一种基于双边拍卖的复制优化策略

提出了基于连续双边拍卖协议的复制优化策略，解决数据网格中复制优化策略难以适应数据高度自治性和动态性的问题。首先给出了双边拍卖机制的实现模型，接着给出了分布式的连续双边拍卖协议算法和基于零智慧增强学习的投标策略，最后使用Optorsim对模型和复制优化策略进行仿真。实验结果表明，该复制优化策略与基于单边拍卖的优化策略相比，具有较低的任务运行时间和网络有效占用率,较好的存储和计算资源利用率。     

信息网格中基于Agent的数据复制方法探讨

说明了有效的数据复制机制和副本管理策略是信息网格研究的重要内容.概述了应用Agent技术实现信息网格中数据复制的基本方法.探讨了在全国人口信息系统中基于Agent的数据复制方法的设计和实现,利用Agent的自主性、移动性和交互性等特点,实现上层创建下层副本,及同层间副本的创建.采用数据格式转换系统解决底层数据库的异构问题.重点分析了基于Agent的推动和拉动复制方法的实现特点,说明了基于Agent的数据复制方法在信息网格中的合理性和高效性.     

网格环境面向服务的Multi-Agent交互协议及协商模型

网格目的是为了实现地理上分布的异构资源和服务的共享.资源的分配、应用开发和模型使用是网格环境中的关键问题和复杂的任务.利用具有一定自主推理、自主决策能力的Agent以及由其组成的Multi-Agent系统(MAS)来模拟、优化、实施、控制网格资源的分配和管理为网格研究提供了一种有效的新方法.在基于MAS的网格系统中资源分配和调度活动都是通过协商完成的,因此如何保证Agent之间能够有效、有序地进行协商的协商机制是网格计算环境的重点.针对目前对协商的研究还只是单纯地研究协商协议或者只重视研究协商策略,缺乏对网格环境下协商模型的研究背景下,提出了一个医学图像处理网格环境下的面向服务的多代理协商模型.     

一种基于市场机制的数据网格复制选择策略*

针对数据网格提出了一种基于市场机制的复制选择策略,以一般均衡理论为基础,依靠市场机制实现数据网格的复制选择策略。首先给出了数据网格复制选择的市场模型,其中效用函数用于刻画应用代理与数据所在站点之间进行数据传输的消耗;然后在此基础上根据供需规律提出了副本的自动生成策略以及副本的自动选择策略;最后对本策略对应的算法时间复杂度和空间复杂度进行了分析。     

一种基于权重的数据网格副本替换策略

针对数据网格中存储站点的容量限制,提出了一种基于权重的数据副本替换策略(WBRR).在网格模拟环境OptorSim上进行的模拟实验结果表明:基于权重的策略相比于传统的副本替换策略,在降低网络利用率的同时缩短了系统的响应时间,达到了提高系统性能的目的.     

基于遗传算法的副本管理策略

在数据网格环境中,为了提高数据的可靠性和降低用户对数据访问的延迟,广泛采用了数据复制技术;由于副本技术的引入,就会面临一个很直接的问题,即一个应用程序如何根据副本性能和访问特性,从一组副本中选择一个最佳副本,就是副本选择问题.针对数据复制技术中的关键技术:副本创建和副本选择,在价格机制模型和并行数据传输的基础上,采用了遗传算法实现副本的优化管理.最后使用网格模拟器OptorSim对算法进行测试分析,结果显示基于遗传算法的策略有更好的性能.     

基于移动Agent的DDM过滤机制

该文在分析基于区域和基于网格两种传统DDM过滤机制中区域匹配速度慢、不相关数据多等不足的基础上,提出了一种基于移动Agent的新的DDM过滤机制,并对移动Agent模型和管理Agent模型进行了分析和设计.     

