Combined performance and availability analysis of distributed resources in grid computing

This paper presents a mathematical model to evaluate the performance of grid resources when availability of the resources is taken into account. The proposed model uses continuous time Markov chains (CTMCs) to model the failure-repair behavior of a grid resource. In grid computing environment, a resource not only may fail during task execution, but also it can cancel its membership at any time. Hence, the proposed CTMC considers the availability of a grid resource to a grid user in both failure and membership refusal situations. After modeling the availability of the resource, the mean sojourn time of grid tasks in each of the availability states is estimated. Assigning the mean sojourn times of the tasks as performance levels to each of the CTMC’s states, a Markov reward model (MRM) representing the combined performance and availability measures is obtained. Computing the cumulative state probability of the CTMC and multiplying reward rates of the MRM’s states to each of the corresponding state probabilities, the expected accumulated sojourn time of grid tasks in each of the grid resources is achieved. An illustrative example is presented and the results obtained from the proposed model are reported in cases where various scheduling disciplines are considered inside the grid resource to simultaneously service grid and local tasks.     

校园网格环境构建的关键技术研究

网格计算是新型分布式计算技术。网格是一个集成的计算与资源环境,它能够充分吸纳各种计算资源并将它们转化成一种方便可靠的计算能力。文中论述了网格计算的特点和体系结构及其与传统分布式计算的区别,进而分析了校园网格环境构建的关键技术。校园网格环境的构建是网格普及化必经的关键一步。在校园网格系统构建中除了文中提到的关键技术之外还有诸多问题如安全机制、资源管理、任务管理、通信技术等都有待于进一步研究和具体化。     

计算网格的资源分发和发现机制

1 引言计算网格的资源管理系统是为实现计算网格系统资源共享所应提供的最主要的服务之一。计算网格资源管理系统的基本功能是接受来自计算网格范围内的机器的资源请求,并且把特定的资源分配给资源请求者,并且合理地调度相应的资源,使请求资源的作业得以运行。资源分发、资源发现和资源的调度构成了计算网格资源管理系统的最主要的内容。资源分发和资源发现提供方法,通过该方法,在计算网格内部的机器能够形成一个可用的资源和其状态的一个视图。资源     

基于网格的煤矿安全应用网格体系研究

煤矿安全应用工作中数据信息分布、计算资源闲置、异构以及计算功能单一严重制约着行业应用和信息化的发展。网格（grid）是新一代高性能计算环境和信息服务基础设施,能够实现动态跨地域的资源共享和协同工作。该文基于新兴的网格技术,参照OGSA体系,提出并构建了从信息获取、处理到应用的完整的煤矿安全应用网格（CMSAG）体系结构。同时结合行业应用需求,着重研究了其资源表示、资源管理、存储访问以及数据缓存等关键技术,最后通过具体应用示例的工作流程论证了该网格体系的应用模式。     

网格环境下的集群系统作业管理研究

网格计算已经逐渐形成一个重要的新领域。相对于传统的分布式计算，它的显著之处在于它能够共享网络上的各种资源，包括地理上分布的各种计算资源。PBS是广泛应用于并行计算机的作业管理系统，它可以按照用户定义的配置参数相对公平地为每个作业分配系统资源。但是在网格环境范围内对集群系统进行管理仍然是一门有待研究的课题。利用网格系统软件和集群系统管理软件，实现了一种在网格环境下对集群系统作业进行管理的方法。     

网格环境下一种可扩展的资源发现机制

在分布、异构的网格环境下,如何快速、准确地查找用户所需要的资源是影响网格计算性能的重要因素。本文针对集中式资源发现方法存在的可扩展性弱的不足,将P2P方法应用到网格资源发现领域,提出了一种扩展性好的分布式资源发现方法。该方法使用二叉树来管理数据,网格中每个节点都负责管理一部分资源信息。实验结果表明,本文提
出的方法能有效解决集中式资源发现方法可扩展性不好、资源信息服务器负载过重、容易造成单点失效等问题。     

