基于虚拟机的自适应网格运行环境

网格应用必须适应动态变化的运行环境。该文探讨动态且自适应的资源管理模式,设计并实现一种面向服务的分布式虚拟机——Abacus虚拟机。根据资源管理策略与运行时的可用资源情况,Abacus虚拟机自适应地在分布式环境中为应用程序分配资源。实验结果显示,该自适应的资源管理方式是可行有效的。     

Adaptive middleware supporting scalable performance for high-end network services

Network service-based computation is a promising paradigm for both scientific and engineering, and enterprise computing. The network service allows users to focus on their application and obtain services when needed, simply by invoking the service across the network. In this paper, we show that an adaptive, general-purpose run-time infrastructure in support of effective resource management can be built for a wide range of high-end network services running in a single-site cluster and in a Grid. The primary components of the run-time infrastructure are: (1) dynamic performance prediction; (2) adaptive intra-site resource management; and (3) adaptive inter-site resource management. The novel aspect of our approach is that the run-time system is able to dynamically select the most appropriate performance predictor or resource management strategy over time. This capability not only improves the performance, but also makes the infrastructure reusable across different high-end services. To evaluate the effectiveness and applicability of our approach, we have transformed two different classes of high-end applications—data parallel and distributed applications—into network services using the infrastructure. The experimental results show that the network services running on the infrastructure significantly reduce the overall service times under dynamically varying circumstances.     

实时中间件自适应资源管理策略的分析与实现

本文介绍了一种分布式实时计算环境下自适应资源管理概念，提出了实时中间件的自适应资源管理策略和相关的实现机制，并给出了该策略的QoS模型和服务管理器层次结构     

云计算环境下的自适应资源管理技术综述

云环境下的自适应资源管理是当前云计算研究领域的热点问题,是云计算具备弹性扩展、动态分配和资源共享等特点的关键技术支撑,具有重要的理论意义和实用价值.其主要研究点包括:虚拟机放置优化算法,虚拟资源动态伸缩模型、多IDC间的全局云计算资源调度、全局资源配置及能力规划模型等.对云环境下自适应资源管理研究现状进行分析研究,并指出当前研究中存在的一些主要问题,同时进一步展望本领域未来的研究方向.     

Hierarchical control of multiple resources in distributed real-time and embedded systems

Real-time and embedded systems have traditionally been designed for closed environments where operating conditions, input workloads, and resource availability are known a priori, and are subject to little or no change at runtime. There is increasing demand, however, for adaptive capabilities in distributed real-time and embedded (DRE) systems that execute in open environments where system operational conditions, input workload, and resource availability cannot be characterized accurately a priori. A challenging problem faced by researchers and developers of such systems is devising effective adaptive resource management strategies that can meet end-to-end quality of service (QoS) requirements of applications. To address key resource management challenges of open DRE systems, this paper presents the Hierarchical Distributed Resource-management Architecture (HiDRA), which provides adaptive resource management using control techniques that adapt to workload fluctuations and resource availability for both bandwidth and processor utilization simultaneously. This paper presents three contributions to research in adaptive resource management for DRE systems. First, we describe the structure and functionality of HiDRA. Second, we present an analytical model of HiDRA that formalizes its control-theoretic behavior and presents analytical assurance of system performance. Third, we evaluate the performance of HiDRA via experiments on a representative DRE system that performs real-time distributed target tracking. Our analytical and empirical results indicate that HiDRA yields predictable, stable, and efficient system performance, even in the face of changing workload and resource availability.     

嵌入式自适应实时资源管理

现存的资源管理方法是针对传统的封闭嵌入实时系统设计的,在动态环境时难以保证系统的服务质量。为此,依据新一代典型动态实时系统的需求,运用基于控制论的自适应实时资源管理方法,改进Red Hat公司的eCos操作系统,在原eCos操作系统中加入基于控制论的实时调度架构、RM调度器、实时任务API和系统监视器,实现一个嵌入式自适应实时资源管理架构。实验结果证明,该架构具有更精确和高效的服务质量,保证系统实时动态的资源需求和实时任务的正确性。     

自适应实时资源管理技术研究*

分析了自适应实时资源管理面临的问题,研究了自适应实时资源管理的构造和常用模式,并指出其不足,最后提出一种基于控制论的实时资源管理模式,对动态实时系统进行自适应实时资源管理.通过建立抽象的实时资源装置,给出反馈控制、自适应控制和智能控制这三种基本模型,将控制论映射到实时资源管理,更好地满足实时系统的需求.     

