Multilevel manufacturing system of virtual enterprise based on manufacturing grid and strategies for member enterprise selection and task assignment

In order to improve efficiency of virtual enterprise, a manufacturing grid and multilevel manufacturing system of virtual enterprise is built up. When selecting member enterprises and task assignment based on the manufacturing grid, key activities are assigned to the suitable critical member enterprises by task decomposition, enterprise node searching and characteristic matching of manufacturing resources according to the characteristic matching strategy. By task merger, some ordinary activities are merged with corresponding key activities and assigned to corresponding critical member enterprises. However, the other ordinary activities are assigned to the related ordinary member enterprises with enterprise node searching and characteristic matching of manufacturing resources. Finally, an example of developing the artificial hip joint in the virtual enterprise is used to demonstrate that efficiency of the virtual enterprise is improved by using the manufacturing grid and the proposed strategies for member enterprise selection and task assignment.

Task-and-role-based access-control model for computational grid

Access control in a grid environment is a challenging issue because the heterogeneous nature and independent administration of geographically dispersed resources in grid require access control to use fine-grained policies. We established a task-and-role-based access-control model for computational grid (CG-TRBAC model), integrating the concepts of role-based access control (RBAC) and task-based access control (TBAC). In this model, condition restrictions are defined and concepts specifically tailored to Workflow Management System are simplified or omitted so that role assignment and security administration fit computational grid better than traditional models; permissions are mutable with the task status and system variables, and can be dynamically controlled. The CG-TRBAC model is proved flexible and extendible. It can implement different control policies. It embodies the security principle of least privilege and executes active dynamic authorization. A task attribute can be extended to satisfy different requirements in a real grid system.

The Application of IHA in Grid Cloud Computing Task Decomposition and Scheduling Based on Bionics

Based on the large amount and variations of the power grid task as well as its requirement of real- time performance and economic benefit, we make a further improvement and expansion of IHA (Improved Heuristic Algorithm) on the combination of bionics in genetic engineering and evolution to solve the decomposing and scheduling problems. Firstly, we transform those complex decomposing problems into the operational optimal solution problem by IHA to decrease the rate of running into the local optimal solution [1]. In task scheduling, we classify the sub-tasks by the emergency degree for resource allocation, which not only largely reduces the calculation and resource cost but also improves working efficiency and the speed of execution [2]. Finally, we select optimal scheduling scheme by the Fitness function defined about time and cost.

A hybrid differential evolution algorithm for meta-task scheduling in grids

Task scheduling is one of the core steps to effectively exploit the capabilities of heterogeneous re-sources in the grid.This paper presents a new hybrid differential evolution(HDE)algorithm for findingan optimal or near-optimal schedule within reasonable time.The encoding scheme and the adaptation ofclassical differential evolution algorithm for dealing with discrete variables are discussed.A simple but ef-fective local search is incorporated into differential evolution to stress exploitation.The performance of theproposed HDE algorithm is showed by being compared with a genetic algorithm(GA)on a known staticbenchmark for the problem.Experimental results indicate that the proposed algorithm has better perfor-mance than GA in terms of both solution quality and computational time,and thus it can be used to de-sign efficient dynamic schedulers in batch mode for real grid systems.

Task scheduling scheme by checkpoint sharing and task duplication in P2P-based desktop grids

A scheduling scheme is proposed to reduce execution time by means of both checkpoint sharing and task duplication under a peer-to-peer(P2P) architecture. In the scheme, the checkpoint executed by each peer(i.e., a resource) is used as an intermediate result and executed in other peers via its duplication and transmission. As the checkpoint is close to a final result, the reduction of execution time for each task becomes higher, leading to reducing turnaround time. To evaluate the performance of our scheduling scheme in terms of transmission cost and execution time, an analytical model with an embedded Markov chain is presented. We also conduct simulations with a failure rate of tasks and compare the performance of our scheduling scheme with that of the existing scheme based on client-server architecture. Performance results show that our scheduling scheme is superior to the existing scheme with respect to the reduction of execution time and turnaround time.

An Improved Task Scheduling Algorithm in Grid Computing Environment

Algorithm research of task scheduling is one of the key techniques in grid computing. This paper firstly describes a DAG task scheduling model used in grid computing environment, secondly discusses generational scheduling (GS) and communication inclusion generational scheduling (CIGS) algorithms. Finally, an improved CIGS algorithm is proposed to use in grid computing environment, and it has been proved effectively.

