云计算中相似驱动的并行任务划分方法

云计算是并行计算、分布式计算和网格计算等高性能计算的进一步发展,它的异构性、按需等特征对高性能计算提出了新的挑战。针对云计算的典型特征,提出了基于并行任务和云环境相似驱动的任务划分方法。首先用图刻画了并行任务和云环境,建立了图的相似关系及其相似度计算方法;其次给出云计算中拟解决的问题,通过图局部相似和全局相似度偏差最小来实现并行任务和体系结构的异构匹配及按需要求;接着利用F度标号方法给出相似驱动的任务划分算法;最后通过实验和其他划分方法进行比较,阐明了该方法的优点。     

一种用于异构环境中并行任务调度的弹性节能算法

节能调度算法设计是高性能计算领域中的一个研究热点。本文通过软件方法设计异构多核计算机的调度算法,实现系统的弹性节能,达到降低能耗并提升系统性能的目的。本文的调度策略建立在基于处理器异构的并行任务调度的环境中,构建了节能模型,提出了EAPS（Energy-aware parallel scheduling）算法模型,该算法在每一任务完成之后重新计算优先级以使优先级符合任务的实时情况,并对复制的前驱任务是否冗余任务进行判断从而避免资源的浪费,并通过调节节点电压选择能耗最少的节点进行调度,在节能与期望完成时间之间取得平衡。     

基于云计算的大规模性能测试服务平台

性能测试是通过自动化的测试工具模拟多种正常、峰值以及异常负载条件来对系统的各项性能指标进行测试。测试对象分为基准测试和非基准测试两种。大规模的性能测试受到所需的大量软硬件资源以及与此规模匹配的管理维护代价的限制,传统的性能测试采用一种1:20的微缩仿真模拟,但这种微缩仿真测试不充分,会带来严重的后果。采用云计算技术,使用其承诺的按需的廉价软硬件资源服务,来构建大规模性能测试服务平台,提供按需定制的测试服务。     

面向安全风险的云计算测试技术研究

为了降低云计算的安全风险,保证云计算的安全、正常、可靠的运行,以面向云计算测试技术为主要的研究对象,首先对云计算的定义、特征、类型以及其发展现状进行简要的介绍,其次对云计算的安全风险问题进行了分析和讨论,最后对云计算的安全测试技术和其解决的方案进行探讨,给出了相关的解决方案。     

基于云计算的移动测试教学

介绍云计算的概念、特点和服务,说明云测试的概念以及移动测试在教学实践中的问题和挑战,提出基于云计算移动测试的概念、方法和工具,阐述在现有移动云测试平台上教学实践以及基于云计算的移动测试平台基础架构。     

基于云计算的智能测试保障体系构建

针对当前武器装备系统复杂以及测试保障难度大的问题,提出基于云计算的装备智能测试保障体系。该体系以云化智能测控网及运行其上的人工智能算法组成的云计算平台为基础,结合装备的自主保障流程,设计一种基于认知的智能测试与保障方法,实现装备的智能测试与自主保障。基于认知的智能测试以装备的测试性设计为前提,包含机内测试和机外测试两种方式,其运行原理相同,通过云化智能测控网,依托云计算,对获得的历史数据和实时数据进行自组织学习,实时调整装备测试机制、数据传输机制和数据分析与故障预测机制,为实现装备的自主保障提供智能决策支持。     

多通道压力测试系统Petri网建模及其性能分析

建立了n路多通道压力测试系统的广义随机Petri网(GSPN)模型,利用模型的可达图和MC链分析了GSPN模型的基本性能;推导出了压力测试系统的测试性能的数学公式,测试性能包括各部分的利用率、单位时间内的激发次数、系统时间延迟;并且分析了测试系统各部分之间的相互影响。经过实验验证,证明了该模型的正确性。     

基于云计算的并行测试方案设计与实现

目前对软件测试用例的需求在以指数级增长,导致测试资源相对不足、测试成本高、测试用例执行效率低等问题更加突出。为解决上述问题,设计一个基于云计算的并行测试方案,采用有限状态机定义测试对象及测试过程中的状态迁移,借鉴随机路线的思想,提出一个并行测试用例生成算法,在此基础上给出基于MapReduce模型和云计算平台的并行测试脚本。实验结果表明,与顺序执行测试序列相比,该方案的加速比可达20,测试效率有明显提高。     

