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.     

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.     

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 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。     

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 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.     

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.     

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.     

A privacy preserving authorisation system for the cloud

In this paper we describe a policy based authorisation infrastructure that a cloud provider can run as an infrastructure service for its users. It will protect the privacy of users? data by allowing the users to set their own privacy policies, and then enforcing them so that no unauthorised access is allowed to their data. The infrastructure ensures that the users? privacy policies are stuck to their data, so that access will always be controlled by the policies even if the data is transferred between cloud providers or services. This infrastructure also ensures the enforcement of privacy policies which may be written in different policy languages by multiple authorities such as: legal, data subject, data issuer and data controller. A conflict resolution strategy is presented which resolves conflicts among the decisions returned by the different policy decision points (PDPs). The performance figures are presented which show that the system performs well and that each additional PDP only imposes a small overhead.     

An SLA-based cloud computing that facilitates resource allocation in the distributed data centers of a cloud provider

The number of cloud service users has increased worldwide, and cloud service providers have been deploying and operating data centers to serve the globally distributed cloud users. The resource capacity of a data center is limited, so distributing the load to global data centers will be effective in providing stable services. Another issue in cloud computing is the need for providers to guarantee the service level agreements (SLAs) established with consumers. Whereas various load balancing algorithms have been developed, it is necessary to avoid SLA violations (e.g., service response time) when a cloud provider allocates the load to data centers geographically distributed across the world. Considering load balancing and guaranteed SLA, therefore, this paper proposes an SLA-based cloud computing framework to facilitate resource allocation that takes into account the workload and geographical location of distributed data centers. The contributions of this paper include: (1) the design of a cloud computing framework that includes an automated SLA negotiation mechanism and a workload- and location-aware resource allocation scheme (WLARA), and (2) the implementation of an agent-based cloud testbed of the proposed framework. Using the testbed, experiments were conducted to compare the proposed schemes with related approaches. Empirical results show that the proposed WLARA performs better than other related approaches (e.g., round robin, greedy, and manual allocation) in terms of SLA violations and the provider’s profits. We also show that using the automated SLA negotiation mechanism supports providers in earning higher profits.     

面向容器的云平台数据重分布策略研究

随着Docker等的问世,基于容器的操作系统级虚拟化技术受到云计算厂商的广泛关注。针对云平台上不同应用领域的数据库容器(面向事务型任务的数据库容器与面向分析型任务的数据库容器)在运行时对宿主机资源需求的差异,提出一种面向容器的数据重分布策略,用于优化容器中数据库服务的性能。实验结果表明,该策略达到了预期效果,可以有效提升容器中数据库服务的性能。     

An adult image identification system employing image retrieval technique

As the development of the Internet, children are easily exposed on the pornography through web browsers. To block adult images, content-based image retrieval technique is employed for adult image identification. First, the background is removed to obtain the rectangular region of interesting based on the detection of skin-like pixels. For each input image, the MPEG-7’s color, texture, and the proposed shape feature is used to retrieve 100 most similar images from the image database which contains both adult and non-adult images. If the retrieved images contains more than T_ad adult images, the input one is identified as an adult image. Otherwise, it is identified as a non-adult image. Experiment results have shown the effectiveness of the proposed method.     

一种基于MAS的自适应图像分割方法

复杂图像的全自动分割是极具挑战性的问题．提出了一种基于MAS的自适应图像分割方法．通过属性和行为描述Agent个体，通过合作竞争描述了Agent间的交互．MAS系统在Agent反复自适应过程中达到平衡，同时完成图像分割．通过分割复杂的医学图像证实了该方法的有效性，MAS在图像分割领域具有应用价值．     

An adaptive resource management scheme in cloud computing

There are various significant issues in resource allocation, such as maximum computing performance and green computing, which have attracted researchers’ attention recently. Therefore, how to accomplish tasks with the lowest cost has become an important issue, especially considering the rate at which the resources on the Earth are being used. The goal of this research is to design a sub-optimal resource allocation system in a cloud computing environment. A prediction mechanism is realized by using support vector regressions (SVRs) to estimate the number of resource utilization according to the SLA of each process, and the resources are redistributed based on the current status of all virtual machines installed in physical machines. Notably, a resource dispatch mechanism using genetic algorithms (GAs) is proposed in this study to determine the reallocation of resources. The experimental results show that the proposed scheme achieves an effective configuration via reaching an agreement between the utilization of resources within physical machines monitored by a physical machine monitor and service level agreements (SLA) between virtual machines operators and a cloud services provider. In addition, our proposed mechanism can fully utilize hardware resources and maintain desirable performance in the cloud environment.     

An enhanced GA technique for system training and prognostics

The commonly used genetic algorithm (GA)-based methods have some shortcomings in applications such as time-consuming and slow convergence. A novel enhanced genetic algorithm (EGA) technique is developed in this paper to overcome these problems in classical GA methods so as to provide a more efficient technique for system training and optimization. Two approaches are proposed in the EGA technique: Firstly, a novel group-based branch crossover operator is suggested to thoroughly explore local space and speed up convergence. Secondly, an enhanced MPT (Makinen-Periaux-Toivanen) mutation operator is proposed to promote global search capability. The effectiveness of the developed EGA is verified by simulations based on a series of benchmark test problems. The EGA technique is also implemented to train a neural-fuzzy predictor for real-time gear system monitoring. Test results show that the branch crossover operator and enhanced MPT mutation operator can effectively improve the convergence speed and global search capability. The EGA technique outperforms other related GA methods with respect to convergence speed and global search capability.     

Virtual machine power measuring technique with bounded error in cloud environments

In virtualized datacenters, accurately measuring the power consumption of virtual machines (VMs) is the prerequisite to achieve the goal of fine-grained power management. However, existing VM power models can only provide power measurements with empirical accuracy and unbounded error. In this paper, we firstly formulize the co-relation between utilization and accuracy of power model, and compare two classes of VM power models; then we propose a novel VM power model which is based on a conception named relative performance monitoring counter (PMC); finally, based on the relative PMC power model, we propose a novel VM scheduling algorithm which uses the information of relative PMC to compensate the recursive power consumption. Theoretical analysis indicates that the proposed algorithm can provide bounded error when measuring per-VM power consumption. Extensive experiments are conducted by using various benchmarks on different platforms, and the results show that the error of per-VM power measurement can be significantly reduced. In addition, the proposed algorithm is effective to improve the power efficiency of a server when its virtualization ratio is high.