基于虚拟机部署策略的云平台容错即服务方法

针对如何充分利用云基础架构层资源,满足上层云应用系统租户对应用系统容错的需求多样性和高可靠性要求的问题,提出一种面向租户和云服务提供商的、基于虚拟机部署策略的云平台容错即服务方法。该方法根据租户的特定容错需求适配适合的容错方法及容错级别,据此计算并最优化云服务提供商的收益和资源使用量,在此基础上对提供容错服务的虚拟机进行优化部署,充分利用底层虚拟机资源为租户的云应用系统提供更为可靠的容错服务。实验结果表明,所提方法能够在保障云服务提供商收益的基础上,为多租户云应用系统实现更灵活且可靠性更高的容错服务。     

融合容错需求和资源约束的云容错服务适配方法

云计算环境下,失效成为一种常态行为,可靠性保障能力不足不仅成为云计算应用推广的主要障碍,而且还促使云计算环境下的容错服务研究成为一个亟待解决的问题。针对目前云计算容错服务研究中存在的用户容错需求定义无法直接反映用户关心的可靠性问题,以及云容错服务供应商资源得不到灵活利用等问题,提出了一种融合容错需求和资源约束的云容错服务适配方法。从用户的角度,以组件为单位,基于可靠性对用户的容错需求进行定义。从云容错服务供应商的角度,分别在其资源充足和资源不足的情况下研究最佳的容错服务适配方法,并使用最优化理论求解该适配方法下的容错服务。实验结果表明,所提出的容错服务适配方法生成的容错服务能更好地满足用户和云容错服务供应商的需求。     

基于服务替换的Web Services容错方法

现有网络服务(Web Services)容错方案存在资源开销大、与Web Services应用系统特性不一致等不足。针对该问题,根据Web Services应用系统特点提出基于服务替换的Web Services应用系统容错方法,其主要思想是在失效发生时使用网络中的等价服务替换失效服务。实验表明了该方法的可行性。     

基于虚拟化技术的仿真系统容错优化方法

节点崩溃或者仿真资源不足导致的分布式仿真系统故障,降低了仿真系统可靠性。为保证系统容错效果,降低容错开销,提出了一种基于虚拟化技术的仿真系统容错方法,按照系统故障发生的位置,对不同类型故障动态采用不同类型的容错策略。分析了检查点容错策略的优化方法,给出了最优设置间隔;结合虚拟化技术的优势,解决了副本容错策略的节点选择、副本数量以及位置分布问题;同时,引入基于虚拟机迁移的容错策略,并将其作为检查点容错策略和副本容错策略的补充,以降低容错开销。通过仿真实验数据对比,分析了动态容错策略与普通容错策略的性能,可知动态容错策略保证了系统容错性能,容错开销也保持在较低水平。     

超级计算环境容错机制

中国科学院超级计算环境是整合了包括总中心、分中心和所级中心计算资源的3层架构超级计算环境.为提升超级计算环境的可靠性,提供稳定、可靠的计算服务,其容错机制的研究成为超级计算环境的一个研究重点.在对容错基本思想及各类计算机容错技术进行充分调研的基础上,提出一种适用于超级计算环境的容错框架,依据该框架给出了不同层次的容错方案,并对不同层次的容错开销进行了分析和比较,验证了不同层次容错方案对应用程序所带来的影响.     

一种高可用集群头节点的实现

基于Beowulf Linux集群结构,比较几种头节点的高可用模型,利用已成熟的软件模块,采用容错的头节点系统服务方法,提供连续可靠的系统服务.     

面向内存的混合容错编码动态调节设计

针对内存系统中高强度的容错编码容易造成过大开销的问题,为同时实现容错强度和容错开销的权衡,提出一种低开销的支持混合容错编码的动态调节设计.通过分析发现常见纠错检错编码数据位长与校验位长存在固定的比例关系,提出一种地址映射逻辑电路;当系统存取内存数据及容错强度发生调节时,该方法可保证容错编码中校验信息的存取,实现对内存容错强度调节以及数据与校验信息在内存中分开存储的支持.实验结果表明,文中设计简单,硬件和性能代价小、功耗开销低.     

信息系统分布式轻量化容器云的设计与应用

针对分布式环境下信息系统面临的发展需求,设计并构建了信息系统分布式轻量化容器云平台,支撑信息系统"网-云-边-端"应用模式.基于国产容器技术的分布式架构的集群管理解决方案,融合服务器组合的计算、存储及网络等基础硬件资源,构建弹性、可扩展、共享式虚拟资源池,实现各类资源可视化的统一管理、按需分配和灵活调度;借助高可用机制、负载均衡等技术,为信息系统提供了轻量、灵活、可靠、高效的信息服务支撑能力,提升系统效能.     

一种面向服务的事件驱动架构信息集成平台构造方法

信息集成方法研究对企业异构复杂应用集成具有重要的学术和应用价值.提出一种面向服务的事件驱动架构SOEDA,可构建灵活的分布式信息集成平台.采用以服务单元形式封装的适配器连接各种异构系统,通过分布式标准消息路由器作为底层平台实现服务单元的注册、发现和通信.层次化体系和模块化设计有助于提高应用系统的敏捷性、互操作和集成能力.服务单元中采用事件驱动和动态线程池技术保障了系统的高效性.引入系统外部负载、线程开销和资源冲突,采用排队论模型对其性能进行评价.实例表明采用该方法构建的SynchroESB平台能够灵活集成企业遗留系统,提供高效可靠服务.     

