Three-layer control policy for grid resource management

The paper proposes a three-layer control policy for grid resource management, which applies a bottom-up approach to dynamic resource management. In order to hierarchically allocate resources in the system to maximize system utility, different controllers are deployed at three levels: local controller, group controller, and global controller. Global control considers all applications and coordinates three layers of grid architecture in response to large system changes at coarse time granularity. While local control adapts a single application to small changes at fine granularity. Global control exploits the interlayer coupling of fabric layer, collective layer and application layer to perform less frequent and effective control actions for a system-wide optimization. The interaction between grid layers is controlled through the use of the pricing variable, which coordinates the user demand at the application layer and supply of resources at the fabric layer. The paper proposes pricing-based three-layer control algorithms. This paper demonstrates the benefits of the control algorithm through simulations.     

Hierarchical control policy for dynamic resource management in grid virtual organization

This paper proposes a hierarchical control system in grid virtual organization. The hierarchical system can be decomposed into multiple application groups, which can be further decomposed into multiple applications. At the top of the hierarchy, the global controller controls the gross allocation of resources to the groups. At the next level down, the group controller coordinates the local deployments of all applications that consume the local allocation of resources. At the lowest level, the local controllers adjust the local resource usages to optimize the utility of single application. The hierarchical control system considers all applications and coordinates all layers of grid architecture upon any changes. According to different time granularity, we adopt a different control scheme. The global control considers all applications and coordinates three layers of grid architecture in response to large system changes at coarse time granularity, while local control adapts a single application to small changes at fine granularity. This paper adopts utility-driven cross layer optimization for grid applications to find a system wide optimization and solves the cross-layer optimization by using pricing based decomposition. A set of hierarchical utility functions is used to measure the performance of the grid system that follows the system, group and application hierarchy. This paper uses total utility to measure the overall quality of grid system. The experiments are conducted to test the performance of the hierarchical control algorithms.

Multi-layer optimization in service-oriented sensor grid

Sensor devices such as video cameras, infrared sensors and microphones are being widely exploited in grid application. The paper deals with multi-layer optimization in service oriented sensor grid to optimize utility function of sensor grid, subject to resource constraints at resource layer, service composition constraints at service layer and user preferences constraints at application layer respectively. The multi-layer optimization problem can be decomposed into three subproblems: sensor grid resource allocation problem, service composing problem, and user satisfaction degree maximization problem, all of which interact through the optimal variables for capacities of sensor grid resources and service demand. The proposed algorithm decomposes global sensor grid optimization problem into a sequence of three sub-problems at three layers via an iterative algorithm. The simulations are conducted to validate the efficiency of the multi-layer optimization algorithm. The experiments compare the performance of the multi-layer global optimization approach with application layer local optimization and resource layer local optimization approach respectively.     

Agent based sensors resource allocation in sensor grid

Sensor enabled grid may combine real time data about physical environment with vast computational resources derived from the grid architecture. One of the major challenges of designing a sensor enabled grid is how to efficiently schedule sensor resource to user jobs across the collection of sensor resources. The paper presents an agent based scheme for assigning sensor resources to appropriate sensor grid users on the basis of negotiation results among agents. The proposed model consists of two types of agents: the sensor resource agents that represent the economic interests of the underlying sensor resource providers of the sensor grid and the sensor user agents that represent the interests of grid user application using the grid to achieve goals. Interactions between the two agent types are mediated by means of market mechanisms. We model sensor allocation problems by introducing the sensor utility function. The goal is to find a sensor resource allocation that maximizes the total profit. This paper proposes a distributed optimal sensor resource allocation algorithm. The performance evaluation of proposed algorithm is evaluated and compared with other resource allocation algorithms for sensor grid. The paper also gives the application example of proposed approach.     

