面向异构集群系统的动态负载均衡技术研究

在集群系统中的一个非常重要的问题就是尽量确保负载的均衡.由于目前的负载均衡算法大多针对同构的集群系统,没有很好的扩展性.研究了异构集群系统,提出了一种异构服务器集群的动态负载均衡算法,并取得了较好的效果,特别是在负载较重的时候.     

云计算环境下的混沌萤火虫的资源负载均衡算法

负载均衡问题是当前云计算研究的重要问题。由于云计算中的负载均衡存在效率低、准确性不高以及资源需求动态变化等问题,建立了云计算环境下的负载均衡模型,通过在发送者策略中引入混沌算法和在接收者策略中引入萤火虫算法,提高了目标节点的最优化选择以及转移任务量的准确性。仿真实验表明,改进后的资源负载算法能够有效地避免负载处理的不均衡,提高系统整体处理能力。     

嵌入式Web服务器的负载均衡技术的研究

为提高嵌入式Web服务器的处理能力,克服其计算和存储资源有限的缺点,增强容错性,在分析了嵌入式Web服务器现状及发展趋势的基础上,结合传统Web服务器负载均衡技术,探讨了基于嵌入式Web服务器的负载均衡技术,设计了适合于嵌入式Web服务器的基于访问请求优先级的最少连接数算法,并给出了工作流程,实验表明系统达到了对紧急请求优先、快速、准确执行的目标.     

An operator load-balancing problem in a semi-automatic parallel machine shop

This paper deals with a load-balancing problem among several operators in a semi-automatic parallel machine shop in which two types of machine are operated. The objective is to assign jobs to the proper machines and allocate machines to operators in order to minimize the unbalance of the workloads among operators under the constraints of available machine- and operator-time. There are two types of parallel machines and jobs are classified into the two types. However, operator can handle machines of both types.Although, this situation can be formulated with non-linear integer programming, it is difficult to solve this program. Therefore, a hierarchical heuristic solution procedure is suggested and the performance of the algorithm is evaluated with various data.     

Pegasus: a distributed and load-balancing fingerprint identification system

Fingerprint has been widely used in a variety of biometric identification systems in the past several years due to its uniqueness and immutability. With the rapid development of fingerprint identification techniques, many fingerprint identification systems are in urgent need to deal with large-scale fingerprint storage and high concurrent recognition queries, which bring huge challenges to the system. In this circumstance, we design and implement a distributed and load-balancing fingerprint identification system named Pegasus, which includes a distributed feature extraction subsystem and a distributed feature storage subsystem. The feature extraction procedure combines the Hadoop Image Processing Interface (HIPI) library to enhance its overall processing speed; the feature storage subsystem optimizes MongoDB’s default load balance strategy to improve the efficiency and robustness of Pegasus. Experiments and simulations are carried out, and results show that Pegasus can reduce the time cost by 70% during the feature extraction procedure. Pegasus also balances the difference of access load among front-end mongos nodes to less than 5%. Additionally, Pegasus reduces over 40% of data migration among back-end data shards to obtain a more reasonable data distribution based on the operation load (insertion, deletion, update, and query) of each shard.     

一种云环境下的发动机健康管理系统及其应用

针对航空公司对大量飞机发动机进行健康管理的需求,通过建立发动机健康管理云端数据中心,建立了一种云环境下的民航发动机健康管理系统,该系统对于验证发动机故障诊断方法的有效性具有突出优势,并且对于实现多种方法协同进行发动机故障诊断具有重要价值;提出了一种基于灰色关联分析的灰色故障识别方法,通过在云端平台使用灰色故障识别方法实现JT9D-7R4发动机的典型气路性能故障诊断为例,表明云环境下的发动机健康管理系统可以有效地进行航空发动机故障诊断。     

基于公平调度算法的分布式系统负载均衡研究

负载均衡是提高分布式系统性能的重要技术,同时也是系统高可用性、可扩展性、冗余性的必然要求.针对分布式系统任务调度不均衡问题,在分析和建立系统仿真和任务调度模型的基础上,提出了一种基于公平指标的任务调度负载均衡算法,推导出在多节点条件下的任务分配方法,并在此模型下改进了基于公平指标的负载均衡算法.最后,在Linux平台下,进行了仿真实验和性能比较.实验结果表明,该算法是有效的,它可以有效地提高分布式系统的性能和效率.     