A Fault Tolerance Algorithm for Resource Discovery in Semantic Grid Computing Using Task Agents

One of the interesting topics in grid computing systems is resources discovery. After the failure of a resource in a chain of resources made for a specific task in grid environment, discovering and finding a new resource that reconstructs the chain is an important topic. In this study, with defining new agent that is called task agent, and by proposing an algorithm, we will increase the fault tolerance against probable failure of a resource in the resource chain.

基层治理中的双向压力型体制及其运行逻辑——基于H镇网格化管理的个案分析

压力型体制作为本土概念业已成熟,但现有研究大多关注上级压力下传的单一向度,忽略了压力型体制对基层服务需求的主动回应。H镇网格化管理的案例表明,在基层治理场域中存在双向压力型体制。这种体制以二重性的压力运行过程应对上级管理要求和群众服务需求的两大压力环境。通过自上而下和自下而上两种压力的对流与互通,分别回应了上下差异性任务需求,也让两侧压力环境分别实现了势能释放和能量互补。双向压力型体制的概念是沟通压力型体制经典框架和基层治理经验的中层理论,凝练了基层实现服管结合的有益经验,蕴含着我国基层治理的独特逻辑。

基于GridSim的网格算法测试软件开发

针对目前绝大部分的网格任务调度实验均建立在仿真实验的基础之上,而GridSim网格模拟器在支持多任务、多资源环境下的网格算法测试方面表现得越来越力不从心,开发一个便于网格研究者使用的网格算法测试软件迫在眉睫。设计并实现了基于GridSim的网格算法测试程序,该程序包括了构建环境模块、任务调度模块和结果输出模块三个子模块。通过实验验证,该程序能够达到在多任务、多资源环境中分析、对比和验证网格调度算法性能的目的。

Access Grid中基于任务和角色的访问控制

Access G rid是计算网格的一个扩展,用于支持在网格上进行组对组的大规模视频会议,也可用于协同工作、远程教学等多个领域。基于任务和角色的访问控制是最新的访问控制模型,它对传统访问控制进行了改进和扩充,能为Access G rid提供更高效的安全和管理服务。因此,Access G rid中基于任务和角色的访问控制是网格研究中的重要方向。

基于GridSim的A-MM调度算法模拟

对网格计算环境中的任务调度模块做了分析,介绍了两种最经典的网格任务调度算法:Min-Min与Max-Min。通过对两者的分析,得知Min-Min算法的优点在于其执行的高效性,但它存在资源负载不平衡的问题。而Max-Min则正好相反。经过对比,提出了一种平衡两者优缺点的A-MM算法。使用GridSim网格环境仿真包建立A-MM调度算法的模拟平台,对GridSim的内部机制及用法给予简单介绍,并通过模拟实验对A-MM算法进行性能评测。评测结果表明:A-MM算法的完成时间接近Min-Min,而负载平衡性接近Max-Min,平衡了Min-Min与Max-Min的优缺点。

A Parallel Genetic Simulated Annealing Hybrid Algorithm for Task Scheduling

In this paper combined with the advantages of genetic algorithm and simulated annealing, brings forward a parallel genetic simulated annealing hybrid algorithm （PGSAHA） and applied to solve task scheduling problem in grid computing. It first generates a new group of individuals through genetic operation such as reproduction, crossover, mutation, etc, and than simulated anneals independently all the generated individuals respectively. When the temperature in the process of cooling no longer falls, the result is the optimal solution on the whole. From the analysis and experiment result, it is concluded that this algorithm is superior to genetic algorithm and simulated annealing.

