网格环境中资源发现机制的研究

随着高性能应用需求的迅猛发展,单台高性能计算机已经不能胜任一些超大规模应用问题的解决。这就需要将地理上分布、系统异构的多种计算资源通过高速网络连接起来,共同解决大型应用问题,从而产生了网格（Grid）。但是在网格这种共享资源的集合中,由于资源共享的广泛性,资源的异构性等等都使得资源发现更具有挑战性。目前存在的一些分布式环境中的资源发现大多是基于集中获分布的,由于集中式资源发现不能很好地满足系统的扩充,而分布式资源发现又会产生大量的网络通讯,因此这两种机制将不可避免地结合在一起,即形成分层式资源发现机制。     

网格环境下分布式仿真应用的资源调度*

针对网格环境中典型的交互密集型应用——分布式仿真问题,综合考虑具体应用的计算量及通信需求情况,结合网络资源的限制约束,提出了实现资源节点优化选择的过程框架,给出了节点优化选择的问题模型和求解算法。仿真实验表明所提出的优化策略能够显著提高分布式仿真应用执行的效率。     

云制造环境下基于功能需求的资源发现方法

云制造环境中存在大量功能相同或相似的资源,使得用户很难获得符合需求的资源。针对这个关键问题,对云制造环境下基于功能需求的资源发现进行了研究,提出了一种云制造资源发现框架。首先对云制造资源进行形式化描述,并分别计算语义相似性,然后进行加权相似度计算,最后采用聚类技术,从功能相似角度对资源进行聚类预处理,从而降低资源查找空间,提高资源发现效率。通过实验验证了所提方法的有效性。     

网格环境中基于二叉树模型的资源监控方法

针对虚拟共享平台（VSP）对资源监控和用户任务计费的需求,设计实现了VSP的网格资源监控模块VSPMonitor。VSPMonitor中资源消耗的计算采用二叉树对系统进程树进行存储。通过对二叉树的非递归遍历统计出用户任务的资源消耗量。实验表明这种基于二叉树模型的方法可以高效计算出用户任务的资源消耗量,满足VSP对资源监控和任务计费的需求。     

网格计算经济中的资源绑定

针对网格计算经济,提出资源绑定的模型,并结合任务对资源的需求和QoS要求,就绑定资源的描述、价格、资源绑定的优化,绑定流程等进行了深入研究.在此基础上,实现了基于绑定的调度算法.实验结果证明,资源绑定在任务完成时间和成本支出上取得了良好的效果,促进了资源调度,有利于提高整个网格资源管理系统的效率.     

面向云平台的资源分配策略研究

资源分配策略的研究一直是云计算领域研究的热点和难点。针对当前已有的资源分配策略进行了深入调研和分析,首先简要分析了云平台资源分配存在的挑战;然后给出了云平台资源分配的形式化描述;在此基础上,从启发式分配算法、基于经济理论的策略,以及其他分配策略三个方面对当前主流的资源分配策略进行描述,分析其优缺点;最后结合具体指标对主流策略进行了综合比较,并指出未来的研究方向。     

Energy-efficient sender-initiated threshold-based load balancing (e-STLB) in cloud computing environment

Cloud computing has transformed service delivery through its pay-per-use model, supporting diverse users with multiple heterogeneous Virtual Machines (VMs). However, energy consumption has emerged as a critical concern, necessitating cloud resource optimization for environment-friendly practices. This research paper presents an innovative energy-efficient threshold-based sender-initiated load-balancing strategy (e-STLB) to address this concern. The approach employs threshold values to trigger task migration between VMs, ensuring optimal performance while maximizing energy efficiency. The proposed strategy significantly reduces Makespan and increases Resource Utilization in an energy-conscious manner. Experimental evaluations using Cloudsim 3.0 demonstrate that the e-STLB outperforms other state-of-the-art solutions, offering a compelling approach to sustainable cloud computing.     

De-identification of metering data for smart grid personal security in intelligent CCTV-based P2P cloud computing environment

Various security threats exist in the smart grid environment due to the fact that information and communication technology are grafted onto an existing power grid. In particular, smart metering data exposes a variety of information such as users’ life patterns and devices in use, and thereby serious infringement on personal information may occur. Therefore, we are in a situation where a de-identification algorithm suitable for metering data is required. Hence, this paper proposes a new de-identification method for metering data. The proposed method processes time information and numerical information as de-identification data, respectively, so that pattern information cannot be analyzed by the data. In addition, such a method has an advantage that a query such as a direct range search and aggregation processing in a database can be performed even in a de-identified state for statistical processing and availability.     

网格计算经济中资源的可靠性控制

针对网格资源的可靠性问题,提出了一个包括注册真实性,信任评价和调度契约构成的分层控制模型.针对该模型,提出了资源注册信息的验证方法和契约的模式.仿真实验表明,该模型在资源注册信息验证的基础上,优先调度了信息真实、运行可靠的资源,通过可靠性资源的选择和控制促进了计算经济的可靠性.     

