基于JXTA的P2P远程学习系统的设计与实现

JXTA技术主要用于提供P2P应用系统所需的基础服务.在总结传统B/S架构的远程学习系统许多弊端的基础上.分析JXTA技术的体系结构、核心协议及开发P2P应用系统的优势,利用JXTA技术和Java语言设计和实现一个简单的P2P远程学习系统.测试结果表明,该系统可靠性好、伸缩性强,极大地提高了学生的学习效率.     

基于JXTA的P2P远程学习系统的设计与实现

JXTA技术主要用于提供P2P应91系统所需的基础服务。在总结传统B／S架构的远程学习系统许多弊端的基础上．分析JXTA技术的体系结构、核心协议及开发P2P应用系统的优势,利用JXTA技术和Java语言设计和实现一个简单的P2P远程学习系统。测试结果表明,该系统可靠性好、伸缩性强,极大地提高了学生的学习效率。     

基于P2P网络的Web服务发布模型研究

为了突显P2P和Web服务的优势,基于JXTA平台的核心协议和Web服务的体系架构,建立了P2P网络中的Web服务发布模型,该模型通过利用管道、通告等JXTA协议机制实现了Web服务的SOAP消息的打包,完成了P2P网络中的Web服务的发布和请求及其协同工作.本模型通过JXTA协议的二次开发实现了将Web服务发布到P2P网络中的各对等点上,在一定程度上实现了P2P和Web的融合.     

JXTA平台下协同工作中应用共享机制研究

应用共享技术是计算机支持协同工作(CSCW)系统中支持同步协作的关键技术之一,该共享技术在JXTA网络模式下的研究比较匮乏.首先讨论了CSCW中已有的应用共享的实现机制,然后提出基于JXTA平台下的混合式应用共享机制理论:最后给出具体模型框架的实现方法,并对模型中的动作控制、会晤服务器和状态控制等关键组件进行详细阐述.该模型弥补和加强了已有JXTA平台对协同式工作开发的技术支持.     

一种全新的P2P网络解决方案-JXTA技术

对等网络P2P（Peer to Peer）是因特网实现下一次飞跃的的关键。在P2P网络模式的研究领域里，JXTA （juxtapose,并行）作为一种全新的P2P网络解决方案已经日益受到业界的关注。首先介绍了P2P网络以及它与传统网络模式的区别，然后详细介绍了JXTA技术，并在此基础上提出了对等体发现的几点思路。     

小型无线传感器网络的实现与P2P互联

传感器网络之间的互联与通信将成为未来无线传感器网络的应用模式之一,通过P2P技术互联多小传感器网络,奇为用户提供大规模、大范围、多样化的信息服务。在设计和实现基于SimplciTl协议的小型无线传感器网络的基础上,提出在无线传感器网络上的P2P覆盖,利用JXTA协议搭建P2P平台,实现了多个传感器网络以P2P的方式互联,并给出了硬件和软件的实现方案。     

基于P2PSIP即时通信系统的设计与实现

在传统SIP（会话初始协议）网络的即时通信中,存在单点失效、系统瓶颈以及难于扩展等问题。P2P（对等网络）网络的分布式特点打破了传统的C／S架构,所以把P2P技术引入到SIP网络已经成为一种趋势。文章给出基于P2PSIP即时通信系统的结构及其详细的设计与实现过程．包括实时通信模块和JXTA网络模块,JXTA网络模块由发...     

基于JXTA的无线网络的实现研究

本文首先介绍了当前的无线网络的发展,并对P2P做了相关说明。然后把JXTA与当前的主要P2P技术进行了比较,随后仔细讲解了如何构建基于JXTA的网络,最后通过一个demo对系统做了试验,并得到了一些结论。     

基于JXTA的无线网络的实现研究

本首先介绍了当前的无线网络的发展，并对P2P做了相关说明。然后把JXTA与当前的主要P2P技术进行了比较，随后仔细讲解了如何构建基于JXTA的网络，最后通过一个demo对系统做了试验，并得到了一些结论。     