密度泛函微扰理论中响应密度矩阵的迭代求解算法研究

针对密度泛函微扰理论中响应密度矩阵的计算问题,提出了一种全新的Sternheimer方程的并行求解方法,即通过共轭梯度算法和矩阵直接分解算法对Sternheimer方程进行求解,并且在第一性原理的分子模拟软件FHI-aims中实现了这两种算法。实验结果表明采用共轭梯度算法和矩阵直接分解算法的计算结果精度较高,相比传统方法的计算结果误差较小,且具有可扩展性,验证了新的Sternheimer方程中线性方程求解的正确性和有效性。     

云计算环境下高复杂度动态数据的增量密度快速聚类算法研究

针对传统的聚类算法存在开销大、聚类质量差、聚类速度慢等问题,提出一种新的云计算环境下高复杂度动态数据的增量密度快速聚类算法。首先,依据密度对云计算环境下高复杂度动态数据进行聚类,从数据空间中找到部分子空间,使得数据映射至该空间后可产生高密度点集区域,将连通区域的集合看作聚类结果;其次,通过DBSCAN算法进行增量聚类,并对插入或删除数据导致的原聚类合并或分裂进行研究;最后,在更新的过程中通过改变核心状态数据的邻域中含有的全部核心数据进行处理,从插入或删除数据两方面进行增量聚类分析。实验结果表明,所提算法开销低、聚类速度快、聚类质量高。     

云计算环境中面向DAG任务的多目标调度算法

为了实现任务执行效率与执行代价的同步优化,提出了一种云计算环境中的DAG任务多目标调度优化算法。算法将多目标最优化问题以满足Pareto最优的均衡最优解集合的形式进行建模,以启发式方式对模型进行求解;同时,为了衡量多目标均衡解的质量,设计了基于hypervolume方法的评估机制,从而可以得到相互冲突目标间的均衡调度解。通过配置云环境与三种人工合成工作流和两种现实科学工作流的仿真实验测试,结果表明,比较同类单目标算法和多目标启发式算法,算法不仅求解质量更高,而且解的均衡度更好,更加符合现实云的资源使用特征与工作流调度模式。     

Implementation and performance evaluation of a scheduling algorithm for divisible load parallel applications in a cloud computing environment

Cloud computing is an emerging technology in which information technology resources are virtualized to users in a set of computing resources on a pay‐per‐use basis. It is seen as an effective infrastructure for high performance applications. Divisible load applications occur in many scientific and engineering applications. However, dividing an application and deploying it in a cloud computing environment face challenges to obtain an optimal performance due to the overheads introduced by the cloud virtualization and the supporting cloud middleware. Therefore, we provide results of series of extensive experiments in scheduling divisible load application in a Cloud environment to decrease the overall application execution time considering the cloud networking and computing capacities presented to the application's user. We experiment with real applications within the Amazon cloud computing environment. Our extensive experiments analyze the reasons of the discrepancies between a theoretical model and the reality and propose adequate solutions. These discrepancies are due to three factors: the network behavior, the application behavior and the cloud computing virtualization. Our results show that applying the algorithm result in a maximum ratio of 1.41 of the measured normalized makespan versus the ideal makespan for application in which the communication to computation ratio is big. They show that the algorithm is effective for those applications in a heterogeneous setting reaching a ratio of 1.28 for large data sets. For application following the ensemble clustering model in which the computation to communication ratio is big and variable, we obtained a maximum ratio of 4.7 for large data set and a ratio of 2.11 for small data set. Applying the algorithm also results in an important speedup. These results are revealing for the type of applications we consider under experiments. The experiments also reveal the impact of the choice of the platforms provided by Amazon on the performance of the applications under study. Considering the emergence of cloud computing for high performance applications, the results in this paper can be widely adopted by cloud computing developers. Copyright © 2014 John Wiley & Sons, Ltd.     

