Energy efficiency on location based applications in mobile cloud computing: a survey

The constrained battery power of mobile devices poses a serious impact on user experience. As an increasingly prevalent type of applications in mobile cloud environments, location-based applications (LBAs) present some inherent limitations concerning energy. For example, the Global Positioning System based positioning mechanism is well-known for its extremely power-hungry attribute. Due to the severity of the issue, considerable researches have focused on energy-efficient locating sensing mechanism in the last a few years. In this paper, we provide a comprehensive survey of recent work on low-power design of LBAs. An overview of LBAs and different locating sensing technologies used today are introduced. Methods for energy saving with existing locating technologies are investigated. Reductions of location updating queries and simplifications of trajectory data are also mentioned. Moreover, we discuss cloud-based schemes in detail which try to develop new energy-efficient locating technologies by leveraging the cloud capabilities of storage, computation and sharing. Finally, we conclude the survey and discuss the future research directions.     

可靠性云计算环境下能源效率的提高机制

针对当前云计算能源效率低以及电源故障等不可靠问题,提出了一种物理主机整合机制以及调度算法,在保障云计算可靠性的同时提高能源效率。能量优化机制可以察觉优化时机,在电源等故障时执行调度算法。算法调节虚拟机到物理主机的映射,同时将相应物理主机中空闲的CPU容量,分配到正在运行的虚拟机中,从而提高能源效率。实验结果表明,与传统的调度算法相比,该算法在工作效率上提高了15.8%,在能量消耗上降低了9.8%。     

一种基于QoS参数归约的云计算环境能效评估方法

高性能、低功耗且具有QoS保障的高能效问题是云计算领域的一个研究难点。目前的研究主要是通过限定一个约束条件寻求另外指标的最优来实现三者之间的折衷或均衡,缺乏一种有效的能效计算方法和评估模型将三者整合,以更好地描述云环境能效的“程度”。提出一种云环境下QoS参数的归约方法和加权的能效模型,把系统性能作为一个关键因素引入QoS,并将离散的多个QoS参数度量值归约到同一个量纲区域内,获得评价权重矩阵,求得用户最终的QoS评价值,以单位能耗所提供的整体QoS水平值作为能效值,并且建立云数据中心的能效分级标识,最终将云环境下能效值刻画为一个定性的概念,实现了对云环境下能效的定性评估。此外,分别对单机环境和同构、异构的云计算环境中云数据中心的能效进行了评估分析,并进行了实验验证。实验结果表明,所提出的能效模型和评估方法在评价云系统的QoS水平和能源消耗方面是有效的。     

Early fault detection in IaaS cloud computing based on fuzzy logic and prediction technique

Availability is one of the most important requirements in production system. Keeping a persistent level of high availability in the Infrastructure-as-a-Service (IaaS) cloud computing is a challenge due to the complexity of service providing. By definition, the availability can be maintained by coupling with the fault tolerance approaches. Recently, many fault tolerance methods have been developed, but few of them adequately consider the fault detection aspect, which is critical to issue the appropriate recovery actions just in time. In this paper, based on a rigorous analysis on the nature of failures, we would like to introduce a method to early identify the faults occurring in the IaaS system. By engaging fuzzy logic algorithm and prediction technique, the proposed approach can provide better performance in terms of accuracy and reaction rate, which subsequently enhances the system reliability.     

基于功率谱密度的人体运动状态检测

提出了一种视频图像序列中人体运动状态检测的新方法。针对传统人体状态检测方法利用数据信息的复杂性,对图像中人体外接矩形区域提取位置和尺度稳定的非结构化数据信息——关键点,计算关键点的功率谱密度,在序列视频图像中可获得关键点功率谱密度的分布,利用其分布特点判断人体运动状态。实验证明方法的有效、可行和简易。     

基于云计算的多因素电力负荷坏数据自动检测方法

电力负荷坏数据对电网具有严重的破坏性,为了提高对电力负荷坏数据的检测能力,提出基于云计算的多因素电力负荷坏数据自动检测方法。采用云计算模型进行多因素电力负荷坏数据的分布式重组和集成运算,构建多因素电力负荷坏数据的云网格分布模型,在云网格空间中采用主成分特征分析方法进行多因素电力负荷坏数据特征检测,在双极型直流配电网中实现对多因素电力负荷坏数据的共模分量计算,提取电力负荷坏数据的能量谱特征量,根据负荷用电特性、潮流分布及其容量等参数,实现对多因素电力负荷坏数据的特征检测。仿真结果表明,采用该方法进行多因素电力负荷坏数据检测的自动性较好,检测准确率较高。     

Sports performance prediction model based on integrated learning algorithm and cloud computing Hadoop platform

This article discusses the classification and research performance information properties. It also discusses construction and application of the Hadoop cloud computing platform. The model presented in this article is a one piece learning algorithm which is a predictive model and a model of cloud based data collection. This model is supported by Hadoop which is suitable for computing with different data sizes. A large number of simulations are performed on the Hadoop platform, under different working conditions, to verify the accuracy and characteristics of the training skill. Spark framework of this research is to develop computational engine efficiency and improve rain prediction models successfully and effectively using big data and Hadoop learning. Therefore, the planned high timeliness and accuracy of real-time hurricane forecast with rain, can solve the problem.     