一种应用于网格计算环境的任务调度模式*

提出了一种新的网格任务调度模式,针对网格计算资源有组织、松耦合、自治等特性,建立基于多层次虚拟组织形式的计算资源模型;根据网格环境中应用任务粗粒度、特定资源依赖等特点,建立了网格任务的描述模型;提出并实现了相应的子任务生成算法、任务初始调度算法及自动调整算法。设计实现了能够支持仿真及实际网格计算环境可扩展网格任务调度器,通过理论分析和仿真实验对算法的正确性、效果和效率进行了评价。     

Resource scheduling approach for multimedia cloud content management

With the development of multimedia application and services, the multimedia technology has already permeated each aspect of our life. Multimedia cloud is used for processing multimedia services. However due to huge data volume, high concurrency, strict real-time, resource scheduling for content dissemination in multimedia cloud still remain challenges. In order to increase the user satisfaction and decrease completion time of content dissemination, the resource scheduling for content dissemination in multimedia cloud is proposed in this paper. The multimedia jobs are clustered according to user expectation and job complexity. The job with highest priority will be executed first. Moreover, considered multimedia task types and the impact of stragglers, the multimedia task scheduling based on task types and node workload is presented, which is a time-efficient scheduling approach. The experiments are conducted and the experiment results show that the job clustering algorithm-based user expectation and job complexity in multimedia cloud has better user satisfaction and shorter completion time, while the multimedia task scheduling based on task types and node workload can reduce completion time and achieve load-balancing.     

An integrated framework for managing sensor data uncertainty using cloud computing

In recent years, an increasing number of data-intensive applications deal with continuously changing data objects (CCDOs), such as data streams from sensors and tracking devices. In these applications, the underlying data management system must support new types of spatiotemporal queries that refer to the spatiotemporal trajectories of the CCDOs. In contrast to traditional data objects, CCDOs have continuously changing attributes. Therefore, the spatiotemporal relation between any two CCDOs can change over time. This problem can be more complicated, since the CCDO trajectories are associated with a degree of uncertainty at every point in time. This is due to the fact that databases can only be discretely updated. The paper formally presents a comprehensive framework for managing CCDOs with insights into the spatiotemporal uncertainty problem and presents an original parallel-processing solution for efficiently managing the uncertainty using the map-reduce platform of cloud computing.     

Network flow analysis for detection and mitigation of Fraudulent Resource Consumption (FRC) attacks in multimedia cloud computing

Multimedia Tools and Applications - Multimedia computing has evolved as a remarkable technology which provides services to view, create, edit, process, and search multimedia contents. All these...     

面向云环境的Flink负载均衡策略

作为新一代的大数据计算引擎,Flink得到了广泛应用。Flink在云环境下进行容器化部署时,其默认任务调度算法不能感知节点的资源信息,导致即时调整负载和自主均衡能力较差,而主流的容器编排工具虽然提供了管理容器的可能性,却也未能结合Flink特点解决平衡资源利用的同时降低容器组内的通信开销问题。针对以上问题开展研究,提出了一种面向云环境的Flink负载均衡策略FLBS,综合考虑了Flink集群中算子的分布特点和容器间通信机制,以节点间通信开销和均衡负载作为评估标准。实验结果表明,与Flink默认调度策略相比,FLBS能够有效提高计算效率,提升系统性能。     

Data discovery algorithm for scientific data grid environment

In modern scientific computing communities, scientists are involved in managing massive amounts of very large data collections in a geographically distributed environment. Research in the area of grid computing has given us various ideas and solutions to address these requirements. Data grid mostly deals with large computational problems and provides geographically distributed resources for large-scale data-intensive applications that generate large data sets. Peer-to-peer (P2P) networks have also become a major research topic over the last few years. In a distributed P2P system, a discovery algorithm is required to locate specific information, applications, or users within the system. In this research work, we present our scientific data grid as a large P2P-based distributed system model. By using this model, we study various discovery algorithms for locating data sets in a data grid system. The algorithms we studied are based on the P2P architecture. We investigate these algorithms using our Grid Simulator developed using PARSEC. In this paper, we illustrate our scientific data grid model and our Grid Simulator. We then analyze the performance of the discovery algorithms relative to their average number of hop, success rates and bandwidth consumption.     

制造网格资源调度及安全性管理

根据制造网格发展的现状和方向,分析了网格资源调度的主要方法,并对Globus环境下的GSI体系结构做了深入研究,分析讨论了其任务提交执行过程和架构特点,对现有的GSI架构基础做了相关改进,构建了一个满足制造网格下部分特性的安全体系结构M-GSI,实现了实时认证和强授权功能,满足了制造网格部分安全需求。     

云环境下基于运算电路的同态认证方案

针对云服务器上数据验证效率低的问题,为能够在正确执行用户指令的情况下依然保持对数据的高效验证,构造了一种支持云环境下基于运算电路的同态认证方案。首先,利用标签生成算法对验证标签进行多项式表示;其次,调用转化算法对验证标签进行转化以达到满足同态验证的形式,同时利用同态解密算法对验证标签的大小进行降维处理;最后,运用验证算法对检索结果进行验证。结果表明,所提方案能够支持任意次乘法同态而不会增加验证标签维数,克服了验证标签增长缺陷,提高了验证效率,但其计算复杂度会随着增强电路输入位的增加而增加。     

An enhanced secure content de-duplication identification and prevention (ESCDIP) algorithm in cloud environment

Neural Computing and Applications - In cloud computing, de-duplication plays an essential role in detecting the de-duplication of encoded data with minimal computation and cost. De-duplication...     

云制造环境下可信第三方的非常规访问方式

为确保云制造环境下资源提供方利益,满足其在异常情况下实时纠错的需求,提出一种非常规动态访问方式。从服务能力、运营能力、服务成效方面建立评估价值体系,进行第三方可信监管平台选择。给出访问控制总体结构,引入时空属性、自由度、先决条件依赖、生命活动时间等概念作为授权约束,在不涉及服务方商业隐私前提下,允许第三方可信监管平台在生命活动时间内拥有对能力状态异常的资源提供方进行实时纠错的权限,为服务使用者提供更人性的服务。以汽车零配件生产为例,阐述应用该访问方式进行实时纠错的步骤,实验结果表明了该方法的可行性。