, and then processed using K-means clustering, term frequency/inverse document frequency and k-nearest neighbor algorithms, to obtain clusters of audience groups and to find popular TV programs for each cluster. The proposed system can process massive amounts of user data in real-time, and can easily be scaled up.