NoMan-Grid计算原型系统

设计并实现一个无集中资源管理的网格计算原型系统——NoMan-Grid,描述该原型系统的体系结构、信息管理、任务调度机制,对原型系统进行验证与评价。在该原型系统中,不存在任何集中管理节点,所有节点功能相同,有效避免了中央控制节点存在的情况下网格规模受限的情况。对系统性能进行初步测试和分析的结果表明,该原型系统能充分利用Internet上空闲的计算资源,网络通信流量比较少且分布均衡,适合解决大规模分布式应用问题。

A TQCS-based Scheduling Approach for Manufacturing Grid

With Open Grid Service Architecture (OGSA) as system framework, and Globus Toolkit3.0 (GT3) as developing tools, Manufacturing Grid (MG) is proposed in this research to realize resource sharing and collaborative working among manufacturing resources, and task scheduling is one of the most critical components in this system. Nevertheless, the Globus Resource Allocation Manager (GRAM) does not provide scheduling system by default, and traditional performance-guided or economy-guided schedulers cannot satisfy our needs in MG. So, in this paper, a TQCS (Time, Quality, Cost, Service)-based scheduling approach is presented and the corresponding scheduler (Manufacturing Grid Task Scheduler, MGTS) is implemented with the functions of Global Process Planning (GPP) analyzing, resource discovery, resource selection, AHP (Analytic Hierarchy Process)-based resource mapping, and fault-tolerant handling. Furthermore, the application architecture is depicted at the end of the paper to illustrate the utilization of our scheduler.

基于SimGrid的网格依赖任务的Min-Min算法模拟

网格技术——被称为下一代的Internet的计算机网络技术,是近几年IT领域的一个研究热点,网格的主要目标是实现网络资源的充分共享和提供较高的计算能力。一个好的任务调度算法能够更好地发挥网格资源的作用。在网格任务调度的研究中采用一个实际的网格环境是没有必要的,而往往采用网格模拟器来完成。用SimGrid SG模拟网格环境,应用Min-Min算法思想对依赖任务实现了调度,通过对算法的模拟,验证了算法的可行性,并找出了实现算法高效的适用条件。

网格任务调度算法Min—min的GridSim模拟实现

该文用模拟方法研究网格中的任务调度问题．首先对Min—min算法进行分析，然后用GfidSim对Min-min调度算法进行模拟实现，阐述了实现过程，并统计模拟结果，对Min—min算法的MakeSpan和负载等性能进行了分析，验证了模拟实现过程的正确性。

基于Springcolud的智能电网平台的设计与实现

为解决智能电网中运行缓慢、电网运行滞后的问题,设计了智能电网平台系统,系统包括智能电网运行、探索数据挖掘平台和智能电网平台的应用三大模块。首先采用数据挖掘技术,实现了对智能电网平台数据的分析与处理;然后设计了智能电网的可视化平台,采用Intranet技术、多媒体技术和开发主流技术,构建了智能电网的可视化平台,实现了智能电网平台的可视化;最后利用数字地表模型和交互式遗传算法的改进算法,实现了智能电网的任务调度。试验表明,研究的智能电网平台在试验次数增加的同时完成任务的总时间波动最小,在20 s~25 s之间波动。

智能电网用户侧的超密集边缘计算网络卸载方法

随着中国经济的飞速发展,日益增长的居民用电需求和不断增加的用电设备类型使得新型智能电网设备的可靠性、稳定性受到广泛关注,然而仅依靠传统单一的网络架构往往无法应对大规模电网设备的数据请求。首先,针对智能电网用户侧的任务请求,提出一种超密集边缘计算网络下的成本优化模型;其次,考虑到通信资源和计算资源的价格对卸载策略的影响,将资源利用成本作为优化目标;最后,为了提高电网设备请求的服务质量,考虑能耗和时延约束的任务卸载策略,提出莱维飞行-蜉蝣粒子群优化(Lévy flight-mayfly particle swarm optimization,Lévy-MAPSO)算法。结果表明:不同价格对资源利用成本的影响十分显著。与PSO和Lévy-MA算法相比,Lévy-MAPSO算法由于其群体多样性和强大的搜索能力,所得到的资源利用成本最低,性能最好。

电网工程地铁图展示项目关键任务进程系统设计

针对电网工程地铁图分解规划效果受限,展示板块局部变量转换率较低,系统关键任务进程展示结果混乱的问题,进行电网工程地铁图展示项目关键任务进程系统设计。优化任务进程展示的主变化电路和信号转换器,重新分解规划电网工程地铁图关键任务进程,并据此构建WBS关键任务分解模型,进而建立地铁进程展示模块,设计地铁图项目任务进程展示算法,展示关键任务进程,完成电网工程地铁图展示项目关键任务进程的系统设计。测试结果表明:关键任务进程系统的展示板块局部变量转换率最高为69.8%,使得电网工程地铁图展示项目关键任务进程系统更具有实际应用价值。