An improved load-balancing mechanism based on deadline failure recovery on GridSim

Grid computing has emerged a new field, distinguished from conventional distributed computing. It focuses on large-scale resource sharing, innovative applications and in some cases, high performance orientation. The Grid serves as a comprehensive and complete system for organizations by which the maximum utilization of resources is achieved. The load balancing is a process which involves the resource management and an effective load distribution among the resources. Therefore, it is considered to be very important in Grid systems. For a Grid, a dynamic, distributed load balancing scheme provides deadline control for tasks. Due to the condition of deadline failure, developing, deploying, and executing long running applications over the grid remains a challenge. So, deadline failure recovery is an essential factor for Grid computing. In this paper, we propose a dynamic distributed load-balancing technique called “Enhanced GridSim with Load balancing based on Deadline Failure Recovery” (EGDFR) for computational Grids with heterogeneous resources. The proposed algorithm EGDFR is an improved version of the existing EGDC in which we perform load balancing by providing a scheduling system which includes the mechanism of recovery from deadline failure of the Gridlets. Extensive simulation experiments are conducted to quantify the performance of the proposed load-balancing strategy on the GridSim platform. Experiments have shown that the proposed system can considerably improve Grid performance in terms of total execution time, percentage gain in execution time, average response time, resubmitted time and throughput. The proposed load-balancing technique gives 7 % better performance than EGDC in case of constant number of resources, whereas in case of constant number of Gridlets, it gives 11 % better performance than EGDC.     

智能仪表的一种数据交换技术

介绍了智能仪表中的双CPU数据交换技术，这种方法是基于非易失性存储器E^2PROM X24C16的数据交换的方法，它具有编程简单、性能可靠和调试简便的特点。     

An energy-efficient cloud system with novel dynamic resource allocation methods

The Journal of Supercomputing - In the last decade, cloud computing has brought enormous changes to people’s lives. Cloud computing gives a client-driven computational model. In this case,...     

An automated day-time cloud detection technique applied to MSG-SEVIRI data over Western Europe

Since February 2003, the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard the first Meteosat Second Generation (MSG) satellite has provided radiance data in 12 spectral bands for a full Earth hemisphere every 15 minutes. This high frame rate renders it an excellent tool for studies of atmospheric transport of pollutants, aerosol and clouds. TNO (Netherlands Organisation for Applied Scientific Research) is currently developing an algorithm for the retrieval of aerosol properties from MSG-SEVIRI observations over cloud-free scenes. This requires rigorous cloud screening for which a fast and stand-alone algorithm is developed. The detection technique described in this paper, which is based on the ATSR-2 (Along Track Scanning Radiometer 2) cloud screening algorithm, can be easily implemented, and satisfactorily identifies clouds. The study presented here focuses on Western Europe for the year 2006. Cloud detection results are compared to the KNMI/MF (Royal Netherlands Meteorological Institute/Meteo-France) and the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud detection algorithms. According to the statistics, the results obtained with our algorithm show good agreement (>80%) with these data sets.     

An intrusion detection and prevention system in cloud computing: A systematic review

The distributed and open structure of cloud computing and services becomes an attractive target for potential cyber-attacks by intruders. The traditional Intrusion Detection and Prevention Systems (IDPS) are largely inefficient to be deployed in cloud computing environments due to their openness and specific essence. This paper surveys, explores and informs researchers about the latest developed IDPSs and alarm management techniques by providing a comprehensive taxonomy and investigating possible solutions to detect and prevent intrusions in cloud computing systems. Considering the desired characteristics of IDPS and cloud computing systems, a list of germane requirements is identified and four concepts of autonomic computing self-management, ontology, risk management, and fuzzy theory are leveraged to satisfy these requirements.     

Energy-based cloud detection in multispectral images based on the SVM technique

In this paper, the energy characteristics of Gabor texture are used for cloud detection in high-resolution multispectral images. First, the satellite remote-sensing image is divided into superpixels using simple linear iterative clustering (SLIC), and then, the energy characteristics of Gabor texture and spectral characteristics are computed by extracting the texture features of the superpixels. The features of the cloud superpixels are used as the learning sample of the support vector machine (SVM) classifier, and a classification model is obtained by training the SVM classifier. Finally, a cloud-detection experiment is conducted for various sensor images with three visible bands and one near-infrared band. The experimental results showed that the proposed method provides an excellent average overall accuracy for thick and thin clouds in a complex background of forests, harbours, snow and mountains. The characteristic parameters of this paper are not limited by the image parameters; thus, they provide good results and universality for various types of sensors.     

