Fault Aware Dynamic Resource Manager for Fault Recognition and Avoidance in Cloud

Fault tolerance (FT) schemes are intended to work on a minimized and static amount of physical resources. When a host failure occurs, the conventional FT frequently proceeds with the execution on the accessible working hosts. This methodology saves the execution state and applications to complete without disruption. However, the dynamicity of open cloud assets is not seen when taking scheduling choices. Existing optimization techniques are intended in dealing with resource scheduling. This method will be utilized for distributing the approaching tasks to the VMs. However, the dynamic scheduling for this procedure doesn’t accomplish the objective of adaptation of internal failure. The scheme prefers jobs in the activity list with the most elevated execution time on resources that can execute in a shorter timeframe, but it suffers with higher makespan; poor resource usage and unbalance load concerns. To overcome the above mentioned issue, Fault Aware Dynamic Resource Manager (FADRM) is proposed that enhances the mechanism to Multi-stage Resilience Manager at an application-level FT arrangement. Proposed FADRM method gives FT a Multi-stage Resilience Manager (MRM) in the client and application layers, and simultaneously decreases the over-head and degradations. It additionally provides safety to the application execution considering the clients, application and framework necessities. Based on experimental evaluations, Proposed Fault Aware Dynamic Resource Manager (FADRM) method 157.5 MakeSpan (MS) time, 0.38 Fault Rate (FR), 0.25 Failure Delay (FD) and improves 5.5 Performance Improvement Ratio (PIR) for 25, 50, 75 and 100 tasks and 475 MakeSpan (MS) time, 0.40 Fault Rate (FR), 1.30 Failure Delay (FD) and improves 6.75 improves Performance Improvement Ratio (PER) for 100, 200, 300 and 500 Tasks compare than existing methodologies.     

拓扑相关蠕虫仿真分析

从蠕虫传播的漏洞主机发现、代码传播以及代码启动3个方面提出了一个完整的数据包级拓扑相关蠕虫仿真模型,设计了基于有向图的SmallWorld拓扑结构生成算法,并通过选择性抽象在NS2上实现了一个完整的数据包级拓扑相关蠕虫仿真系统.最后,通过该仿真系统实验分析了逻辑拓扑结构和蠕虫代码启动方式对蠕虫传播的影响.仿真实验结果表明,该仿真系统可以为拓扑相关蠕虫研究提供重要的支持.     

云工作流系统中能耗感知的任务调度算法*

云工作流系统研究集中在工作流任务执行的时间效率优化,然而时间最优的任务调度方案可能存在不同能耗,因此,文中求解满足时间约束时能耗最优的调度方案。首先改进任务执行能耗模型,设计适用于评价任务调度方案执行能耗的适应度计算方法。然后基于精准调整粒子速度的自适应权重,提出解决任务调度能耗优化问题的自适应粒子群算法。实验表明,文中算法收敛稳定,调度方案执行能耗较低。     

面向云环境实时感知的服务选取及评估方法研究

在云计算环境中,感知服务能力变化缺少实时性,服务质量难以保证。可信与不可信实体对元服务可能做出相同或者不相同的服务描述,存在欺骗行为。多个用户同时调用同一个高质量的服务时,极有可能出现用户访问量超过服务的负载容量从而导致服务能力下降的情况。针对此类问题,提出了一种基于环境实时感知的服务选取方法。在该方法中,服务调度中心采用招标投标的方式选取满足用户需求的元服务簇,以保证中标服务的QoS。同时服务质量感知模块实时感知元服务质量的变化,确保所选Web服务质量的高实时性和高可靠性。另外,信誉模型对服务提供者的承诺质量进行评价,从而建立高度可信的服务环境。实验结果表明,所提方法能够有效实现服务的预测评估,并为用户提供服务质量更优的Web服务。     

Efficient Energy-Aware Resource Management Model (EEARMM) Based Dynamic VM Migration

In cloud environment, an efficient resource management establishes the allocation of computational resources of cloud service providers to the requests of users for meeting the user’s demands. The proficient resource management and work allocation determines the accomplishment of the cloud infrastructure. However, it is very difficult to persuade the objectives of the Cloud Service Providers (CSPs) and end users in an impulsive cloud domain with random changes of workloads, huge resource availability and complicated service policies to handle them, With that note, this paper attempts to present an Efficient Energy-Aware Resource Management Model (EEARMM) that works in a decentralized manner. Moreover, the model involves in reducing the number of migrations by definite workload management for efficient resource utilization. That is, it makes an effort to reduce the amount of physical devices utilized for load balancing with certain resource and energy consumption management of every machine. The Estimation Model Algorithm (EMA) is given for determining the virtual machine migration. Further, VM-Selection Algorithm (SA) is also provided for choosing the appropriate VM to migrate for resource management. By the incorporation of these algorithms, overloading of VM instances can be avoided and energy efficiency can be improved considerably. The performance evaluation and comparative analysis, based on the dynamic workloads in different factors provides evidence to the efficiency, feasibility and scalability of the proposed model in cloud domain with high rate of resources and workload management.     