国家地质调查应用网格体系及关键技术研究

地质调查工作中数据密集、计算密集、资源分布、异构等问题制约了行业应用和信息化发展，网格(grid)是新一代高性能计算环境和信息服务基础设施，能够实现动态跨地域的资源共享和协同工作，国家地质调查应用网格(NGG)基于网格技术，以“服务”为中心，提出并构建了从信息获取、处理到应用的完整的7层体系结构，同时结合行业应用需求，着重研究资源管理、资源交易、服务等关键技术，在Petri网和图论的基础上提出了一种新的服务链模型(服务／资源网)，实现地质调查领域的资源共享和信息服务。     

动态自适应性网格资源管理模型研究

由于网格计算中的资源具有分布性、自治性、异构性和动态性，对计算网格资源进行高效的管理是一个具有挑战性的问题。提出了把移动代理技术引入网格计算资源管理，从而为资源管理提供一个自适应性的广域资源环境，使网格计算系统能够自动适应环境的变化，为网格计算资源管理的动态自适应性研究指引了方向。     

A resource management and fault tolerance services in grid computing

In grid computing, resource management and fault tolerance services are important issues. The availability of the selected resources for job execution is a primary factor that determines the computing performance. In this paper, we propose a resource manager for optimal resource selection. Our resource manager automatically selects the set of optimal resources among candidate resources that achieves optimal performance using a genetic algorithm. Typically, the probability of a failure is higher in the grid computing than in a traditional parallel computing and the failure of resources affects job execution fatally. Therefore, a fault tolerance service is essential in computational grids. And grid services are often expected to meet some minimum levels of Quality of Service (QoS) for a desirable operation. To address this issue, we also propose a fault tolerance service that satisfies QoS requirements. We extend the definition of failures from the conventional notion of failures in distribute systems in order to provide a fault tolerance service that deals with various types of resource failures, which include process failures, processor failures, and network failures. We also design and implement a fault detector and a fault manager. The implementation and simulation results indicate that our approaches are promising in that (1) the resource manager finds the optimal set of resources that guarantees efficient job execution, (2) the fault detector detects the occurrence of resource failures and (3) the fault manager guarantees that the submitted jobs complete and the performance of job execution is improved due to job migration even if some failures occur.     

Scheduling multi‐component applications with mobile agents in heterogeneous distributed systems

In grid computing environment, several classes of multi‐component applications exist. These types of applications may often require additional resources of different types that go beyond what is available in any of the sites making up the grid resource composition. The heterogeneity nature of both the user application and the computing environment makes this a challenging problem. However, the current off‐the‐shelf scheduling software can hardly cope with these diversities in distributed computing application frameworks. Therefore, there is the need for an adequate scheduling system that would grant simultaneous or coordinated access to application of multi‐component nature that requires resources of possibly multiple types, in multiple locations, managed by different resource providers. The main focus of this paper is to develop a mobile agent scheduling model that addresses the aforementioned challenge. A scheduling policy that pertains to job scheduling and resource allocation is proposed. The scheduling policy treats different multi‐component applications requiring diverse heterogeneous resources fairly. The policy is used by mobile agents to schedule user applications and to also find available and suitable distributed resource that are capable of executing user application at a very minimal time. Copyright © 2015 John Wiley & Sons, Ltd.     

Artificial Neural Network support to monitoring of the evolutionary driven security aware scheduling in computational distributed environments

