自律计算评估技术研究

自律计算的研究还处于初级阶段,具体设计和实现过程还不完善.自律评估是自律计算研究组成部分,对自律计算系统的实现和完善起着重要作用,国内外已有机构和学者对其进行深入研究并取得了一些成果.本文主要对国内外现有的自律计算的评估研究成果进行分类,时其评估能力的有效性和完备性进行分析,探讨影响评估研究的制约因素,最后总结评估研究领域存在问题,并展望该领域未来的发展方向.     

基于自律计算的故障监视机制研究与设计

自律计算是分布式异构环境下进行资源自动化管理的有效技术.其目的是通过系统的自我监视,主动发现硬件故障和软件故障,并采用策略技术加以修复,完成系统的自我管理.因此,故障监视是自律计算中较为重要的一个研究方向,但目前尚欠缺有效可行的方法来完成自律系统的故障监视.提出了一种分布式异构环境下基于事件分类的方法来设计自律计算系统故障监视机制,以统一监视管理异构资源的故障,并与自律系统互联通报,激活相应的策略来修复故障,为自律系统的自我修复提供依据.     

基于自律计算的多数据点闭值检测方法

自律计算在实施过程中通常采用阂值来标记性能故障。阂值是系统的性能计数器的边界值,系统自动检测这些边界值。若某个设备的性能指标达到这些边界值,则表示系统的某台设备出现了性能故障,自律系统的自主管理器便会主动采取策略来修复性能故障,从而保证系统一直处于稳定的工作状态。目前,自律计算领域欠缺对于系统性能故障的研究。在研究自律计算自我监视的基础上,提出了一种多数据点的阂值检测方法,即通过多次检测阂值的越界和恢复来判断系统是否出现性能故障,以保证检测的有效性,为系统采取策略修复故障提供有利的依据。     

一种面向服务器集群的自律计算模型

针对服务器集群系统无法快速部署和维护成本过高的问题，在研究了自律计算和服务器集群系统的特点的基础上，提出了一种基于自律计算的集群部署和管理模型，阐述了模型系统构成和各个模块的功能并提出了自律管理方案，通过系统部署的自动化以及排除操作错误等，削减应用成本从而实现系统的自我管理。     

一种基于WBEM的自律计算模型

针对现有的自律计算系统不能管理混合资源的问题,在分析WBEM的技术体系结构的基础上,提出一种基于WBEM的自律计算模型,该模型在统一管理各种异构的网络资源的基础上,加入策略支持,从而具有自我决策和自我管理能力,能够进行实时的故障监视,在故障发生时进行灵活的资源替换,完成自我诊断、自我配置、自我修复等自律管理功能。阐述了模型各部分的功能,给出基于策略的自律管理方法,实现了系统的自律计算特征和网络资源的统一管理。     

一种云环境下基于分级管理的自律计算模型

自律计算是实现复杂IT系统的自我修复、自我管理的有效手段。但是在云环境下,系统所管理的计算资源数量急剧增长,传统的资源调度算法和模型架构无法对资源进行合理分配,导致资源的利用率很低。为了提高云环境下的资源管理效率,提出了一种面向云环境的自律计算模型,它采用多自主管理器的分级管理模式来解决传统自律计算模型在处理大量请求时的瓶颈问题;并提出了混合策略管理模式来应对不同的故障修复请求。该模型能够降低云环境下的资源管理成本,满足云服务所需的SLA。     

服务器系统管理的自律计算模型

利用时序模型算法和事件分类匹配算法对服务器系统多个节点的事件和系统参数进行分析与预测，结合策略库技术, 构建了一个服务器系统管理的自律计算模型。该模型能够根据已知的操作环境自我配置，在各种灾难发生前自我保护，发生后自我修复，并且可以自我优化。     

基于自律计算的入侵容忍模型

针对当前入侵容忍系统缺乏自适应能力的问题,借鉴自律计算的思想,提出了一个自律容侵模型。利用自律反馈机制对网络连接的可信度进行实时分析,生成初始信度,并通过信度阈值的自主学习与调整实施动态信度优化,完成对可疑信息和可信信息的服务分流,实现对可疑连接的自律容忍。仿真结果表明,具有自律反馈机制的入侵容忍模型可以有效增强系统的自适应能力。     

自律入侵容忍系统的可生存性评估

现有网络系统的评估方法不能很好地满足自律入侵容忍系统可生存性的评估要求。为此,提出一种针对自律入侵容忍系统的可生存性评估方法。根据入侵对系统造成的影响及自律入侵容忍的功能,构建数据机密度、数据完整度、服务可用度和系统自律度4项可生存性评估指标。给出各项指标的量化方法,通过对4项指标的定量计算,动态评估自律入侵容忍系统的可生存性。     

基于IA架构的高性能计算平台的研究

目前众多领域对大规模计算逐渐显示出一种迫切的需求,集群技术在高性能计算领域中的理论研究和实践越来越得到广泛的关注。高性能计算是解决大规模聚变模拟计算的有效途径。本文介绍如何基于集群技术构建聚变研究高性能计算系统,分析了系统的基本体系结构,从硬件和软件两个方面详细解析其高性价比的构建技术。     