一种多速率移动自组网中的拓扑控制算法

提出了一种移动自组网中的拓扑控制算法MATC(multi-rate aware topology control),该算法充分考虑了无线环境中固有的多速率特性,在保证原有网络连接性的前提下删除部分低速链路,为上层的路由协议构造一个良好的拓扑,确保按需路由协议能够在一跳范围内发现最优路由.大量仿真结果表明,MATC对网络性能有较大的提高.     

Data-Locality Aware Scientific Workflow Scheduling Methods in HPC Cloud Environments

Efficient data-aware methods in job scheduling, distributed storage management and data management platforms are necessary for successful execution of data-intensive applications. However, research about methods for data-intensive scientific applications are insufficient in large-scale distributed cloud and cluster computing environments and data-aware methods are becoming more complex. In this paper, we propose a Data-Locality Aware Workflow Scheduling (D-LAWS) technique and a locality-aware resource management method for data-intensive scientific workflows in HPC cloud environments. D-LAWS applies data-locality and data transfer time based on network bandwidth to scientific workflow task scheduling and balances resource utilization and parallelism of tasks at the node-level. Our method consolidates VMs and consider task parallelism by data flow during the planning of task executions of a data-intensive scientific workflow. We additionally consider more complex workflow models and data locality pertaining to the placement and transfer of data prior to task executions. We implement and validate the methods based on fairness in cloud environments. Experimental results show that, the proposed methods can improve performance and data-locality of data-intensive workflows in cloud environments.     

A Review of Dynamic Resource Management in Cloud Computing Environments

In a cloud environment, Virtual Machines (VMs) consolidation and resource provisioning are used to address the issues of workload fluctuations. VM consolidation aims to move the VMs from one host to another in order to reduce the number of active hosts and save power. Whereas resource provisioning attempts to provide additional resource capacity to the VMs as needed in order to meet Quality of Service (QoS) requirements. However, these techniques have a set of limitations in terms of the additional costs related to migration and scaling time, and energy overhead that need further consideration. Therefore, this paper presents a comprehensive literature review on the subject of dynamic resource management (i.e., VMs consolidation and resource provisioning) in cloud computing environments, along with an overall discussion of the closely related works. The outcomes of this research can be used to enhance the development of predictive resource management techniques, by considering the awareness of performance variation, energy consumption and cost to efficiently manage the cloud resources.     

Workflow-and-Platform Aware task clustering for scientific workflow execution in Cloud environment

A scientific workflow, usually consists of a good mix of fine and coarse computational granularity tasks displaying varied runtime requirements. It has been observed that fine grained tasks incur more scheduling overhead than their execution time, when executed on widely distributed platforms. Task clustering is extensively used, in such situations, as a runtime optimization method which involves combining multiple short duration tasks into a cluster, to be scheduled on a single resource. This helps in minimizing the scheduling overheads of the fine grained tasks. However, tasks grouping curtails the degree of parallelism and hence needs to be done optimally. Though a number of task clustering techniques have been developed to reduce the impact of system overheads, they fail to identify the appropriate number of clusters at each level of workflow in order to achieve maximum possible parallelism. This work proposes a level based autonomic Workflow-and-Platform Aware (WPA) task clustering technique which takes into consideration both; the workflow structure and the underlying resource set size for task clustering. It aims to achieve maximum possible parallelism among the tasks at a level of a workflow while minimizing the system overheads and resource wastage. A comparative study with current state of the art task clustering approaches on four well-known scientific workflows show that the proposed method significantly reduces the overall workflow execution time and at the same time is able to consolidate the load onto minimum possible resources.     

云计算在实验室建设及管理中的应用

随着科学技术的快速发展,计算机应用软件也在不断升级发展。现阶段,云计算在全世界范围中已经可以看作是一项非常热门的IT技术,将其应用于实验室的建设和管理中,能够让人们更加快速地掌握这项技术。基于此,首先介绍了云计算的基本概念和发展情况,然后分析了这项技术的应用优势,最后重点探讨了云计算的实践应用模式,以期为相关人员提供一定的参考。     

分布式环境下行为感知的信任管理

针对分布式环境下信任管理的不确定性,在基于角色的信任管理基础上,引入行为感知的分析方法,以此作为信任评价的依据。将行为对信任域的影响量化为固定区间内的信任值,通过统计用户群的累积行为,计算出角色声望和域声望,并反馈调整信任域之间的信任委托关系。静态角色和动态行为的结合有效地增强了分布式网络的信任保障能力。     