Enhancing security and efficiency in cloud computing authentication and key agreement scheme based on smart card

The Journal of Supercomputing - The password-based authentication mechanism is considered as the oldest and the most used method. It is easy to implement, and it does not require any particular...     

基于云计算架构的电力企业管理系统设计和应用研究

传统电力企业管理系统存在时效性较差,安全性不足,故障解决效率不高,可靠性有待提升等问题.为改进这些不足,设计一款基于云计算架构的电力企业管理系统,主要结构包括电力参数分析模块、办公软件管理模块、输电能力计算模块、服务管理模块、电费收缴管理模块、资产管理模块等内容,将其用于电力企业管理当中以弥补传统系统设计中的不足,增强...     

A new method based on PSR and EA-GMDH for host load prediction in cloud computing system

Host load prediction is one of the most effective measures for improving resource utilization in cloud computing systems. Due to the drastic fluctuation of the host load in the Cloud, accurately predicting the host load remains a challenge. In this paper, we propose a new prediction method that combines the Phase Space Reconstruction method and the Group Method of Data Handling based on an Evolutionary Algorithm. The performance of our proposed method is evaluated using two real-world load traces. The first is the load trace in a traditional distributed system, whereas the second is in a Google data center. The results show that the proposed method achieves a better prediction performance than some state-of-the-art methods.     

云计算平台上的增量分类研究

针对已有增量分类算法只是作用于小规模数据集或者在集中式环境下进行的缺点,提出一种基于Hadoop云计算平台的增量分类模型,以解决大规模数据集的增量分类。为了使云计算平台可以自动地对增量的训练样本进行处理,基于模块化集成学习思想,设计相应Map函数对不同时刻的增量样本块进行训练,Reduce函数对不同时刻训练得到的分类器进行集成,以实现云计算平台上的增量学习。仿真实验证明了该方法的正确性和可行性。     

Simulation of economic cost prediction of offshore oil pollution based on cloud computing embedded platform

Embedded component to real-time system, it is called in real-time embedded settings. In previous studies, this allows oil pollution, your decision-making and business management costs, data, for more information, security, maintenance, powerful effects are similar in other sectors of the market improved in terms of full use. Cloud computing is without direct active management of the user, the system resources of the computer, especially the data storage (cloud storage) and on-demand computing capabilities available. This term is, in general, will be used to describe the data center available to many users on the Internet. The information collected from a variety of channels, can be managed in order to grow at such a rapid rate. Some service providers, please refer to the need to upgrade the data collection capabilities of your company. If almost all aspects of the business of the industry of oil pollution have been digitized, in order to facilitate the collection of oil pollution data, embedded computing solutions, it plays an important role in simplification. The integrated energy system is proposed, tuned to complex energy-requiring systems, marine oil pollution, diesel oil supply system, storage oil, and transport system from the marine oil pollution platform. Among them, strong adhesion, such as waste heat recovery and the associated gas use trend, have been proven too closely integrate the introduction of new technologies.     

基于膜计算的蝙蝠算法在云计算资源调度的研究

如何进一步实现云计算环境下的资源利用最大化是目前研究的热点.建立云计算环境下的资源分配模型,云计算资源调度使用蝙蝠算法,同时引入膜计算概念,提出一种基于膜计算的蝙蝠算法,将膜系统内部分解为主膜和辅助膜,在辅助膜内进行蝙蝠的个体局部寻优,将优化后的个体传送到主膜间进行全局优化,从而达到了云计算资源优化分配要求.通过CloudSim平台与其他算法进行仿真对比表明算法提高了云计算环境下的系统处理时间和效率,使得云计算环境下的资源分配更加合理.     

基于云计算和改进K-means算法的海量用电数据分析方法

针对小区居民用电数据挖掘效率低、数据量大等难题,进行了基于云计算和改进K-means算法的海量用电数据分析方法研究。针对传统K-means算法中存在初始聚类中心和K值难确定的问题,提出一种基于密度的K-means改进算法。首先,定义样本密度、簇内样本平均距离的倒数和簇间距离三者乘积为权值积,通过最大权值积法依次确定聚类中心,提高了聚类的准确率;然后,基于MapReduce模型实现改进算法的并行化,提高了聚类的效率;最后,以小区400户家庭用电数据为基础,进行海量电力数据的挖掘分析实验。以家庭为单位,提取出用户的峰时耗电率、负荷率、谷电负荷系数以及平段用电量百分比,建立聚类的数据维度特征向量,完成相似用户类型的聚类,同时分析出各类用户的行为特征。基于Hadoop集群的实验结果证明提出的改进K-means算法运行稳定、可靠,具有很好的聚类效果。