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。     

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.     

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.     

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...     

A novel cloud management framework for trust establishment and evaluation in a federated cloud environment

The Journal of Supercomputing - Cloud Computing is being utilized by large-scale organizations for data storage and management. It provides advantages like reducing the cost of information...     

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。     

面向云计算环境的OpenFOAM网格生成服务

网格生成是计算流体力学仿真分析基础.由于网格生成的计算量大,适合利用服务器的高性能计算能力在服务器端运行,以云服务方式为用户提供高效的网格生成服务.提出了 一种面向云计算的网格生成服务模型,基于该模型能够对传统的单机版网格生成模块进行服务化封装,实现网格生成的云服务.采用该模型对OpenFOAM的网格生成模块进行服务化...     

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。     

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...     

Incremental proxy re-encryption scheme for mobile cloud computing environment

Due to the limited computational capability of mobile devices, the research organization and academia are working on computationally secure schemes that have capability for offloading the computational intensive data access operations on the cloud/trusted entity for execution. Most of the existing security schemes, such as proxy re-encryption, manager-based re-encryption, and cloud-based re-encryption, are based on El-Gamal cryptosystem for offloading the computational intensive data access operation on the cloud/trusted entity. However, the resource hungry pairing-based cryptographic operations, such as encryption and decryption, are executed using the limited computational power of mobile device. Similarly, if the data owner wants to modify the encrypted file uploaded on the cloud storage, after modification the data owner must encrypt and upload the entire file on the cloud storage without considering the altered portion(s) of the file. In this paper, we have proposed an incremental version of proxy re-encryption scheme for improving the file modification operation and compared with the original version of the proxy re-encryption scheme on the basis of turnaround time, energy consumption, CPU utilization, and memory consumption while executing the security operations on mobile device. The incremental version of proxy re-encryption scheme shows significant improvement in results while performing file modification operations using limited processing capability of mobile devices.     

Resource virtualization and service selection in cloud logistics

Cloud computing and Internet of Things have promoted a new logistics service mode, i.e., the cloud logistics mode. This work studies the resource virtualization and service encapsulation of a logistics center, and focuses on the technologies of resource expression and service encapsulation. After the resources of a logistics center are encapsulated in web services, how to find the “best” concrete web service among many is a critically important issue. This work considers service selection as an optimization problem and establishes a Particle Swarm Optimization (PSO)-based web service selection model with quality of service (QoS) constraints. It can be used to address the horizontal adaptation issues from the composite web services. The feasibility and effectiveness of the model are verified by several experiments.     

Role-Based Access Control in a Data Grid Using the Storage Resource Broker and Shibboleth

In this paper, we propose a role-based access control (RBAC) system for data resources in the Storage Resource Broker (SRB). The SRB is a Data Grid management system, which can integrate heterogeneous data resources of virtual organizations (VOs). The SRB stores the access control information of individual users in the Metadata Catalog (MCAT) database. However, because of the specific MCAT schema structure, this information can only be used by the SRB applications. If VOs also have many non-SRB applications, each with its own storage format for user access control information, it creates a scalability problem with regard to administration. To solve this problem, we developed a RBAC system with Shibboleth, which is an attribute authorization service currently being used in many Grid environments. Thus, the administration overhead is reduced because the role privileges of individual users are now managed by Shibboleth, not by MCAT or applications. In addition, access control policies need to be specified and managed across multiple VOs. For the specification of access control policies, we used the Core and Hierarchical RBAC profile of the eXtensible Access Control Markup Language (XACML); and for distributed administration of those policies, we used the Object, Metadata and Artifacts Registry (OMAR). OMAR is based on the e-business eXtensible Markup Language (ebXML) registry specifications developed to achieve interoperable registries and repositories. Our RBAC system provides scalable and fine-grain access control and allows privacy protection. Performance analysis shows that our system adds only a small overhead to the existing security infrastructure of the SRB.     

Resource virtualization methodology for on-demand allocation in cloud computing systems

The resources’ heterogeneity and unbalanced capability, together with the diversity of resource requirements in cloud computing systems, have produced great contradictions between resources’ tight coupling characteristics and user’s multi-granularities requirements. We propose a resource virtualization model and its on-demand allocation oriented infrastructure mainly providing computing services to solve that problem. A loosely coupled resource environment centered on resource users is created to complete a mapping from physical view of resources to logic view of resources. Heuristic resource combination algorithm (HRCA) is proposed to transform physical resources to logic resources, which meets two requirements: randomness in combination and fluctuation control to the size of resources granularities. On the basis of the appraisal indexes presented for the on-demand allocation, resource matching algorithm (RMA), targeting at resource satisfaction with the highest resource utilization, is designed to reuse resources. RMA can satisfy users’ requirement in limited time and keep resource satisfaction in the highest level in the condition of logic resources granularities being less than their required size. Resource reconfiguration algorithm (RRA) is presented to implement resource matching in the condition that virtual computing resource pool cannot match granularities of resource requirements. RRA assures the lowest resource refusal rate and the greatest resource satisfaction. We verify the effectiveness, performance and accuracy of algorithms in implementing the goal of resource virtualization centered on resource users and on-demand allocation.