基于服务冗余的SOA系统容错方法研究

Web Service的分布、异构和动态特性使面向服务的软件系统易受网络和软硬件故障影响,导致Web Service不可用,进而使整个SOA系统失效.论文提出一种基于服务冗余的容错体系结构,给出服务域划分规则,并介绍了三种容错策略以及一般的容错过程.     

网口容错在嵌入式设备中的设计与实现

针对嵌入式网络设备的服务可靠性问题,现有的网口容错主要采用双网卡冗余设计,研究针对单网卡多网口的情况,提出了一种网口容错方法,该方法能有效利用系统带宽资源。提出的网口容错方法包括一种网口状态检测机制和故障网口服务数据迁移方法,并设计了相应的功能模块。故障检测模块实现基于Loopback的检测方法,容错处理模块则实现在检测到故障后,可将故障网口的服务负载数据根据迁移策略迁移到其余正常网口。所提出的方法实现了对网口工作状态的快速检测和负载数据的有效容错。该方法具有应用无关性、资源占用率低的特点,通过测试验证了该方法的可行性。     

Fault tolerant and prioritized scheduling in OGSA‐based mobile Grids

Grids and mobile Grids can form the basis and the enabling technology for pervasive and utility computing due to their ability to being open, highly heterogeneous and scalable. In this paper we present a scheme for advancing quality of service (QoS) attributes, such as fault tolerance and prioritized scheduling, in OGSA‐based mobile Grids. The fault tolerance is achieved by producing and managing sufficient replicas of tasks submitted for execution on the mobile Grid resources. We design a simple and efficient prioritization scheme, which allows the scheduling of the tasks submitted by the Grid users as distinguished priorities that can be managed and exploited as a QoS parameter by the Grid infrastructure operator. The results that are presented show the efficiency of the proposed scheme in being simple and additionally enriching with reliability and QoS features the applications that are built on the concept of mobile Grids. Copyright © 2008 John Wiley & Sons, Ltd.     

MidCloud: an agent‐based middleware for effective utilization of replicated Cloud services

The Cloud relies heavily on resource replication to support the demands of the clients efficiently. Replicated Cloud services are distributed across large geographic areas and are accessible via the Internet. This paper describes MidCloud; an agent‐based middleware that provides Cloud clients with dynamic load balancing and fault tolerance mechanisms for effective utilization of replicated Cloud services and resources. MidCloud can be used to connect clients with multiple replicated Cloud services and provide fast and reliable service delivery from multiple replicas. Several approaches for load balancing and fault tolerance in distributed systems were introduced; however, they require prior knowledge of the environment's operating conditions and/or constant monitoring of these conditions at run time that allows the applications to adjust the load and redistribute the tasks when operational conditions change and when failures occur. These techniques work well when there is no high communication delay. Yet, this is not true in the Cloud, where data storage and computation servers are scattered all over the world and communication delays are usually very high. MidCloud deploys approaches to reduce the negative impact of high and dynamic delays on the Cloud servers and the Internet. The experimental results show the positive effects of using MidCloud to provide efficient load balancing and fault tolerance. Copyright © 2013 John Wiley & Sons, Ltd.     

A fault‐tolerant dynamic scheduling method on hierarchical mobile edge cloud computing

Mobile edge cloud computing has been a promising computing paradigm, where mobile users could offload their application workloads to low‐latency local edge cloud resources. However, compared with remote public cloud resources, conventional local edge cloud resources are limited in computation capacity, especially when serve large number of mobile applications. To deal with this problem, we present a hierarchical edge cloud architecture to integrate the local edge clouds and public clouds so as to improve the performance and scalability of scheduling problem for mobile applications. Besides, to achieve a trade‐off between the cost and system delay, a fault‐tolerant dynamic resource scheduling method is proposed to address the scheduling problem in mobile edge cloud computing. The optimization problem could be formulated to minimize the application cost with the user‐defined deadline satisfied. Specifically, firstly, a game‐theoretic scheduling mechanism is adopted for resource provisioning and scheduling for multiprovider mobile applications. Then, a mobility‐aware dynamic scheduling strategy is presented to update the scheduling with the consideration of mobility of mobile users. Moreover, a failure recovery mechanism is proposed to deal with the uncertainties during the execution of mobile applications. Finally, experiments are designed and conducted to validate the effectiveness of our proposal. The experimental results show that our method could achieve a trade‐off between the cost and system delay.     