A behavior-based intelligent control architecture with applicationto coordination of multiple underwater vehicles

Presents a behavior-based intelligent control architecture for designing controllers which, based on their observation of sensor signals, compute the discrete control actions. These control actions then serve as the “set-points” for the lower level controllers. The behavior-based approach yields an intelligent controller which is a cascade of a perceptor and a response controller. The perceptor extracts the relevant symbolic information from the incoming continuous sensor signals, which enables the execution of one of the behaviors. The response controller is a discrete event system that computes the discrete control actions by executing one of the enabled behaviors. The behavioral approach additionally yields a hierarchical two layered response controller, which provides better complexity management. The inputs from the perceptor are used to first compute the higher level activities, called behaviors, and next to compute the corresponding lower level activities, called actions. The paper focuses on the discrete event subsystem, namely, the response controller. We illustrate the intelligent control architecture by discussing its application to the design of intelligent controllers for autonomous underwater vehicles used for ocean sampling missions. A complete set of discrete event models of the response controller of the underwater vehicles for the above application has been given, and their formal verification discussed     

Optimization decomposition approach for layered QoS scheduling in grid computing

The paper presents optimization decomposition based layered Quality of Service (QoS) scheduling for computational grid. Cross layer joint QoS scheduling is studied by considering the global problem as decomposed into three sub-problems: resource allocation at the fabric layer, service composing at the collective layer, and user satisfaction degree at the application layer. The paper proposes a complete solution from optimization modeling, Lagrange relaxation based decomposition, to solutions for each sub-problem Lagrange relaxation based decomposition. These aspects match the vertical organization of the considered grid system: each layer trade with adjacent layers to find a global optimum of the whole grid system. Through multi-layer QoS joint optimization approach, grid global QoS optimization can be achieved. The cross layer policy produces an optimal set of grid resources, service compositions, and user’s payments at the fabric layer, collective layer and application layer, respectively, to maximize global grid QoS. The owner of each layer obtains inputs from other layers, tries to maximize its own utility and provides outputs back to other layers. This iterative process lasts until presumably all layers arrive at the same solution.     

基于FPGA的传感器网络数据采集传输系统设计

针对目前传感器网络数据采集系统采用顺序处理器件做为控制器,造成数据采集效率低、实时性不能满足设计环境需求、安全控制机制有限、系统设计复杂等问题,提出在硬件上采用可灵活设计且片内资源丰富的FPGA作为设计载体,协议上采用物理层和应用层具备一定扩展能力的分层式实时传感器网络通信协议（HRTSN,Hierachical-Real—TJmeSensorNetwork）实现的传感器网络数据采集系统。通过建立多层安全控制机制,有效提高了系统对传感器网络可靠性的要求和实时事件的响应处理能力。     

Multi-level scheduling for global optimization in grid computing

The paper presents a multi-level scheduling algorithm for global optimization in grid computing. This algorithm provides a global optimization through a cross-layer optimization realized by decomposing the optimization problem in different sub-problems each of them corresponding to one among the grid layers such as application layer, collective layer and fabric layer. The QoS of an abstraction level is a utility function that assigns at every level a different value and that depends on the kind of task that is executed on the grid. The global QoS is given by processing of the utility function values of the three different levels, using the Lagrangian method. Multi-level QoS scheduling algorithm is evaluated in terms of system efficiency and their economic efficiency, respectively. Economic efficiency includes user utility, service provider’s revenue and grid global utility. System efficiency includes execution success ratio and resource allocation ratio.     

Granular computing in the development of fuzzy controllers

This study elaborates on the role of information granularity in the development of fuzzy controllers. As opposed to numeric data being commonly accepted by fuzzy controllers, we discuss a general processing framework involving data-information granules exhibiting various levels of information granularity. The paper analyzes an impact of information granularity on the performance of the controller. We study a way in which information granules arise in control problems, elaborate on a way of describing these granules as well as provide a way of quantifying the level of information granularity. A number of analysis and design issues are studied including robustness of the fuzzy controller, representation of linguistic information and quantification of its granularity. Nonlinear characteristics of the compiled version of the fuzzy controller operating in presence of granular information are discussed in detail. Illustrative numerical examples are provided as well. ©1999 John Wiley & Sons, Inc.     