P2P文件共享系统中对等点发现机制的实现

由于P2P网络的动态性，如何有效和准确地进行对等点定位成为P2P网络中的关键技术。本文在分析现在P2P网络中几种常见的对等点发现机制的优点与不足的基础上，提出了利用IP多播技术和JXTA平台实现的对等点发现机制，并讨论了其在P2P文件共享系统中的实现方法。     

Cross-Layer Collaborative In-Network Processing in Multihop Wireless Sensor Networks

Emerging wireless sensor network (WSN) applications demand considerable computation capacity for in-network processing. To achieve the required processing capacity, cross-layer collaborative in-network processing among sensors emerges as a promising solution: sensors do not only process information at the application layer, but also synchronize their communication activities to exchange partially processed data for parallel processing. However, scheduling computation and communication events is a challenging problem in WSNs due to limited resource availability and shared communication medium. In this work, an application-independent task mapping and scheduling solution in multihop homogeneous WSNs, multihop task mapping and scheduling (MTMS), is presented that provides real-time guarantees. Using our proposed application model, the multihop channel model, and the communication scheduling algorithm, computation tasks and associated communication events are scheduled simultaneously. The dynamic voltage scaling (DVS) algorithm is presented to further optimize energy consumption. Simulation results show significant performance improvements compared with existing mechanisms in terms of minimizing energy consumption subject to delay constraints     

探析JXTA项目2.0中消息的扩散机制

JXTA项目是由SUN公司发起的开放源代码项目。自从2001年4月推出JXTA1．0版本的参考实现后，JXTA社区进一步完善，最近推出了JXTA2．0版本。在新的版本中进行了很多算法方面的改进．本文主要是介绍其中改进最大的一部分——集合点网络的消息扩散机制。     

Evolved data scheduling scheme based on layered P2P streaming in mobile network

To relieve the negative effect brought by the intricate wireless network environment and unstable user behavior in layered mobile peer-to peer(P2P) streaming service,an evolved layered P2P (E-LP2P) dat...     

云环境下一种基于任务类型匹配的负载均衡策略研究

针对云计算环境下大量并行计算节点容易产生计算节点之间的负载不均问题,本文提出了一种基于任务类型匹配的负载均衡方案。该方案针对任务集中的多种不同长度的子任务类型情况进行判定,并对当前主流的Max-Min和Min-Min两种启发式负载均衡算法进行分析,综合其优缺点,并针对任务集的类型采用不同的算法进行任务调度。实验结果表明在该负载均衡的策略下,提出的方案具有比单一应用Max-Min或者Min-Min算法具有更好的负载均衡特性和更短的完成时间。     

Quality of Service Aware Reliable Task Scheduling in Vehicular Cloud Computing

Vehicular Cloud Computing (VCC) facilitates real-time execution of many emerging user and intelligent transportation system (ITS) applications by exploiting under-utilized on-board computing resources available in nearby vehicles. These applications have heterogeneous time criticality, i.e., they demand different Quality-of-Service levels. In addition to that, mobility of the vehicles makes the problem of scheduling different application tasks on the vehicular computing resources a challenging one. In this article, we have formulated the task scheduling problem as a mixed integer linear program (MILP) optimization that increases the computation reliability even as reducing the job execution delay. Vehicular on-board units (OBUs), manufactured by different vendors, have different architecture and computing capabilities. We have exploited MapReduce computation model to address the problem of resource heterogeneity and to support computation parallelization. Performance of the proposed solution is evaluated in network simulator version 3 (ns-3) by running MapReduce applications in urban road environment and the results are compared with the state-of-the-art works. The results show that significant performance improvements in terms of reliability and job execution time can be achieved by the proposed task scheduling model.     

Grid job scheduling using Route with Genetic Algorithm support

In 2006 the Route load balancing algorithm was proposed and compared to other techniques aiming at optimizing the process allocation in grid environments. This algorithm schedules tasks of parallel applications considering computer neighborhoods (where the distance is defined by the network latency). Route presents good results for large environments, although there are cases where neighbors do not have an enough computational capacity nor communication system capable of serving the application. In those situations the Route migrates tasks until they stabilize in a grid area with enough resources. This migration may take long time what reduces the overall performance. In order to improve such stabilization time, this paper proposes RouteGA (Route with Genetic Algorithm support) which considers historical information on parallel application behavior and also the computer capacities and load to optimize the scheduling. This information is extracted by using monitors and summarized in a knowledge base used to quantify the occupation of tasks. Afterwards, such information is used to parameterize a genetic algorithm responsible for optimizing the task allocation. Results confirm that RouteGA outperforms the load balancing carried out by the original Route, which had previously outperformed others scheduling algorithms from literature.     