Monitoring of the system performance in highly distributed computing environments is a wide research area. In cloud and grid computing, it is usually restricted to the utilization and reliability of the resources. However, in today’s Computational Grids (CGs) and Clouds (CCs), the end users may define the special personal requirements and preferences in the resource and service selection, service functionality and data access. Such requirements may refer to the special individual security conditions for the protection of the data and application codes. Therefore, solving the scheduling problems in modern distributed environments remains still challenging for most of the well known schedulers, and the general functionality of the monitoring systems must be improved to make them efficient as schedulers supporting modules.In this paper, we define a novel model of security-driven grid schedulers supported by an Artificial Neural Network (ANN). ANN module monitors the schedule executions and learns about secure task–machine mappings from the observed machine failures. Then, the metaheuristic grid schedulers (in our case—genetic-based schedulers) are supported by the ANN module through the integration of the sub-optimal schedules generated by the neural network, with the genetic populations of the schedules.The influence of the ANN support on the general schedulers’ performance is examined in the experiments conducted for four types of the grid networks (small, medium, large and very large grids), two security scheduling modes—risky and secure scenarios, and six genetic-based grid schedulers. The generated empirical results show the high effectiveness of such monitoring support in reducing the values of the major scheduling criteria (makespan and flowtime), the run times of the schedulers and the grid resource failures.     

Semantic-enabled CARE Resource Broker (SeCRB) for managing grid and cloud environment

Grid computing is mainly helpful for executing high-performance computing applications. However, conventional grid resources sometimes fail to offer a dynamic application execution environment and this increases the rate at which the job requests of users are rejected. Integrating emerging virtualization technologies in grid and cloud computing facilitates the provision of dynamic virtual resources in the required execution environment. Resource brokers play a significant role in managing grid and cloud resources as well as identifying potential resources that satisfy users’ application requests. This research paper proposes a semantic-enabled CARE Resource Broker (SeCRB) that provides a common framework to describe grid and cloud resources, and to discover them in an intelligent manner by considering software, hardware and quality of service (QoS) requirements. The proposed semantic resource discovery mechanism classifies the resources into three categories viz., exact, high-similarity subsume and high-similarity plug-in regions. To achieve the necessary user QoS requirements, we have included a service level agreement (SLA) negotiation mechanism that pairs users’ QoS requirements with matching resources to guarantee the execution of applications, and to achieve the desired QoS of users. Finally, we have implemented the QoS-based resource scheduling mechanism that selects the resources from the SLA negotiation accepted list in an optimal manner. The proposed work is simulated and evaluated by submitting real-world bio-informatics and image processing application for various test cases. The result of the experiment shows that for jobs submitted to the resource broker, job rejection rate is reduced while job success and scheduling rates are increased, thus making the resource management system more efficient.     

基于事件触发的网格资源监控研究

对于异构的高性能分布式计算环境——网格来说,监控其中每个计算节点获取资源的状态信息是至关重要的。然而传统的资源监控方式需要周期性地读取各个节点的信息,对网格系统资源浪费较大。本文提出一种基于事件触发的方法来降低监控服务开销,准确获取资源状态信息。然后采用该方法在Clobus Toolkit 4下实现网格资源监控系统,并对该系统做出客观评价。     

An improved load-balancing mechanism based on deadline failure recovery on GridSim

Grid computing has emerged a new field, distinguished from conventional distributed computing. It focuses on large-scale resource sharing, innovative applications and in some cases, high performance orientation. The Grid serves as a comprehensive and complete system for organizations by which the maximum utilization of resources is achieved. The load balancing is a process which involves the resource management and an effective load distribution among the resources. Therefore, it is considered to be very important in Grid systems. For a Grid, a dynamic, distributed load balancing scheme provides deadline control for tasks. Due to the condition of deadline failure, developing, deploying, and executing long running applications over the grid remains a challenge. So, deadline failure recovery is an essential factor for Grid computing. In this paper, we propose a dynamic distributed load-balancing technique called “Enhanced GridSim with Load balancing based on Deadline Failure Recovery” (EGDFR) for computational Grids with heterogeneous resources. The proposed algorithm EGDFR is an improved version of the existing EGDC in which we perform load balancing by providing a scheduling system which includes the mechanism of recovery from deadline failure of the Gridlets. Extensive simulation experiments are conducted to quantify the performance of the proposed load-balancing strategy on the GridSim platform. Experiments have shown that the proposed system can considerably improve Grid performance in terms of total execution time, percentage gain in execution time, average response time, resubmitted time and throughput. The proposed load-balancing technique gives 7 % better performance than EGDC in case of constant number of resources, whereas in case of constant number of Gridlets, it gives 11 % better performance than EGDC.     