Cost-oriented proactive fault tolerance approach to high performance computing (HPC) in the cloud

Cloud computing offers new computing paradigms, capacity and flexible solutions to high performance computing (HPC) applications. For example, Hardware as a Service (HaaS) allows users to provide a large number of virtual machines (VMs) for computation-intensive applications using the HaaS model. Due to the large number of VMs and electronic components in HPC system in the cloud, any fault during the execution would result in re-running the applications, which will cost time, money and energy. In this paper we presented a proactive fault tolerance (FT) approach to HPC systems in the cloud to reduce the wall-clock execution time and dollar cost in the presence of faults. We also developed a generic FT algorithm for HPC systems in the cloud. Our algorithm does not rely on a spare node prior to prediction of a failure. We also developed a cost model for executing computation-intensive applications on HPC systems in the cloud. We analysed the dollar cost of provisioning spare nodes and checkpointing FT to assess the value of our approach. Our experimental results obtained from a real cloud execution environment show that the wall-clock execution time and cost of running computation-intensive applications in cloud can be reduced by as much as 30%. The frequency of checkpointing of computation-intensive applications can be reduced up to 50% with our FT approach for HPC in the cloud compared with current FT approaches.     

OPTIMIS: A holistic approach to cloud service provisioning

We present fundamental challenges for scalable and dependable service platforms and architectures that enable flexible and dynamic provisioning of cloud services. Our findings are incorporated in a toolkit targeting the cloud service and infrastructure providers. The innovations behind the toolkit are aimed at optimizing the whole service life cycle, including service construction, deployment, and operation, on a basis of aspects such as trust, risk, eco-efficiency and cost. Notably, adaptive self-preservation is crucial to meet predicted and unforeseen changes in resource requirements. By addressing the whole service life cycle, taking into account several cloud architectures, and by taking a holistic approach to sustainable service provisioning, the toolkit aims to provide a foundation for a reliable, sustainable, and trustful cloud computing industry.     

Matrix based proactive resource provisioning in mobile cloud environment

Mobile cloud computing is a dynamic, virtually scalable and network based computing environment where mobile device acts as a thin client and applications run on remote cloud servers. Mobile cloud computing resources required by different users depend on their respective personalized applications. Therefore, efficient resource provisioning in mobile clouds is an important aspect that needs special attention in order to make the mobile cloud computing a highly optimized entity. This paper proposes an adaptive model for efficient resource provisioning in mobile clouds by predicting and storing resource usages in a two dimensional matrix termed as resource provisioning matrix. These resource provisioning matrices are further used by an independent authority to predict future required resources using artificial neural network. Independent authority also checks and verifies resource usage bill computed by cloud service provider using resource provisioning matrices. It provides cost computation reliability for mobile customers in mobile cloud environment. Proposed model is implemented on Hadoop using three different applications. Results indicate that proposed model provides better mobile cloud resources utilization as well as maintains quality of service for mobile customer. Proposed model increases battery life of mobile device and decreases data usage cost for mobile customer.     

A cost‐efficient resource provisioning algorithm for DHT‐based cloud storage systems

Personal cloud storage provides users with convenient data access services. Service providers build distributed storage systems by utilizing cloud resources with distributed hash table (DHT), so as to enhance system scalability. Efficient resource provisioning could not only guarantee service performance, but help providers to save cost. However, the interactions among servers in a DHT‐based cloud storage system depend on the routing process, which makes its execution logic more complicated than traditional multi‐tier applications. In addition, production data centers often comprise heterogeneous machines with different capacities. Few studies have fully considered the heterogeneity of cloud resources, which brings new challenges to resource provisioning. To address these challenges, this paper presents a novel resource provisioning model for service providers. The model utilizes queuing network for analysis of both service performance and cost estimation. Then, the problem is defined as a cost optimization with performance constraints. We propose a cost‐efficient algorithm to decompose the original problem into a sub‐optimization one. Furthermore, we implement a prototype system on top of an infrastructure platform built with OpenStack. It has been deployed in our campus network. Based on real‐world traces collected from our system and Dropbox, we validate the efficiency of our proposed algorithms by extensive experiments. Copyright © 2016 John Wiley & Sons, Ltd.     

Resource scheduling algorithm with load balancing for cloud service provisioning

Cloud computing uses scheduling and load balancing for virtualized file sharing in cloud infrastructure. These two have to be performed in an optimized manner in cloud computing environment to achieve optimal file sharing. Recently, Scalable traffic management has been developed in cloud data centers for traffic load balancing and quality of service provisioning. However, latency reducing during multidimensional resource allocation still remains a challenge. Hence, there necessitates efficient resource scheduling for ensuring load optimization in cloud. The objective of this work is to introduce an integrated resource scheduling and load balancing algorithm for efficient cloud service provisioning. The method constructs a Fuzzy-based Multidimensional Resource Scheduling model to obtain resource scheduling efficiency in cloud infrastructure. Increasing utilization of Virtual Machines through effective and fair load balancing is then achieved by dynamically selecting a request from a class using Multidimensional Queuing Load Optimization algorithm. A load balancing algorithm is then implemented to avoid underutilization and overutilization of resources, improving latency time for each class of request. Simulations were conducted to evaluate the effectiveness using Cloudsim simulator in cloud data centers and results shows that the proposed method achieves better performance in terms of average success rate, resource scheduling efficiency and response time. Simulation analysis shows that the method improves the resource scheduling efficiency by 7% and also reduces the response time by 35.5 % when compared to the state-of-the-art works.