基于教育资源网格的副本放置策略

副本机制是提高网格性能,降低客户端延迟的有效手段,副本放置策略是副本机制的关键问题。该文介绍了教育资源网格的体系结构,针对教育资源网格特殊的体系结构,提出了3种动态副本放置策略,并从不同的角度对它们进行了优化,在保持副本存储空间占用的前提下,降低了客户端延迟。实验表明了策略的有效性。     

Adaptive data replication strategy in cloud computing for performance improvement

Cloud computing is becoming a very popular word in industry and is receiving a large amount of attention from the research community. Replica management is one of the most important issues in the cloud, which can offer fast data access time, high data availability and reliability. By keeping all replicas active, the replicas may enhance system task successful execution rate if the replicas and requests are reasonably distributed. However, appropriate replica placement in a large-scale, dynamically scalable and totally virtualized data centers is much more complicated. To provide cost-effective availability, minimize the response time of applications and make load balancing for cloud storage, a new replica placement is proposed. The replica placement is based on five important parameters: mean service time, failure probability, load variance, latency and storage usage. However, replication should be used wisely because the storage size of each site is limited. Thus, the site must keep only the important replicas.We also present a new replica replacement strategy based on the availability of the file, the last time the replica was requested, number of access, and size of replica. We evaluate our algorithm using the CloudSim simulator and find that it offers better performance in comparison with other algorithms in terms of mean response time, effective network usage, load balancing, replication frequency, and storage usage.     

云存储中动态副本放置机制研究

数据副本管理是云计算系统管理的重要组成部分,在云计算系统的海量数据处理过程中,针对目前已知的数据存放与资源调度算法存在考虑副本动态性和可靠性的不足,提出了一种动态的副本放置机制。该机制基于区域结构,考虑数据处理时其副本的数量和放置位置,以及副本的产生对于内存和带宽等系统资源的开销:首先根据云存储中的副本信息,对被访问频率高且访问平均响应时间长的数据信息进行复制,并给出副本数量的计算方法;考虑缩小副本分布的节点选择范围,提出动态的副本放置算法——DRA,将一定范围内的节点根据提出的域的划分,进行放置筛选,以存放数据副本。实验结果表明,提出的动态放置机制不仅减少了低访问率副本对系统存储空间的浪费;同时也减少了高访问率副本所需跨节点的传输延迟,有效提高了云存储系统中的数据文件的访问效率、负载的均衡水平,以及云存储系统的可靠性和可用性。     

云计算架构下的动态副本管理策略

为有效管理云计算环境中的数据副本,减少系统带宽消耗、最小化响应时间和平衡负载,提出了一种动态副本管理策略.通过建立文件可用性和副本数量间的关系模型来计算系统应维持的最小副本数量;基于数据被请求访问的次数和传输花费进行副本放置;数据被请求时结合节点间带宽和节点效用选择副本.实验结果表明了该策略的正确性有效性.     

面向SaaS应用的SMTDM可伸缩多租户数据管理框架

云计算是一种可以弹性并按需提供资源的技术.在多租户共享数据存储模式下,如何实现数据的动态伸缩存储是云数据管理的关键.针对SaaS应用如何随着租户数量及请求规模的变化而进行自适应伸缩的问题,在分析数据存储层的伸缩性需求的基础上,基于Walraven等人提出的多租户架构思想,扩展典型的云应用架构,设计了一个多租户数据管理框架,实现存储资源的弹性.基于该框架,开发了一个面向网络管理领域的SaaS原型系统,验证了其有效性和可用性.     

云环境下元数据弹性分级一致性保障机制研究 *

云存储环境下,元数据提供数据定位与资源描述服务,云存储系统利用副本技术保障元数据的可用性。为了解决元数据的动态更新导致的元数据副本的不一致,本文从云存储系统中不同元数据对一致性强度需求的差异性出发,提出了基于元数据弹性分级的一致性保障机制。根据元数据访问频率以及更新属性敏感度对元数据更新弹性分级,结合基于读优化的Quorum算法实现元数据更新的分级一致性保障。实验证明该机制可以有效权衡元数据副本可用性、一致性和系统开销,相比单一的一致性策略更为灵活。     

访问特征驱动的副本存储策略自动生成框架

大规模分布式数据存储是云计算和大数据时代的重要支撑技术.在分布式存储系统中,数据副本如何放置是一个基本问题.然而,现有可实用的算法或忽略应用具体的访问特征而牺牲效率,或拘泥于单一应用而不具备泛化能力.通过建立副本存储策略的统一描述模型以及提取应用的关键访问特征参数,定义出副本存储策略自动生成算法的输出和输入;通过机器学习的方法获得访问特征参数和最优副本存储策略参数之间的一般性关系,从而形成自动生成机制的核心算法.在提高存储系统访问性能及节约能耗等成本的同时,有效降低副本存储策略设计过程中的人工干预程度.     