Nearest-neighbor algorithms for load-balancing in parallel computers

With nearest-neighbor load-balancing algorithms, a processor makes balancing decisions based on localized workload information and manages workload migrations within its neighborhood. The paper compares a couple of fairly well-known nearest-neighbor algorithms, the dimension-exchange (DE) and the diffusion (DF) methods and their several variants—the average dimension-exchange (ADE), optimally tuned dimension-exchange (ODE), local average diffusion (ADF) and optimally tuned diffusion (ODF). The measures of interest are their efficiency in driving any initial workload distribution to a uniform distribution and their ability in controlling the growth of the variance among the processors' workloads. The comparison is made with respect to both one-port and all-port communication architectures and in consideration of various implementation strategies including synchronous/asynchronous invocation policies and static/dynamic random workload behaviors. It turns out that the dimension-exchange method outperforms the diffusion method in the one-port communication model. In particular, the ODE algorithm is best suited for statically synchronous implementations of a load-balancing process regardless of its underlying communication models. The strength of the diffusion method is in asynchronous implementations in the all-port communication model; the ODF algorithm performs best in that case. The underlying communication networks considered assume the most popular topologies, the mesh and the torus and their special cases: the hypercube and the k-ary n-cube.     

存储区域网络的动态负载均衡算法研究

针对存储区域网络（SAN）中负载变化的特性,提出了一种临界值动态任务负载分配算法。通过负载状态值准确地反映SAN中当前负载情况;通过调整临界因子来避免服务器重载的现象;通过测试获得算法中所需的参数,使配置更为简单合理。实验结果显示,该算法对大数据大密度的任务访问效果明显。     

A multi-dimensional histogram technique for cloud classification

An old method (often called graphical) for decomposing a mixed distribution to Gaussian components was generalized for the multidimensional case. The technique approximates an initial histogram by means of a sum of normally distributed components. A special separation algorithm for n-dimensional (nD) histograms was introduced for that purpose. Application of the separation method enables one to replace the customary pixel-by-pixel processing with a cluster-by-cluster procedure in any threshold algorithm. Stability of the algorithm was tested, comparing the decomposition of the radiation histograms produced by means of the Advanced Very High Resolution Radiometer (AVHRR) five channel measurements in the 3D and 5D cases for an area over Europe during nine orbits. The results show that multidimensional histograms are easily separable due to sufficiently large Euclidean distance between basic cloud and surface clusters in the measurement space. Applying the separation scheme in conjunction with a certain threshold technique to process the AVHRR-based histograms enables one to produce an automatic cloud detection algorithm. The algorithm sets necessary thresholds without auxiliary (i.e. beyond AVHRR) information and estimates average cloud amount, cloud top temperature and cloud reflectance at three levels for the histogram area. An example of such an algorithm for determining cloudiness parameters necessary for the Earth's radiation budget monitoring is presented.     

云机器人系统研究综述

随着云计算、大数据和其他新兴技术的发展,云技术和多机器人系统的集成使得能够设计出具有改进能源效率、实时性高、成本低的多机器人系统。为了解决云增强系统中机器人的潜力,介绍了云机器人的起源、发展过程以及有关云机器人的研究项目。介绍了云机器人系统的整体架构,然后从开源源码、云计算、雾计算、网络切换管理、机器人即服务（RaaS）、大数据等方面分析了云机器人系统中各层的主要发展驱动力。详细阐述了当前云机器人系统中的关键技术,并给出了一些可能的解决方案。最后,对云机器人系统的未来发展进行了讨论。     

一种改进的云存储系统容错机制

在云存储平台下, 提出了一种基于访问统计的自适应容错机制SFMAF, 该机制通过近似最近最少使用算法维护一张文件访问频率表来自适应调整容错方式。SFMAF对于常读取的文件采用副本冗余机制; 对于不常读取的文件采用Reed-Solomon（RS）纠删码容错机制。实验结果表明, SFMAF相对于副本冗余机制, 在CPU和内存使用率可接受范围内的增加上, 减少了系统内部数据的传输流量, 即减少了系统存储空间。