KAD网络负载均衡技术研究

由于应用环境的特殊性和网络节点的异构性,大多数DHT网络都存在负载不均衡问题。以拥有大量用户群的eMule的KAD网络为研究对象,通过实际测量发现,由于关键词使用频率的不同,文件索引信息在KAD网络中的存储分布是不均匀的,会影响系统正常的资源发布和搜索。针对这一问题,本文提出了一个基于多重目标ID的KAD索引信息发布机制,通过让更多的节点负责拥有高频关键词的文件索引,提高KAD网络文件索引资源的负载均衡,并通过仿真实验证明了该方法的有效性。     

A Noise-Robust Convex-Optimized Positioning System Based on Code-Aided RSS Estimation and Virtual Base Station Transform

Parallelization of Digital Signal Processing (DSP) software is an important trend in Multiprocessor System-on-Chip (MPSoC) implementation. The performance of DSP systems composed of parallelized computations depends on the scheduling technique, which must in general allocate computation and communication resources for competing tasks, and ensure that data dependencies are satisfied. In this paper, we formulate a new type of parallel task scheduling problem called Parallel Actor Scheduling (PAS) for MPSoC mapping of DSP systems that are represented as Synchronous Dataflow (SDF) graphs. In contrast to traditional SDF-based scheduling techniques, which focus on exploiting graph level (inter-actor) parallelism, the PAS problem targets the integrated exploitation of both intra- and inter-actor parallelism for platforms in which individual actors can be parallelized across multiple processing units. We first address a special case of the PAS problem in which all of the actors in the DSP application or subsystem being optimized are parallel actors (i.e., they can be parallelized to exploit multiple cores). For this special case, we develop and experimentally evaluate a two-phase scheduling framework with three work flows that involve particle swarm optimization (PSO) — PSO with a mixed integer programming formulation, PSO with simulated annealing, and PSO with a fast heuristic based on list scheduling. Then, we extend our scheduling framework to support the general PAS problem, which considers both parallel actors and sequential actors (actors that cannot be parallelized) in an integrated manner. We demonstrate that our PAS-targeted scheduling framework provides a useful range of trade-offs between synthesis time requirements and the quality of the derived solutions. We also demonstrate the performance of our scheduling framework from two aspects: simulations on a diverse set of randomly generated SDF graphs, and implementations of an image processing application and a software defined radio benchmark on a state-of-the-art multicore DSP platform.     

A distributed utility‐based scheduling for peer‐to‐peer video streaming over wireless networks

Interactive multimedia applications such as peer‐to‐peer (P2P) video services over the Internet have gained increasing popularity during the past few years. However, the adopted Internet‐based P2P overlay network architecture hides the underlying network topology, assuming that channel quality is always in perfect condition. Because of the time‐varying nature of wireless channels, this hardly meets the user‐perceived video quality requirement when used in wireless environments. Considering the tightly coupled relationship between P2P overlay networks and the underlying networks, we propose a distributed utility‐based scheduling algorithm on the basis of a quality‐driven cross‐layer design framework to jointly optimize the parameters of different network layers to achieve highly improved video quality for P2P video streaming services in wireless networks. In this paper, the quality‐driven P2P scheduling algorithm is formulated into a distributed utility‐based distortion‐delay optimization problem, where the expected video distortion is minimized under the constraint of a given packet playback deadline to select the optimal combination of system parameters residing in different network layers. Specifically, encoding behaviors, network congestion, Automatic Repeat Request/Query (ARQ), and modulation and coding are jointly considered. Then, we provide the algorithmic solution to the formulated problem. The distributed optimization running on each peer node adopted in the proposed scheduling algorithm greatly reduces the computational intensity. Extensive experimental results also demonstrate 4–14 dB quality enhancement in terms of peak signal‐to‐noise ratio by using the proposed scheduling algorithm. Copyright © 2015 John Wiley & Sons, Ltd.