A uniform approach for programming distributed heterogeneous computing systems

Large-scale compute clusters of heterogeneous nodes equipped with multi-core CPUs and GPUs are getting increasingly popular in the scientific community. However, such systems require a combination of different programming paradigms making application development very challenging.In this article we introduce libWater, a library-based extension of the OpenCL programming model that simplifies the development of heterogeneous distributed applications. libWater consists of a simple interface, which is a transparent abstraction of the underlying distributed architecture, offering advanced features such as inter-context and inter-node device synchronization. It provides a runtime system which tracks dependency information enforced by event synchronization to dynamically build a DAG of commands, on which we automatically apply two optimizations: collective communication pattern detection and device-host-device copy removal.We assess libWater’s performance in three compute clusters available from the Vienna Scientific Cluster, the Barcelona Supercomputing Center and the University of Innsbruck, demonstrating improved performance and scaling with different test applications and configurations.     

Self-scaling cooperative discovery of service compositions in unstructured P2P networks

We propose an efficient technique for improving the performance of automatic and cooperative compositions in unstructured Peer-to-Peer networks during service discovery. The technique exploits a probabilistic forwarding algorithm that uses different sources of knowledge, such as network density and service grouping, to reduce the amount of messages exchanged in the network. The technique, analysed in several network configurations by using a simulator to observe resolution time, recall and message overhead, presents good performance especially in dense and large-scale service networks.     

Cloud based intelligent system for delivering health care as a service

The promising potential of cloud computing and its convergence with technologies such as mobile computing, wireless networks, sensor technologies allows for creation and delivery of newer type of cloud services. In this paper, we advocate the use of cloud computing for the creation and management of cloud based health care services. As a representative case study, we design a Cloud Based Intelligent Health Care Service (CBIHCS) that performs real time monitoring of user health data for diagnosis of chronic illness such as diabetes. Advance body sensor components are utilized to gather user specific health data and store in cloud based storage repositories for subsequent analysis and classification. In addition, infrastructure level mechanisms are proposed to provide dynamic resource elasticity for CBIHCS. Experimental results demonstrate that classification accuracy of 92.59% is achieved with our prototype system and the predicted patterns of CPU usage offer better opportunities for adaptive resource elasticity.     

Hybrid shuffled frog leaping algorithm for energy-efficient dynamic consolidation of virtual machines in cloud data centers

Cloud computing aims to provide dynamic leasing of server capabilities as scalable virtualized services to end users. However, data centers hosting cloud applications consume vast amounts of electrical energy, thereby contributing to high operational costs and carbon footprints. Green cloud computing solutions that can not only minimize the operational costs but also reduce the environmental impact are necessary. This study focuses on the Infrastructure as a Service model, where custom virtual machines (VMs) are launched in appropriate servers available in a data center. A complete data center resource management scheme is presented in this paper. The scheme can not only ensure user quality of service (through service level agreements) but can also achieve maximum energy saving and green computing goals. Considering that the data center host is usually tens of thousands in size and that using an exact algorithm to solve the resource allocation problem is difficult, the modified shuffled frog leaping algorithm and improved extremal optimization are employed in this study to solve the dynamic allocation problem of VMs. Experimental results demonstrate that the proposed resource management scheme exhibits excellent performance in green cloud computing.     

基于安全性能接口的调度和管理模型研究

以云计算效能提高为目的, 以研究Job调度和资源管理两大决定云计算环境能效的最重要因素为基础, 使用调度和管理对象对作为系统统计信息的参考指标进行采集的方式展开研究。性能代理和服务接口模式(PASI)已经从云环境中采集了原始数据, 通常情况下应用于云的安全技术属于被动防御模式, 如实时动态监控或者深度包检测模式(DPI), 其主要目的是实现动态检测和防御。提出了一个有效的框架以提高云计算中心的综合性能指标, 该架构的建立是基于PASI与DPI相结合的方式, 并且提出了一个基于安全PASI的Job调度和资源管理模型(JDRMSP)。实验结果表明, 该模型有效地提升了云环境的整体运作效能。     

基于云计算的预拌混凝土行业在线质量管理

以实现质量的科学化管理和实时的动态监测为目的,提出了预拌混凝土行业基于云计算的在线质量管理。通过分析预拌混凝土企业包括经营数据和生产实时数据等大量数据的存储、整合、分析、挖掘、利用等方面需求,以云计算为平台,运用人工智能（ AI）相关算法,实现了基于实际生产过程的质量预测,为解决影响混凝土强度及工作性能的多组分因素问题提供了思路和方法。提取云平台上的生产实时数据进行模拟实验,以C35的28天抗压强度为例,用1000组数据对网络进行训练,用100组数据对已训练的网络进行检验,结果证明了预测值的准确性,同时也验证了平台的可行性。     

云数据中心心跳检测与故障评估

为提高云数据中心的抗毁能力,针对软硬件日趋复杂化带来的诸多故障问题,以传统的服务器心跳检测机制为基础,结合云计算的特点,提出一种保证云数据中心有效运行的心跳检测与故障评估方案。在实验室搭建仿真平台进行实验,实验结果表明,该方案能够及时检测到节点故障,依据预先制定的故障等级评估标准,采用模糊综合法进行评估,为云数据中心的管理与运作提供参考依据与保障。     

一种基于点集拓扑分形变幻的云计算登录认证方法

考虑到云服务登录安全性及便捷性的需求,针对单点登录认证模式,提出云计算的统一登录认证方法,并采用点集拓扑分形变幻方法生成随机密钥,提高登录认证的安全等级。首先对云计算登录认证系统结构进行分析,然后以网络公共数据库中的指纹图像为例,进行分形变幻环运算,生成伪随机序列,再将这些序列加密并通过UDP发送给服务器端进行认证,最后进行实例仿真。     

基于GPGPU的实体表面实时提取

为实时提取三维实体表面,提出一种基于GPGPU并行计算的实体表面实时提取方法。在分析深度剥离算法原理和GPU图形绘制管线的基础上,给出在GPU上利用深度剥离算法实现实时提取三维实体表面的算法;通过OpenGL的高级着色语言GLSL控制GPU的图形绘制管线实现了该算法,给出其伪代码。以龙、叶轮和刀具扫描体的模型为应用实例验证了该算法效果良好,特别是对于刀具扫描体表面的提取,可满足实时性要求。     

云计算环境中高效可扩展的元数据管理方法

针对现有元数据管理方法存在可扩展性差或访问效率低的问题,提出一种高效可扩展的元数据管理方法.基于可扩展哈希方法实现元数据系统动态可扩展,使用并行定位方法实现元数据服务器高效定位,提出动态K叉树的元数据组织方法以提高元数据服务器扩展时选择迁移元数据的速度.实验结果表明,该方法具有近似线性扩展性能,其选择迁移元数据的时间几乎为零,有效解决了云计算环境中元数据管理系统的高效动态可扩展问题.