面向SOA关键型系统的混合关键度驱动自适应资源管理

针对运行于开放、不确定环境下的SOA架构的多使命复杂关键型系统,提出了一种以混合关键度为驱动的动态分级自适应资源分配算法。它通过构造闭环反馈的分布式控制环,在线动态调整任务Qos,针对不同关键度的任务动态调整资源分配。该方法不但在统计意义上确保了关键型系统的实时性,而且实验证明该方法能有效提高系统在高负载条件下的实际资源利用率和降低关键任务的死线错过率,保证了关键型系统的高可靠性。     

Empirical prediction models for adaptive resource provisioning in the cloud

Cloud computing allows dynamic resource scaling for enterprise online transaction systems, one of the key characteristics that differentiates the cloud from the traditional computing paradigm. However, initializing a new virtual instance in a cloud is not instantaneous; cloud hosting platforms introduce several minutes delay in the hardware resource allocation. In this paper, we develop prediction-based resource measurement and provisioning strategies using Neural Network and Linear Regression to satisfy upcoming resource demands.Experimental results demonstrate that the proposed technique offers more adaptive resource management for applications hosted in the cloud environment, an important mechanism to achieve on-demand resource allocation in the cloud.     

Proactive virtualized resource management for service workflows in the cloud

A Cloud platform offers on-demand provisioning of virtualized resources and pay-per-use charge model to its hosted services to satisfy their fluctuating resource needs. Resource scaling in cloud is often carried out by specifying static rules or thresholds. As business processes and scientific jobs become more intricate and involve more components, traditional reactive or rule-based resource management methods are not able to meet the new requirements. In this paper, we extend our previous work on dynamically managing virtualized resources for service workflows in a cloud environment. Extensive experimental results of an adaptive resource management algorithm are reported. The algorithm makes resource management decisions based on predictive results and high level user specified thresholds. It is also able to coordinate resources among the component services of a workflow so that unnecessary resource allocations and terminations can be avoided. Based on observations from previous experiments, the algorithm is extended with a new resource merge strategy in order to prevent average resource size from shrinking. Simulation results from synthetic workload data demonstrated the effectiveness of the extension.     

Adaptive scheduling algorithm for media-optimized traffic management in software defined networks

Multi-policy resource management have been considered as an efficient methodology for delivering ready-to-use media-optimized applications in Software-Defined Networks (SDNs). Prioritized flow scheduling ensures high-speed communication in SDNs under large-scale distribution, heterogeneity of network resources, and exponential distribution of the flows granularity. The effectiveness of priority-based approaches depends usually on the control mechanism of the resource management. In this paper we improve the resource utilization by developing a novel adaptive scheduling strategy. We came with an effecting scheduling strategy to determine what resource to be allocated to a set of flows keeping their priority, increasing the average utilization of resources and, most importantly, establishing a virtual circuit for a specific flow over a network. Our theoretical remarks and extensive simulation results show that the proposed scheduling strategies can achieve the described goals.     

Adaptive mechanism-based congestion control for networked systems

In order to assure the communication quality in network systems with heavy traffic and limited bandwidth, a new ATRED (adaptive thresholds random early detection) congestion control algorithm is proposed for the congestion avoidance and resource management of network systems. Different to the traditional AQM (active queue management) algorithms, the control parameters of ATRED are not configured statically, but dynamically adjusted by the adaptive mechanism. By integrating with the adaptive strategy, ATRED alleviates the tuning difficulty of RED (random early detection) and shows a better control on the queue management, and achieve a more robust performance than RED under varying network conditions. Furthermore, a dynamic transmission control protocol–AQM control system using ATRED controller is introduced for the systematic analysis. It is proved that the stability of the network system can be guaranteed when the adaptive mechanism is finely designed. Simulation studies show the proposed ATRED algorithm achieves a good performance in varying network environments, which is superior to the RED and Gentle-RED algorithm, and providing more reliable service under varying network conditions.     

Comparison of a brain-based adaptive system and a manual adaptable system for invoking automation

OBJECTIVE: Two experiments are presented examining adaptive and adaptable methods for invoking automation. BACKGROUND: Empirical investigations of adaptive automation have focused on methods used to invoke automation or on automation-related performance implications. However, no research has addressed whether performance benefits associated with brain-based systems exceed those in which users have control over task allocations. METHOD: Participants performed monitoring and resource management tasks as well as a tracking task that shifted between automatic and manual modes. In the first experiment, participants worked with an adaptive system that used their electroencephalographic signals to switch the tracking task between automatic and manual modes. Participants were also divided between high- and low-reliability conditions for the system-monitoring task as well as high- and low-complacency potential. For the second experiment, participants operated an adaptable system that gave them manual control over task allocations. RESULTS: Results indicated increased situation awareness (SA) of gauge instrument settings for individuals high in complacency potential using the adaptive system. In addition, participants who had control over automation performed more poorly on the resource management task and reported higher levels of workload. A comparison between systems also revealed enhanced SA of gauge instrument settings and decreased workload in the adaptive condition. CONCLUSION: The present results suggest that brain-based adaptive automation systems may enhance perceptual level SA while reducing mental workload relative to systems requiring user-initiated control. APPLICATION: Potential applications include automated systems for which operator monitoring performance and high-workload conditions are of concern.     