基于Cloud Foundry的云计算PaaS平台拓扑展示设计与实现

云计算PaaS（platformasaservice,平台即服务）平台是互联网领域近几年来非常热的技术方向,不仅可以为用户提供开发环境、开发平台和硬件资源,还可以提高硬件资源的利用率,降低业务运营成本。然而,在提供服务的过程中,缺乏一种有效的机制来约束服务提供商的行为。文章为实现云计算PaaS平台下对应用的监测,提出了拓扑管理的概念。应用拓扑可以监测应用与资源的关系,资源拓扑可以监测资源与应用的关系,两者从不同的角度实现对应用的监测。文章采用哈希数组保存拓扑信息,采用消息总线的“发布-订阅”模式传递消息,实现拓扑信息的获取,将结果使用网页进行展示。本系统可以使服务提供商清晰地掌握系统中应用与资源的关系以及资源情况,有效提高资源利用率,因此,系统可以提升服务质量,进而保障用户利益。应用拓扑展示和资源拓扑展示可以及时反映系统中应用与资源的关系,从两种不同的角度展示出来,给云计算提供者管理应用提供了很大的方便。     

云计算在信息管理系统中的应用

云计算技术在信息管理系统中发挥了越来越重要的作用,在医疗、教育等领域中得到了广泛的应用,通过应用云计算技术能够使信息管理系统具有更加智能化的功能,提升了资源的利用率和信息的处理效率。笔者分析了云计算技术的特点,阐述了云计算技术的发展现状,并详细分析了云计算在信息管理系统中的框架构建以及应用。     

云桌面技术在计算机实验室建设和管理中的应用

计算机是当前高校教育管理中常用的设备,云桌面技术作为计算机软硬件更换升级的重要技术形式,将其应用到高校计算机实验室中能够提升实验室管理成效。因此,笔者在阐述云桌面技术内涵、优点的基础上,针对计算机实验室建设、管理存在的问题,为更好实现云桌面技术在计算机实验室建设和管理中的应用进行了分析,旨在更好发挥计算机在高校教育管理中的作用。     

浅谈云计算在客户关系管理中的应用

近年来,云计算作为一种迅速发展的信息技术已应用在国内外诸多领域,对于客户关系管理而言,云计算的诞生对于客户关系管理带来了机遇和挑战。在介绍云计算基本原理及发展概述的基础上,探讨云计算技术对电信行业客户关系管理的影响,以及云计算技术应用于客户关系管理时需要重点关注的问题。     

移动自组网中感知拓扑失配的P2P覆盖网构建

对等计算主要关注构造在应用层的虚拟覆盖网络. 在上层的覆盖网络和底层的物理网络之间, 通常都存在着拓扑失配. 这种失配会导致P2P应用耗费相当大的通信开销. 在移动自组网中, 由于资源(比如带宽, 电池能量等)和节点移动性的限制, 拓扑失配问题变得更加严重. 而已有工作对这一问题没有进行充分的研究. 本文研究了移动自组网中的拓扑失配问题对非结构化P2P覆盖网中目标搜索的影响, 并提出一个分布式的、能感知拓扑失配的覆盖网络构建算法D-TAOC.分析和实验表明在D-TAOC构建的拓扑失配感知的覆盖网中, P2P应用能够在较少牺牲目标搜索效率的前提下, 明显地降低网络中的通信负载.     

模拟器和虚拟机在网络协议虚拟实验的应用

通过分析模拟器、虚拟机和虚拟实验技术,探讨了模拟器和虚拟机技术在网络协议虚拟实验中的应用,最后结合Boson NetSim模拟器和VMware虚拟机技术建立了DiffServ/MPLS测试Qos的虚拟实验.     

云应用管理引擎的设计与实现

介绍了云应用管理引擎的设计思想与实现方式,以解决传统上应用程序必须安装在本地才能运行的问题.云应用管理引擎对安装在Windows或Linux应用服务器上的应用程序进行管理,在集群范围或服务器范围内将应用程序发布为远程应用,并将远程应用授权给用户.授权用户可以从各种客户端设备和系统运行交付给自己的远程应用程序,就像运行本地应用程序一样.     

一种适应节能的云存储系统元数据动态建模与管理方法

云存储规模的不断扩大以及设计时对能耗因素的忽略使其日益暴露出高能耗低效率的问题,并且此问题已经成为制约云计算与大数据快速发展的一个主要瓶颈.从云存储系统的元数据信息的组成与组织方式考虑系统的节能改进与适应性问题,提出适应节能的元数据建模与管理方法,将存储磁盘及节点状态、数据块存储位置与状态等信息纳入到新的元数据模型中.围绕节能的元数据模型设计了适应该模型的节能模式切换算法,有效的解决了现有算法与节能算法或策略的不匹配问题.实验结果表明:适应节能的元数据模型与算法能够提高系统磁盘级的能耗利用率.     

动态VLAN管理及其应用

介绍了一种利用SNMP协议实现动态VLAN管理的方法;描述了用SNMP 读取、处理MIB对象值的过程,实现TVLAN的动态配置。给出了动态VLAN技术在网络数字语音系统中的应用。