Method of multiagent scheduling of resources in cloud computing environments

The paper describes a method of scheduling distributed computing resources in cloud environments for solving user tasks using a variety of software agents physically located on separate processor units connected to the cloud infrastructure and representing their interests in the process of computing. The advantages of the proposed approach are as follows: Firstly, the fast tracking of all resource changes occurring to the processing unit using agents and real time correction of the computing process taking into account these changes, which in turn makes it possible to use computing resources with a dynamically changing performance in the cloud environment (e.g., personal privately owned computers), and, secondly, a cost reduction for the cloud infrastructure because there is no need to introduce expensive dedicated nodes that perform service functions into its structure.     

基于资源目录技术的网格系统软件设计与实现

由于网格计算系统中地理分布着大量不同类型动态实体，资源、服务和计算的发现、特征化、监控变得非常困难，资源信息服务成为网格软件的关键技术之一。采用面向对象方法设计网格资源信息模型，使用目录服务技术建造网格系统软件中的资源信息服务，提供了资源信息发现、收集、存储和访问机制，介绍了国家高性能计算环境的基本结构和功能，详细描述了基于轻权目录访问协议（LDAP）的网格系统软件的设计和实现。     

一种基于在线反向拍卖技术的计算网格资源分配方法

利用经济模型研究网格资源管理是当前网格研究新的热点。在已有的网格资源管理方法的基础上,针对供大于求的计算网格环境,提出了一种基于在线反向拍卖技术的计算网格资源分配方法,并定义了相应的QoS函数,分析了该方法的适用范围和优点。最后通过模拟实验验证了该方法的效用,实验结果证明该方法是一种有效的计算网格资源分配方法。     

面向服务的织女星网格系统软件设计与评测

网格是开放的分布式系统,具有自治、动态和可扩展特性．在Internet环境下实现网格系统必须解决大规模资源共享与协同问题,也就是如何将Internet上无序的资源有序组织的问题．织女星网格系统软件针对现有网格平台软件存在的不足,借鉴传统计算机系统结构的方法来研究网格体系结构及其系统软件．面向网格应用的共性需求,提出了面向服务的层次化软件结构;引入网格资源空间的概念,实现了网格资源的虚拟化,屏蔽使用网格资源的技术细节．文章通过对网格关键问题的讨论,阐述了织女星网格系统软件的关键技术和设计思路,并通过对传统批作业的测试,分析了织女星网格系统软件的性能．     

Availability Modeling and Cost Optimization for the Grid Resource Management System

Grid computing is a recently developed technique for complex systems with large-scale resource sharing, wide-area communication, and multi-institutional collaboration. Although the development tools and techniques for the grid have been extensively investigated, the availability of the grid resource management system (RMS) has not been comprehensively studied. In order to contribute to this lacking but important field, this paper first models the grid RMS availability by considering both the failures of resource management (RM) servers and the length limitation of request queues. A hierarchical Markov reward model is implemented to evaluate the grid RMS availability. Based on the availability model, an optimization problem for designing the grid RMS is studied in order to minimize the cost by determining the best number of RM servers. Then, the sensitivity analysis is conducted, and a dynamic switching scheduling method is further presented based on the sensitivity analysis.     

面向SoC设计的网格PSE资源管理器与原型实现

集成电路技术的发展始终存在着两个主要矛盾：集成电路工艺加工能力的快速增长与设计生产力的矛盾,集成电路复杂度增加与设计时间紧缩的矛盾。文章从网格技术的角度,把SoC设计过程中不同的设计资源,包括IP核、EDA设计工具软件、代码、设计专家信息等,通过统一的资源管理机制实现对各种设计资源的管理,解决SoC设计对资源共享和协同的需要,提高SoC设计的整体效率。同时介绍了面向SoC设计网格问题求解环境下资源管理器的原型实现。