基于QoS的网格计算经济模型

提出了基于经济模型的网格QoS控制机制，刻画了基于经济模型的网格QoS管理结构。用生产一消费模型给出基于多QoS指标的全局QoS优化模型，用整数规划中的背包问题形式化描述了以用户QoS效用函数为导向的资源分配问题。这种形式化描述将用户的QoS需求、资源稀缺以及服务成本等网格关键因素统一在一个框架中分析，提出用价格和契约来控制网格QoS的策略。     

网格数据复本管理的动态自适应软件体系结构

单一策略的复本管理不能适应网格系统复杂、多变的环境.提出了软件体系结构驱动的动态自适应数据复本管理架构DSA-RM.在该架构中,软件体系结构描述作为核心元素被整个数据网格运行环境共享,适用于不同应用环境和虚拟组织的数据复本管理策略则可以按照描述规则封装为构件.DSA-RM以该描述作为系统调度依据,运行各数据复本管理构件.通过对DSA-RM的抽象,形式化地描述了框架下的复本管理构件,讨论了动态环境下构件演化、复制过程的动态自适应规则,并按这些定义和规则设计了DSA-RM的实现框架和构件复制算法.通过性能分析以及对框架下多策略执行模拟验证表明,DSA-RM可以有效提升网格系统的整体性能.最后,介绍了下一步在上海医学数据网格上的应用计划.     

数据网格中的数据复制技术研究

数据复制技术是数据网格系统中广泛采用的改善网格系统性能的关键技术之一。与传统分布式系统应用领域中的复制相比，数据网格中的复制技术在复制目标、复制粒度、复制关键技术等方面表现出独特性质。本文将数据复制技术概括为副本创建、数据传输、副本删除、副本选择、副本一致性管理、安全管理等环节，深入分析、探讨了数据网格系统中数据复制的各项关键技术，为建立综合的数据网格复制策略和技术框架提供了全面的技术分析。最后，对数据复制技术下一步的研究方向作了分析和预测。     

Enhanced Dynamic Hierarchical Replication and Weighted Scheduling Strategy in Data Grid

The Data Grid provides massive aggregated computing resources and distributed storage space to deal with data-intensive applications. Due to the limitation of available resources in the grid as well as production of large volumes of data, efficient use of the Grid resources becomes an important challenge. Data replication is a key optimization technique for reducing access latency and managing large data by storing data in a wise manner. Effective scheduling in the Grid can reduce the amount of data transferred among nodes by submitting a job to a node where most of the requested data files are available. In this paper two strategies are proposed, first a novel job scheduling strategy called Weighted Scheduling Strategy (WSS) that uses hierarchical scheduling to reduce the search time for an appropriate computing node. It considers the number of jobs waiting in a queue, the location of the required data for the job and the computing capacity of the sites Second, a dynamic data replication strategy, called Enhanced Dynamic Hierarchical Replication (EDHR) that improves file access time. This strategy is an enhanced version of the Dynamic Hierarchical Replication strategy. It uses an economic model for file deletion when there is not enough space for the replica. The economic model is based on the future value of a data file. Best replica placement plays an important role for obtaining maximum benefit from replication as well as reducing storage cost and mean job execution time. So, it is considered in this paper. The proposed strategies are implemented by OptorSim, the European Data Grid simulator. Experiment results show that the proposed strategies achieve better performance by minimizing the data access time and avoiding unnecessary replication.     

A dynamic replica management strategy in data grid

Data Grid provides scalable infrastructure for storage resource and data files management, which supports several large scale applications. Due to limitation of available resources in grid, efficient use of the grid resources becomes an important challenge. Replication is a technique used in data grid to improve fault tolerance and to reduce the bandwidth consumption. This paper proposes a Dynamic Hierarchical Replication (DHR) algorithm that places replicas in appropriate sites i.e. best site that has the highest number of access for that particular replica. It also minimizes access latency by selecting the best replica when various sites hold replicas. The proposed replica selection strategy selects the best replica location for the users' running jobs by considering the replica requests that waiting in the storage and data transfer time. The simulated results with OptorSim, i.e. European Data Grid simulator show that DHR strategy gives better performance compared to the other algorithms and prevents unnecessary creation of replica which leads to efficient storage usage.     