Honey bee behavior inspired load balancing of tasks in cloud computing environments

Scheduling of tasks in cloud computing is an NP-hard optimization problem. Load balancing of non-preemptive independent tasks on virtual machines (VMs) is an important aspect of task scheduling in clouds. Whenever certain VMs are overloaded and remaining VMs are under loaded with tasks for processing, the load has to be balanced to achieve optimal machine utilization. In this paper, we propose an algorithm named honey bee behavior inspired load balancing (HBB-LB), which aims to achieve well balanced load across virtual machines for maximizing the throughput. The proposed algorithm also balances the priorities of tasks on the machines in such a way that the amount of waiting time of the tasks in the queue is minimal. We have compared the proposed algorithm with existing load balancing and scheduling algorithms. The experimental results show that the algorithm is effective when compared with existing algorithms. Our approach illustrates that there is a significant improvement in average execution time and reduction in waiting time of tasks on queue.     

SLA-RALBA: cost-efficient and resource-aware load balancing algorithm for cloud computing

The Journal of Supercomputing - Recently, Service-level agreement (SLA) is deemed to be an integral aspect for on-demand provisioning of scalable resources on Cloud. SLA defines important...     

云计算环境下基于MapReduce并行的Apriori算法优化研究

根据MapReduce模型并行运行实现的特点,针对可扩展性差的传统Apriori的特点和传统Apriori算法,采用了＂云＂强大的廉价计算处理方式和关联规则挖掘算法,改进提高Apriori算法的运算效率。通过改进在云计算环境下MapReduce编程框架,并且结合验证MR-Apriori算法的实验为基础,这对传统意义上的Apriori算法在数据挖掘过程中所出现的客观问题进行处理,从而真正意义上的完成了本文研究的基于MapReduce并行的Apriori算法的扩展性提升的目标,并且表明了元计算技术结合关联规则挖掘算法的可能性。     

改进鲸鱼算法在云计算资源负载预测中的应用

为了解决传统云计算资源负载预测方法对负载序列高频分量预测精度不高和泛化能力弱的缺点,提出一种混合小波包变换和正余混沌双弦鲸鱼优化（CSCWOA）算法优化多层感知器神经网络（MLP）的短期云计算资源负载预测方法。通过小波包变换对负载序列进行多频段预处理分解,然后采用CSCWOA算法优化的MLP神经网络,对单支重构所得的负载子序列进行预测;最后叠加各子序列的预测值来获取实际预测结果。实验结果表明,该方法能掌握负载序列各频段冲击毛刺的变化规律,具有较好的预测精度和泛化能力。     

Load balancing of IoT tasks in the cloud computing by using sparrow search algorithm

IoT is one of the most important technologies that are used over the past few years, where everything is connected to the Internet; it is used in many fields; one of these fields is healthcare system that includes mobile health and remote patient monitoring (patients with kidney, heart disease, cancer, blood pressure, diabetes, respiratory disease and stroke). Integration of IoT and cloud computing can improve the performance of healthcare system and the development of the innovative applications in future. One of the major problems that cannot be ignored in cloud computing system is load balancing. Solving that problem leads to reduce the response time, power consumption, cost and increase server availability. This paper is comprised of two parts which are creating and implementing healthcare system by using IoT, and solving the problem of load balancing of the cloud computing by using intelligent algorithm called sparrow search algorithm (SSA). The SSA is used to select the best virtual machine (VM) among a group of VMs depending on the its fitness value; also many and varied tasks are scheduled with priority and assign to the best VMs depending on the its instruction millions (IM), where the task that has high IM is assigned to the best VM that has high fitness value. The outcomes demonstrated that the proposed method focuses to reduce the latency and packet loss while maximizing the throughput in healthcare systems; also the SSA has proved its robustness, efficiency and success when compared to other methods in terms of reducing makespan time, total processing time and provides load balancing among VMs, where the value of makespan time, processing time and degree of imbalance has decreased into (23.05), (899.8979) and (177.7675), respectively, in case of applying 500 tasks.

     

A self-routing load balancing algorithm in parallel computing: comparison to the central algorithm

Load balancing is an important stage of a system using parallel computing where the aim is the balance of workload among all processors of the system. In this paper, we introduce a new load balancing algorithm with new capabilities for parallel systems, among which is the independence of a separate route-finder algorithm between the load receiver and sender nodes. In addition to simulation of the new algorithm, due to similarity in behavior to the proposed algorithm, the central algorithm is simulated. Simulation results show that, the system performance increases with the increase of the degree of neighborhood between the processors. These results also indicate the algorithm’s high compatibility with environment changes.