Scalable replica selection based on node service capability for improving data access performance in edge computing environment

The replica strategies in traditional cloud computing often result in excessive resource consumption and long response time. In the edge cloud environment, if the replica node cannot be managed efficiently, it will cause problems such as low user’s access speed and low system fault tolerance. Therefore, this paper proposed replica creation and selection strategy based on the edge cloud architecture. The dynamic replica creation algorithm based on access heat (DRC-AH) and replica selection algorithms based on node service capability (DRS-NSC) were proposed. The DRC-AH uses data block as replication granularity and Grey Markov chain to dynamically adjust the number of replicas. After the replica is created, when client receives the user’s request, the DRS-NSC selects the best replica node to respond to the user. The experiments show that the proposed algorithms have significant advantages in prediction accuracy, user’s request response time, resource utilization, etc., and improve the performance of the system to a certain extent.

     

云环境中Web应用的微服务架构评估

云计算为我们提供了一种全新、高效的方式来部署可扩展的Web应用,这种方式使企业的应用可以按需对计算资源进行分配.微服务架构用于将庞大复杂的应用系统拆分为一系列可独立开发、测试、部署、运行、升级的服务模块.微服务架构为大批互联网企业实现云环境中的应用扩展、降低应用开发复杂度、实现敏捷开发提供了更加有效地方法.本文分析并测试了微服务架构模式,通过一个具体案例——在云环境中开发和部署的企业级应用系统,对两种架构模式实现（单体架构模式和微服务架构模式）进行性能测试,得出评估结果,这些结果对解决企业级应用微服务化中可能遇到的问题具有一定指导意义.     

网格环境中的资源监测

为了构建和执行网格应用，就必须了解网格资源的类型和可用性。该文提出了一个专门为网格设计的开放和通用的资源监测结构，并描述了这个资源监测结构中的资源监测系统的实现。     

大数据背景下智慧教育云平台构建研究

基于神经网络教育平台存在资源获取速度慢的缺点,针对该问题,提出了大数据背景下智慧教育云平台构建研究。在大数据支持下,设计智慧教育云平台总体架构,通过物理层、虚拟资源层配置相关硬件设备,经过逻辑层和展现层为用户提供有效服务,利用现有智慧信息,为用户提供个性化服务。优化处理Web端框架代码,降低代码复杂性,注重系统权限管控,科学设计服务功能,完成智慧教育云平台构建。由实验结果可知,该平台资源获取速度最高可达到99.5 MB/s,有效提高了学生学习效率。     

Dynamically negotiated resource management for data intensiveapplication suites

In contemporary computers and networks of computers, various application domains are making increasing demands on the system to move data from one place to another, particularly under some form of soft real-time constraint. A brute force technique for implementing applications in this type of domain demands excessive system resources, even though the actual requirements by different parts of the application vary according to the way it is being used at the moment. A more sophisticated approach is to provide applications with the ability to dynamically adjust resource requirements according to their precise needs, as well as the availability of system resources. This paper describes a set of principles for designing systems to provide support for soft real-time applications using dynamic negotiation. Next, the execution level abstraction is introduced as a specific mechanism for implementing the principles. The utility of the principles and the execution level abstraction is then shown in the design of three resource managers that facilitate dynamic application adaptation: Gryphon, EPA/RT-PCIP, and the DQM architectures     

Fog computing for next-generation Internet of Things: Fundamental,state-of-the-art and research challenges

In recent times, the Internet of Things (IoT) applications, including smart transportation, smart healthcare, smart grid, smart city, etc. generate a large volume of real-time data for decision making. In the past decades, real-time sensory data have been offloaded to centralized cloud servers for data analysis through a reliable communication channel. However, due to the long communication distance between end-users and centralized cloud servers, the chances of increasing network congestion, data loss, latency, and energy consumption are getting significantly higher. To address the challenges mentioned above, fog computing emerges in a distributed environment that extends the computation and storage facilities at the edge of the network. Compared to centralized cloud infrastructure, a distributed fog framework can support delay-sensitive IoT applications with minimum latency and energy consumption while analyzing the data using a set of resource-constraint fog/edge devices. Thus our survey covers the layered IoT architecture, evaluation metrics, and applications aspects of fog computing and its progress in the last four years. Furthermore, the layered architecture of the standard fog framework and different state-of-the-art techniques for utilizing computing resources of fog networks have been covered in this study. Moreover, we included an IoT use case scenario to demonstrate the fog data offloading and resource provisioning example in heterogeneous vehicular fog networks. Finally, we examine various challenges and potential solutions to establish interoperable communication and computation for next-generation IoT applications in fog networks.     

