基于Z的协议形式描述与验证

一、引言在过去的十年.计算机网络与分布式系统已取得了很大的进展[6j。步入九十年代,各种新型通信技术和分布式应用已相继出现.并已对计算机通信软件发生了重要影响。这些新技术及应用主要包括高速光纤网、多媒体通信、宽带综合业务数字网(B一IsDN)、智能网络技术.如智能服务器、智能路由选择、智能协议开发环境等.以及综合语音、数据、图文和图象服务等等。为了适应这种形势的发展一门新兴的学科一协议工程已应运而生川。     

计算机网络互连模式及实现技术

本文讨论了计算机网络互连的体系结构模式及实现技术。文中先论述了ＩＳＯ、ＤＯＤ和Ｐｕｐ网际互连参考模式，然后讨论了网络互连的主要实现技术：互连层，网际寻址，网际路由选择，信包分拆与重装，网际流控与拥挤控制，网际保安与差错控制，网际信关与桥以及协议转换等技术。     

Energy constrained resource allocation optimization for mobile grids

A mobile grid incorporates mobile devices into Grid systems. But mobile devices at present have severe limitations in terms of processing, memory capabilities and energy. Minimizing the energy usage in mobile devices poses significant challenges in mobile grids. This paper presents energy constrained resource allocation optimization for mobile grids. The goal of the paper is not only to reduce energy consumption, but also to improve the application utility in a mobile grid environment with a limited energy charge, ensuring battery lifetime and the deadlines of the grid applications. The application utility not only depends on its allocated resources including computation and communication resources, but also on the consumed energy, this leads to a coupled utility model, where the utilities are functions of allocated resources and consumed energy. Energy constrained resources allocation optimization is formulated as a utility optimization problem, which can be decomposed into two subproblems, the interaction between the two sub-problems is controlled through the use of a pricing variable. The paper proposes a price-based distributed energy constrained resources allocation optimization algorithm. In the simulation, the performance evaluation of our energy constrained resources allocation optimization algorithm is conducted.     

A flexible layered control policy for resource allocation in a sensor grid

The paper proposes a flexible layered control policy for sensor resource allocation in a sensor grid. In order to allocate sensor resources in the system to maximize the sensor grid utility, different controllers are deployed at three levels: a job-level controller, an application group controller, and a sensor grid system controller. At the lowest levels, job-level controllers perform fast, frequent, local adaptation for optimizing a single sensor grid application at a time, while, at the highest levels, sensor grid system controllers perform less frequent control actions to optimize all applications. Sensor grid system control considers all sensor grid applications in response to large system changes at coarse time granularity. Sensor grid system control exploits the interlayer coupling of the resource layer and the application layer to achieve a system-wide optimization based on the sensor grid users’ preferences. Job-level control adapts a single application to small changes at fine granularity. The layered control system uses a set of utility functions to evaluate the performance of sensor grid applications and groups. The control system chooses control actions that would result in a higher level of utility. In the simulation, a performance evaluation of the algorithm is carried out.     

A resource selection scheme for QoS satisfaction and load balancing in ad hoc grid

Ad hoc grids are highly heterogeneous and dynamic, in which the availability of resources and tasks may change at any time. The paper proposes a utility based resource selection scheme for QoS satisfaction and load balancing in ad hoc grid environments. The proposed scheme intends to maximize the QoS satisfaction of ad hoc grid users and support load balancing of grid resources. For each candidate ad hoc grid resource, the scheme obtains values from the computations of utility function for QoS satisfaction and benefit maximization game for ad hoc grid resource preference. The utility function for QoS satisfaction computes the utility value based on the satisfaction of QoS requirements of the grid user request. The benefit maximization game for grid resource node preference computes the preference value from the resource point of view. Its main goal is to achieve load balancing and decrease the number of resource selection failure. The utility value and the preference value of each candidate ad hoc grid resource are combined to select the most suitable grid resource for ad hoc grid user request. In the simulation, the performance evaluation of proposed algorithm for ad hoc grid is conducted.     

移动Ad Hoc网络多播路由协议的研究进展

移动AdHoc网络是一个自组织、移动节点通过无线链路组成的动态拓扑变化的网络.由于网络规模小、无基础设施、构建迅速等特点,从而广泛用于紧急事件、军事和民用领域以及多媒体应用等.随着网络应用规模的增长,在移动AdHoc网络中支持多播路由成为网络领域中一类重要的研究课题.该文综述了移动AdHoc网络多播路由方面的一些最新工作,论述了设计移动AdHoc网络多播路由协议的特性、问题和技术,详细描述和比较了目前典型的移动AdHoc网络多播路由协议,为进一步的应用和研究提出了新的课题.     

一种多类支持向量机概率建模新方法

在支持向量机多类分类问题输出概率建模中,提出了一种直接求解后验概率的概率建模新方法。在对多个两类支持向量机分类器的输出概率进行组合时,该方法充分考虑了各个两类支持向量机分类器的差异,并以后验概率作为各个两类支持向量机分类器的权系数。仿真图像的实验结果表明,该文提出的直接求解后验概率方法与投票法及Pairwise Coupling方法相比,不仅具有较好的分类性能,而且得到的后验概率具有较好的概率分布形态。     

Joint QoS optimization for layered computational grid

Many existing grid resource allocation and scheduling algorithms mainly focus on isolated layers of the grid architecture. The inflexibility of the strict layering structure results in an inefficient utilization of the grid resources. This paper takes a system view of the computational grid and aims to jointly optimize global QoS by adopting cross-layer design. Cross-layer design is based on information exchange and joint optimization among multiple grid layers. Parameters from different layers are provided to a cross-layer optimizer, which selects the values of the layer specific parameters maximizing joint global QoS. The objective of the paper is to jointly optimize the parameters of all layers in a decentralized optimization problem and decompose joint QoS optimization into three sub problems at fabric layer, collective layer and application layer. In simulation part, we compare the performance of the global joint QoS optimization approach with application layer local optimization and resource layer local optimization approach, respectively.     

Utility-based QoS optimisation strategy for multi-criteria scheduling on the grid

The paper presents quality of service (QoS) optimisation strategy for multi-criteria scheduling on the grid, based on a mathematical QoS model and a distributed iterative algorithm. Three QoS criteria are considered, namely payment, deadline and reliability, which are formulated as utility function. The optimisation problem is split into two parts: task optimisation performed on behalf of the user and resource optimisation performed on behalf of the grid. The strategy employs three types of agents: task agents responsible for task optimisation, computation resource and network resource agents responsible for resource optimisation. The agents apply economic models for optimisation purposes. Dynamic programming is used to optimise the total system utility function in terms of an iterative algorithm. The objective of multi-criteria scheduling is to maximise the global utility of the system. This paper proposes an iterative scheduling algorithm that is used to perform QoS optimisation-based multi-criteria scheduling. The proposed QoS optimisation-based multi-criteria scheduling problem solution has been practically examined by simulation experiments.