Combination of data replication and scheduling algorithm for improving data availability in Data Grids

Data Grid is a geographically distributed environment that deals with large-scale data-intensive applications. Effective scheduling in Grid can reduce the amount of data transferred among nodes by submitting a job to a node, where most of the requested data files are available. Data replication is another key optimization technique for reducing access latency and managing large data by storing data in a wisely manner. In this paper two algorithms are proposed, first a novel job scheduling algorithm called Combined Scheduling Strategy (CSS) that uses hierarchical scheduling to reduce the search time for an appropriate computing node. It considers the number of jobs waiting in queue, the location of required data for the job and the computing capacity of sites. Second a dynamic data replication strategy, called the Modified Dynamic Hierarchical Replication Algorithm (MDHRA) that improves file access time. This strategy is an enhanced version of Dynamic Hierarchical Replication (DHR) strategy. Data replication should be used wisely because the storage capacity of each Grid site is limited. Thus, it is important to design an effective strategy for the replication replacement. MDHRA replaces replicas based on the last time the replica was requested, number of access, and size of replica. It selects the best replica location from among the many replicas based on response time that can be determined by considering the data transfer time, the storage access latency, the replica requests that waiting in the storage queue and the distance between nodes. The simulation results demonstrate the proposed replication and scheduling strategies give better performance compared to the other algorithms.     

Job scheduling and dynamic data replication in data grid environment

Data Grid is a geographically distributed environment that deals with large-scale data-intensive applications. Effective scheduling in Grid can reduce the amount of data transferred among nodes by submitting a job to a node, where most of the requested data files are available. Data replication is another key optimization technique for reducing access latency and managing large data by storing data in a wisely manner. In this paper, two algorithms are proposed: first, a novel job scheduling algorithm called Combined Scheduling Strategy (CSS) that considers the number of jobs waiting in queue, the location of required data for the job, and computational capability; second, a dynamic data replication strategy called Dynamic Hierarchical Replication Algorithm (DHRA) that improves file access time. DHRA stores each replica in an appropriate site, i.e., appropriate site in the requested region that has the highest number of access for that particular replica. Also, it can minimize access latency by selecting the best replica when various sites hold replicas of datasets. The simulation results demonstrate the proposed replication and scheduling strategies give better performance compared to the other algorithms.     

云存储智能多数据副本放置机制

数据副本管理机制是云存储系统的重要组成部分。为了提高云存储系统的可伸缩性、可靠性,同时改善用户访问时间,通常采用多数据副本机制,并且需要解决数据副本放置问题。为此,提出了一种用于云存储系统的智能多数据副本放置机制。该机制基于p-中心模型,以最小化访问代价为优化目标,基于遗传算法（genetic algorithm,GA）确定优化的数据副本放置方案,基于生物地理学优化（biogeography-based optimization,BBO）算法确定用户访问请求对数据副本的优化分配。基于CloudSim进行了仿真实现和性能评价,结果表明,云存储智能多数据副本放置机制是可行和有效的。     

Network and data location aware approach for simultaneous job scheduling and data replication in large-scale data grid environments

Data Grid integrates graphically distributed resources for solving data intensive scientific applications. Effective scheduling in Grid can reduce the amount of data transferred among nodes by submitting a job to a node, where most of the requested data files are available. Scheduling is a traditional problem in parallel and distributed system. However, due to special issues and goals of Grid, traditional approach is not effective in this environment any more. Therefore, it is necessary to propose methods specialized for this kind of parallel and distributed system. Another solution is to use a data replication strategy to create multiple copies of files and store them in convenient locations to shorten file access times. To utilize the above two concepts, in this paper we develop a job scheduling policy, called hierarchical job scheduling strategy (HJSS), and a dynamic data replication strategy, called advanced dynamic hierarchical replication strategy (ADHRS), to improve the data access efficiencies in a hierarchical Data Grid. HJSS uses hierarchical scheduling to reduce the search time for an appropriate computing node. It considers network characteristics, number of jobs waiting in queue, file locations, and disk read speed of storage drive at data sources. Moreover, due to the limited storage capacity, a good replica replacement algorithm is needed. We present a novel replacement strategy which deletes files in two steps when free space is not enough for the new replica: first, it deletes those files with minimum time for transferring. Second, if space is still insufficient then it considers the last time the replica was requested, number of access, size of replica and file transfer time. The simulation results show that our proposed algorithm has better performance in comparison with other algorithms in terms of job execution time, number of intercommunications, number of replications, hit ratio, computing resource usage and storage usage.     