局部域中构件服务自主调度模型的设计与实现

自主计算环境(ACE)是一个可自主管理的大规模分布式网络应用软件部署、运行的基础架构。环境中的任务可以被看成为由若干个被抽象为构件的服务所组成，任务的完成依赖于各个构件服务的最终调度运行状况。借鉴自主计算的思想，设计并实现了自主计算环境下局部管理域中构件服务的自主调度模型。该模型利用Java/Jini相关技术实现，并给出了构件的自主调度算法。     

Concurrency of self-* in autonomic systems

New computing systems are currently at crucial point in their evolution: autonomic systems (ASs), which are inspired by the human autonomic nervous system. Autonomic computing (AC) is characterized by self-*such as self-configuration, self-healing, self-optimization, self-protection and more which run simultaneously in ASs. Hence, self-*is a form of concurrent processing in ASs. Taking advantage of categorical structures we establish, in this paper, a firm formal basis for specifying concurrency of self-* in ASs.     

面向自主计算的主体服务匹配：研究综述

主体服务能力描述和能力匹配是实现自主单元自配置的关键,也是基于主体的自主计算技术中的核心内容.在简要介绍自主计算基础上,重点对主体服务描述语言和主体服务匹配算法的研究成果进行了综述,并指出未来的发展趋势.     

An autonomic cloud environment for hosting ECG data analysis services

Advances in sensor technology, personal mobile devices, wireless broadband communications, and Cloud computing are enabling real-time collection and dissemination of personal health data to patients and health-care professionals anytime and from anywhere. Personal mobile devices, such as PDAs and mobile phones, are becoming more powerful in terms of processing capabilities and information management and play a major role in peoples daily lives. This technological advancement has led us to design a real-time health monitoring and analysis system that is Scalable and Economical for people who require frequent monitoring of their health. In this paper, we focus on the design aspects of an autonomic Cloud environment that collects peoples health data and disseminates them to a Cloud-based information repository and facilitates analysis on the data using software services hosted in the Cloud. To evaluate the software design we have developed a prototype system that we use as an experimental testbed on a specific use case, namely, the collection of electrocardiogram (ECG) data obtained at real-time from volunteers to perform basic ECG beat analysis.     

自主计算环境

自主计算技术是为了解决软件复杂性危机而提出的一门新兴的研究课题。该文分析了目前自主计算研究的现状和不足，提出了一种自主计算系统的建模方法，给出了自主单元和自主计算系统体系结构，并在该结构的基础上实现了自主计算环境ACE。     

MyAds: A system for adaptive pervasive advertisements

In this paper we show how pervasive technologies can be employed on a public-display advertisement scenario to enable behavioral self-adaptation of content. We show this through MyAds, a system capable of exploiting pervasive technologies to autonomously adapt the advertisement process to the trends of interests detected among the audience in a venue. After describing the rationale, the architecture and the prototype of MyAds, we describe the advantages brought by the use of such a system, in terms of impact on the audience and economic efficiency. The comparison of MyAds performances with different advertisement selection techniques confirms the validity of our advertisement model, and our prototype in particular, as a means for maximising product awareness in an audience and for enhancing economic efficiency.     

An approach to implement data fusion techniques in wireless sensor networks using genetic machine learning algorithms

Wireless Sensor Networks (WSNs) can be used to monitor hazardous and inaccessible areas. In these situations, the power supply (e.g. battery) of each node cannot be easily replaced. One solution to deal with the limited capacity of current power supplies is to deploy a large number of sensor nodes, since the lifetime and dependability of the network will increase through cooperation among nodes. Applications on WSN may also have other concerns, such as meeting temporal deadlines on message transmissions and maximizing the quality of information. Data fusion is a well-known technique that can be useful for the enhancement of data quality and for the maximization of WSN lifetime. In this paper, we propose an approach that allows the implementation of parallel data fusion techniques in IEEE 802.15.4 networks. One of the main advantages of the proposed approach is that it enables a trade-off between different user-defined metrics through the use of a genetic machine learning algorithm. Simulations and field experiments performed in different communication scenarios highlight significant improvements when compared with, for instance, the Gur Game approach or the implementation of conventional periodic communication techniques over IEEE 802.15.4 networks.     

Efficient autonomic cloud computing using online discrete event simulation

Interest is growing in open source tools that let organizations build IaaS clouds using their own internal infrastructures, alone or in conjunction with external ones. A key component in such private/hybrid clouds is virtual infrastructure management, i.e., the dynamic orchestration of virtual machines, based on the understanding and prediction of performance at scale, with uncertain workloads and frequent node failures. Part of the research community is trying to solve this and other IaaS problems looking to Autonomic Computing techniques, that can provide, for example, better management of energy consumption, quality of service (QoS), and unpredictable system behaviors. In this context, we first recall the main features of the NAM framework devoted to the design of distributed autonomic systems. Then we illustrate the organization and policies of a NAM-based Workload Manager, focusing on one of its components, the Capacity Planner. We show that, when it is not possible to obtain optimal energy-aware plans analytically, sub-optimal plans can be autonomically obtained using online discrete event simulation. Specifically, the proposed approach allows to cope with a broader range of working conditions and types of workloads.