基于通用内模的变频电网有源电力滤波器自适应重复控制

为了提高变频电网并联型有源电力滤波器(APF)的电流控制性能,针对谐波源的多样性和负载状态的变化,提出了一种通用内模,然后,结合两次坐标变换,通过内模p值的自适应调节,建立了基于通用内模的重复控制器.这种控制器,能够根据电网或负载状态,调整动态响应时间和补偿范围.这样就解决了谐波源或负载的变化引起的补偿效果降低的问题.针对电网频率的大范围变化,又引入了周期自适应参数.为了弥补重复控制动态延迟的缺陷,将并联比例控制器组成复合控制系统,并对该系统的主要特性进行了分析.在飞机变频交流电网并联APF系统中,首先将所提重复控制方法与其他四种方法了进行仿真对比,最后进行了实验验证.仿真和实验结果证明了基于通用内模的复合重复控制策略的有效性和优越性.     

网格环境中资源发现机制的研究

资源发现机制是关系到广域分布式环境中资源共享和协同工作效率的关键，在计算网格、Web服务和P2P技术中，都需要对这个问题进行深入研究，在织女星网格前期工作的基础上，讨论了资源发现机制中的核心问题，描述了织女星网格资源发现机制的层次结构以及各个层次的功能和实现，通过分析和实验，可以认为提出的资源发现机制具有以下特点：可在较小的开销下取得满意的资源定位性能，并具有网格环境下的可扩展性；能够适应网格资源动态变化的特性；能够解决资源发现过程中的负载平衡问题；可支持多种网格资源与应用。     

一种新的能量有效性无线传感网节点设计

当无线传感网运用于恶劣、时变的环境中,如何使网络的生命周期最大化,并满足一定QoS要求,成为无线传感网在这些应用领域广泛应用的关键问题。主要提出了一种基于直扩通信体制的双内核节点架构,不仅能满足复杂环境中的通信性能要求,并且功耗较低。该节点的设计主要采用了软硬件相结合的动态能量管理策略以及参数可调的软件无线电设计,通过底层的灵活设计为无线传感网各层协议的设计提供了一个良好的测试和实践平台,从而研究、设计新的物理层及上层协议来减少非理想硬件平台的功耗,提高整个网络的生存时间和通信性能。     

推荐共享机制在网格资源查找中的应用

根据网格环境特点,结合现有网格以及其他分布式环境下资源查找的特点,提出了网格资源查找机制的分层框架,将网格中的资源组织为网格虚拟社区和资源两个基本层次,建立一种分层网格框架,按照资源属性,对网格进行逻辑划分。在此分层框架的基础上,根据小世界模型,提出一种基于推荐的资源信息共享机制,即通过第三方结点,将原本无关系的而需求相近的结点联系起来,共享各自的资源池信息。通过模拟仿真,和Globus MDS进行资源查找速度和匹配程度的比较,证明了该策略的高效性。     

PLC在智能装置自动测试系统的研究和应用

可编程控制器在工业控制领域应用广泛,本文从可编程控制器在电力系统智能装置自动检测系统应用出发,介绍自动检测系统设计原理和系统组成,详细介绍PLC在本系统功能和具体应用范围,并针对可编程控制器应用中出现特殊问题进行分析,并给出解决方法。实践证明,可编程控制器系统的扩展性和可编程特点可有效满足部分控制系统的特殊要求。     

Sensor Resource Management driven by threat projection and priorities

This paper extends previous Sensor Resource Management (SRM) work by addressing information flow from sensor inputs to SRM, through four levels of the US DoD’s Joint Directors of Laboratories (JDL) sensor fusion model. The method flexibly adapts to several domains/problems. Human situation awareness information needs are linked to sensor control in a manner similar to perception management. The key to effective integration of JDL levels is the timely determination of the highest priorities via threat projection accomplished via Probabilistic Accumulative Situation Calculus (PASC), which quantifies threat intent using an appropriate level of automated context-based reasoning. The accuracy of the threat projection is improved over time using self-learning techniques. The multiple sensor system levels are unified primarily using the structure of quantified priorities. Algorithms are presented for a radar sensor resource allocation and adjustment method in which the dwell time per track parameter is the key radar sensor resource to be managed. A developed application of the method to an Integrated Air Defense System (IADS) sensor system problem is detailed, with simulation results shown to demonstrate the effectiveness of the method.     