图书馆多媒体信息资源网络的自适应传输控制方法

为了增强图书馆多媒体信息的稳健传输和多媒体信息网络的鲁棒性、稳定性,提出了一种新颖的RED策略。最小阈值(minth)和最大阈值(maxth)能够随着缓存队列的平均占用率的变化动态调整,而不再是预设的固定参数。改进后,ATRED对路由器队列的控制力度能够根据网络环境的变化自适应调整,使得队长方差减小,队列振荡变小,网络更加稳定,能够在复杂多变的网络环境下提供更加可靠的服务。     

基于资源管理的信息融合系统工程的研究

基于资源管理的信息融合系统开发是信息融合系统工程相关技术发展的新方向.资源管理问题贯穿信息融合整个过程,在对信息融合中的资源管理问题分析的基础上,提出并分析了基于资源管理的功能模型,并在此基础上进一步分析了将资源管理用于信息融合的DNN结构,该结构形式简单易于操作,为后续操作提供了便利.在此基础上,对基于资源管理的信息融合系统工程方法进行了研究,给出了基于资源管理的信息融合系统工程过程,有助于提高信息融合工程的经济性,并对自适应信息融合的实现、多级数据融合的协调具有重要意义.     

QoS映射的区间映射模型

提出一种新的QoS映射模型一区间映射，该映射模型可以将应用层参数映射为传输层参数的一个区间范围，解决了传统映射方法不能适应不同用户的使用需求，或不能适应网络动态变化的缺点，为在开放网络中实施自适应QoS保障打下了基础。     

Adaptive resource management for dynamic distributed real-time applications

The dynamic distributed real-time applications run on clusters with varying execution time, so re-allocation of resources is critical to meet the applications’s deadline. In this paper we present two adaptive recourse management techniques for dynamic real-time applications by employing the prediction of responses of real-time tasks that operate in time sharing environment and run-time analysis of scheduling policies. Prediction of response time for resource reallocation is accomplished by historical profiling of applications’ resource usage to estimate resource requirements on the target machine and a probabilistic approach is applied for calculating the queuing delay that a process will experience on distributed hosts. Results show that as compared to statistical and worst-case approaches, our technique uses system resource more efficiently.     

基于IP网络的自适应QoS管理方案研究

目前实时音频、视频多媒体应用已经开始进入IP网络,但还有许多问题没有得到很好地解决,其中一个关键问题是多媒体服务的QoS问题、TCP／IP协议本身只提供一种“Best-effort”级别的服务,对QoS支持很少,“Best-effort”级别的服务往往会导致实时应用出现延迟抖动、分组丢失率高,从而极大的影响了实时应用的运行效果,因此必须研究可行的、高效的基于IP网络的QoS控制机制。IP网络QoS已成为分布式多媒体和网络通信的重要研究热点和难点课题。本文在IEＴＦ Intserv与Diffserv相结合的体系结构基础上,提出了一种适用于IP网络的自适应QoS管理框架,与Intserv或Diffserv模型不同的是,该QoS管理框架引入优先级节和自适应概念,QoS俦权处理采用基于多媒体优先级节的算法,在传输控制上采用了自适应QoS控制算法。它在应用层上完成,因此独立于底层网络协议。本文首先讨论了基本概念和函数,然后提出一种基于优先级节的自适应QoS管理框架和优先级调度流程。提出一种基于IP的自适应QoS协商机制,基本思想是基于RSVP（资源预留协议）。在提出QoS请求时进行QoS映射,然后启动适应性函数和资源管理函数进行协商,直到获得一组在当前资源状况下最佳的QoS指标。最后,本文还讨论了此方案的有效性并在自行开发的实验平台GUT上进行的QoS实验。实验表明应用该方法可以根据网络当前状态自适应地整多媒体实时应用的QoS需求。     

元数据驱动的分布式数据资源管理技术

采用元数据驱动方法,研究分布式数据资源管理,实现数据的统一组织,提供高效数据共享服务。在对数据进行分类分析基础上,基于元数据定义和管理,开展分布式数据管理的数据检索和缓存。突破分布式数据资源管理关键技术瓶颈,构建分布式资源全局目录,实现目录服务,进行高效传输的数据缓存管理,减少处理、采集、分发的延时。提供基于多副本的动态自适应数据调度方案,解决分布式数据资源管理的数据选择和动态变化问题。     

An optimization-based adaptive resource management framework for economic Grids: A switching mechanism

The application of Grid computing has been broadening day by day. An increasing number of users has led to the requirement of a job scheduling process, which can benefit them through optimizing their utility functions. On the other hand, resource providers are exploring strategies suitable for economically efficient resource allocation so that they can maximize their profit through satisfying more users. In such a scenario, economic-based resource management strategies (economic models) have been found to be compelling to satisfy both communities. However, existing research has identified that different economic models are suitable for different scenarios in Grid computing. The Grid application and resource models are typically very dynamic, making it challenging for a particular model for delivering stable performance all the time. In this work, our focus is to develop an adaptive resource management architecture capable of dealing with multiple models based on the models’ domains of strengths (DOS). Our preliminary results show promising outcomes if we consider multiple models rather than relying on a single model throughout the life cycle of a Grid.