Hierarchical data replication strategy to improve performance in cloud computing

Cloud computing environment is getting more interesting as a new trend of data management. Data replication has been widely applied to improve data access in distributed systems such as Grid and Cloud. However, due to the finite storage capacity of each site, copies that are useful for future jobs can be wastefully deleted and replaced with less valuable ones. Therefore, it is considerable to have appropriate replication strategy that can dynamically store the replicas while satisfying quality of service (QoS) requirements and storage capacity constraints. In this paper, we present a dynamic replication algorithm, named hierarchical data replication strategy (HDRS). HDRS consists of the replica creation that can adaptively increase replicas based on exponential growth or decay rate, the replica placement according to the access load and labeling technique, and finally the replica replacement based on the value of file in the future. We evaluate different dynamic data replication methods using CloudSim simulation. Experiments demonstrate that HDRS can reduce response time and bandwidth usage compared with other algorithms. It means that the HDRS can determine a popular file and replicates it to the best site. This method avoids useless replications and decreases access latency by balancing the load of sites.     

Replica Placement Strategies in Data Grid

Replication is a technique used in Data Grid environments that helps to reduce access latency and network bandwidth utilization. Replication also increases data availability thereby enhancing system reliability. The research addresses the problem of replication in Data Grid environment by investigating a set of highly decentralized dynamic replica placement algorithms. Replica placement algorithms are based on heuristics that consider both network latency and user requests to select the best candidate sites to place replicas. Due to dynamic nature of Grid, the candidate site holds replicas currently may not be the best sites to fetch replicas in subsequent periods. Therefore, a replica maintenance algorithm is proposed to relocate replicas to different sites if the performance metric degrades significantly. The study of our replica placement algorithms is carried out using a model of the EU Data Grid Testbed 1 [Bell et al. Comput. Appl., 17(4), 2003] sites and their associated network geometry. We validate our replica placement algorithms with total file transfer times, the number of local file accesses, and the number of remote file accesses.     

Replica Management in the European DataGrid Project

Within the European DataGrid project, Work Package 2 has designed and implemented a set of integrated replica management services for use by data intensive scientific applications. These services, based on the web services model, enable movement and replication of data at high speed from one geographical site to another, management of distributed replicated data, optimization of access to data, and the provision of a metadata management tool. In this paper we describe the architecture and implementation of these services and evaluate their performance under demanding Grid conditions.This work was partially funded by the European Commission program IST- 2000-25182 through the European DataGrid Project.     

教育资源网格模型及副本创建策略

教育资源网格是解决目前分布式教育资源共享问题的有效手段.针对中小学教育资源共享问题,提出了层次式的教育资源网格模型,定义了各层节点的功能.通过与欧洲数据网格对比,分析了教育资源网格的特点.基于层次式的教育资源网格,对影响副本创建策略性能的因素进行了分析,然后引入网络带宽和文件大小两个参数,提出了一种动态副本创建策略(dynamic replica creation strategy,简称EDRS).利用数据网格模拟工具OptorSim构建了教育资源网格虚拟环境,分析比较了EDRS策略与Caching-LRU策略、Caching-LFU策略和基于经济模型的副本创建策略的性能.最后,综合各项指标分析了不同策略对教育资源网格系统性能的影响.结果表明,EDRS策略在教育资源网格应用中有着更好的系统性能.