An efficient coordination scheme between layers of grid architecture

Grid architecture is protocol architecture, with protocols defining the basic mechanisms by which virtual organization (VO) users and resources negotiate, establish, manage, and exploit sharing relationships. A standard based open architecture facilitates extensibility, interoperability, portability, and code sharing; standard protocols make it easy to define standard services that provide enhanced capabilities. Grid architecture organizes into layers. In the paper, a three-layer coordination of grid architecture is proposed. The aim is to optimize the end-to-end quality of the dynamic grid application as well as efficiently utilizing the grid resources. A joint coordination algorithm, which exploits the interlayer coupling of fabric layer, collective layer and application layer, is proposed. The algorithm aims to achieve a system-wide optimization based on the user's preferences. We formulate the integrated coordination of three layers into a constrained optimization problem. Utility functions are used to express grid user' preferences, resource provider's benefit function and system's objectives. The optimization framework provides a layered approach to the sum utility maximization problem. The advantage of the proposed joint three-layer coordination is demonstrated through simulations.     

Research on resource allocation for multi-tier web applications in a virtualization environment

Resource allocation for multi-tier web applications in virtualization environments is one of the most important problems in autonomous computing. On one hand, the more resources that are provisioned to a multitier web application, the easier it is to meet service level objectives (SLO). On the other hand, the virtual machine which hosts the multi-tier web application needs to be consolidated as much as possible in order to maintain high resource utilization. This paper presents an adaptive resource controller which consists of a feedback utilization controller and an auto-regressive and moving average model (ARMA)-based model estimator. It can meet application-level quality of service (QoS) goals while achieving high resource utilization. To evaluate the proposed controllers, simulations are performed on a testbed simulating a virtual data center using Xen virtual machines. Experimental results indicate that the controllers can improve CPU utilization and make the best tradeoff between resource utilization and performance for multi-tier web applications.     

Scheduler Modeling Based on the Controller Synthesis Paradigm

The controller synthesis paradigm provides a general framework for scheduling real-time applications. Schedulers can be considered as controllers of the applications; they restrict their behavior so that given scheduling requirements are met. We study a modeling methodology based on the controller synthesis paradigm. The methodology allows to get a correctly scheduled system from timed models of its processes in an incremental manner, by application of composability results which simplify schedulability analysis. It consists in restricting successively the system to be scheduled by application of constraints defined from scheduling requirements. The latter are a conjunction of schedulability requirements that express timing properties of the processes and policy requirements about resource management. The presented methodology allows a unified view of scheduling theory and approaches based on timing analysis of models of real-time applications.     

Rate control for heterogeneous wireless sensor networks: Characterization,algorithms and performance

This paper addresses the rate control and resource allocation problem for heterogeneous wireless sensor networks, which consist of diverse node types or modalities such as sensors and actuators, and different tasks or applications. The performance of these applications, either elastic traffic nature (e.g., typical data collection) or inelastic traffic nature (e.g., real-time monitoring and controlling), is modeled as a utility function of the sensor source rate. The traditional rate control approach, which requires the utility function to be strictly concave, is no longer applicable because of the involvement of inelastic traffic. Therefore, we develop a utility framework of rate control for heterogeneous wireless sensor networks with single- and multiple-path routing, and propose utility fair rate control algorithms, that are able to allocate the resources (wireless channel capacity and sensor node energy) efficiently and guarantee the application performance in a utility proportional or max–min fair manner. Furthermore, the optimization and convergence of the algorithm is investigated rigorously as well.