Optimized mobile cloud resource discovery architecture based on dynamic cognitive and intelligent technique

In current world, the Mobile Cloud Computing (MCC) will undergo a dramatic upgrade with integrating of mobile cloud resources in small regions to provide a better MCC service. This paper considers the MCC resource availability problem in integrated mobile cloud environment to optimization resources availability with a better effectiveness, accuracy, reliable, low latency and low complexity. The DCICRD architecture is proposed in this paper optimizes the resource discover process with better resource availability. The proposed DCICRD architecture runs various operations such Resource demand prediction to find the required level of resources, Cloudlet Resource Discovery process which discovers resources based on the requirement predicted using two states expand and shrink. The expand state is used to discover resources on-demand in resource scarcity situation and the shrink state is called when abundant resource available to the required level.  Further, Resource reliability check is performed to identify the reliable resource with energy level and signal strength above the threshold level. Finally, Local Resource Information Management Table is used to store the local resource provider information and Central Resource Information Management Table is used to store all local resource information for handover of resource provider. The implementation and evaluation is conducted by comparing the HARD architecture. The comparison result shows that proposed DCICRD architecture is better than HARD architecture by optimizing the resource discovery process and produce reliable resources on demand as per the required level with a better effectiveness using expand and shrink state, accuracy by making resource available for required level, reliable by sending message for frequent checking of Resource with required energy level and signal strength, low latency by making resource available locally and reduce complexity by performing only on-demand resource discovery operation after initial startup. Thus, the proposed DCICRD architecture is better when compared to HARD architecture.     

Automatic replication of WSRF-based Grid services via operation providers

The advent of service-oriented architectures in Grid environments has fostered the development of applications in distributed deployments. The Globus Toolkit 4 (GT4) and its implementation of stateful Web services, via the WS-Resource Framework (WSRF), is a suitable platform to develop these Grid services. This way, its increased usage in many scientific areas reveals new scenarios where fault-tolerance and high availability should be considered. This paper describes a library that manages the automatic replication of WSRF-based Grid services. This functionality can be plugged to existing Grid services, by means of minimal changes in its source code, to achieve state replication through WS-Resources. The architecture of the library and its performance evaluation are described. In particular, two different replica topologies are addressed: ring-based and leaf-to-root complete binary tree, in order to achieve resource state update in logarithmic time with respect to the number of replicas. Finally, the paper describes the integration of the replication library into a service-oriented metascheduler to enhance fault-tolerance and to guarantee service availability.     

云原生数据库综述

云基础设施的虚拟化、高可用、可弹性调度等特点,为云数据库提供了开箱即用、可靠可用、按需计费等优势.云数据库按照架构可以划分为云托管数据库(cloud-hosted database)以及云原生数据库(cloud-native database).云托管数据库将数据库系统直接部署到云上虚拟机环境中,具备低成本、易运维、高可靠的优势.在此基础上,云原生数据库充分利用云基础设施弹性伸缩的特点,采用计算存储分离的架构,实现了计算资源和存储资源的独立伸缩,进一步提升数据库性价比.然而计算存储分离的架构为数据库系统设计带来了新的挑战.深入分析云原生数据库系统的架构和技术.首先将云原生OLTP和云原生OLAP的数据库架构按照资源分离模式的差异分别进行归类分析,并对比各类架构的优势与局限.其次,基于计算存储分离的架构,按照各个功能模块深入探讨云原生数据库的关键技术:主要包括云原生OLTP关键技术(数据组织、副本一致性、主备同步、故障恢复以及混合负载处理)和云原生OLAP关键技术(存储管理、查询处理、无服务器感知计算、数据保护以及机器学习优化).最后,总结现有云原生数据库的技术挑战并展望未来研究方向.     

基于云服务的计算化学社区

中国科学院超算环境是由中国科学院统筹规划建设的“院总中心-分中心-所级中心”三层结构的超级计算环境,环境的资源聚合使用网格中间件 SCE,并使用 API 接口对外提供服务。依托超算环境建设的科学计算学云服务社区采用软件即服务的模式,使用浏览器/服务器结构来提供专业交互式的计算化学科学计算服务,以“降低使用门槛、提高使用效率,助跑计算化学用户的科学研究工作”为目标,为广大科研用户提供“一站式”计算的 Web 服务模式和异构资源协同工作模式。本文同时介绍了社区的建设应用情况以及环境的运行、监控情况。     

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。