一种基于多移动Agent的对等计算动态协作模型

对等计算技术为Internet上海量节点之间计算和信息资源的共享提供了基础平台.对等节点的协作可以有效利用分布于各对等节点上的资源以解决单节点难以完成的复杂任务.然而对等计算环境的动态性、不可预测性等特征也为顺利的协作带来了困难.提出一种基于多移动Agent的对等计算动态协作模型,利用多Agent技术构建P2P平台的协作层,将对等节点之间的协作转换为Agent之间的协作,再通过移动Agent来动态部署针对具体任务的功能组件.还给出了该动态协作模型在密码分析中的应用示范和对比实验数据结果,对该模型的实用价值进行验证.     

Data Management in Peer-to-Peer Environment: A Perspective of BestPeer

Peer-to-Peer (P2P) systems have attracted much attention in academic commu-nity and industry circles due to their promising applications in various domains. This paper presents the authors‘ research efforts on introducing complex query capabilities in a P2P environ-ment consisting of numerous peers with large volume of data. An underlying hybrid P2P computing platform, named BestPeer is described first. The connection among peers within BestPeer is self-configurable through maintaining the nearest neighbor of peers, and the agent techniques employed in the system ensure its capability of providing sophisticated services. The designs of three P2P data management systems which are all based on BestPeer are described in detail. They provide support for information retrieval, query processing and Web services respectively. Advantages and limitations are discussed, while ongoing work is presented. Current systems can provide basic functions for keyword-based search, SQL-like query processing, and Web services querying and discovery. Some further topics on providing fully-fledged data management functionalities for P2P distributed computing systems with security guarantee are also discussed.     

增强计算系统可信赖性:融合虚拟化和SOA

随着计算机软、硬件技术的不断进步和应用需求的日益增长,以计算机为中心的计算系统的应用范围越来越广,其复杂程度也在迅速提高,人们对如何评估和提高计算系统的可信赖性的需求日益迫切.首先给出了计算系统的可信赖性的定义,并系统地定义了一整套量化评价指标;同时,对计算系统面临的各种可信赖性威胁进行了详细的归类分析.传统的方法难以应对复杂系统面临的各种可信赖性问题,人们仍在不断地寻求新的技术.虚拟化技术在这种应用背景下走向复兴,成为一大研究热点.介绍了已有的虚拟化技术在增强系统可信赖性上相关的研究成果,并且总结了虚拟化技术在增强系统可信赖性方面的各种特性和机制.然而由于现有的计算系统体系结构的限制,难以将虚拟化技术在增强系统可信赖性方面的优势充分地发挥出来.面向服务的体系结构(service oriented architecture,简称SOA)以其松散耦合、平台无关性等特点很好地适应了虚拟化技术的需求.因此,最后将SOA 和虚拟化技术相结合,提出了一种增强计算系统可信赖性的系统架构,即面向服务的虚拟化SOV(service oriented virtualization),并且分析了SOV 系统如何在遭受各种可信赖性威胁时,运用体系结构优势和虚拟化技术的各种机制保证系统可信赖性.     

志愿者计算模型在电力系统潮流计算中的运用

提出了一种电力系统潮流计算并行处理的新方法,该方法使用基于协同服务器组的志愿者计算平台P2HP作为高性能编程环境和运行平台。分析了大规模电力系统潮流计算问题在P2HP计算平台上的任务划分、并行粒度和实现技术。使用对象序列化技术和本地接口调用技术可以使潮流计算应用程序跨平台运行。仿真计算结果证明,这种方法能够满足安全性、实时性要求很高的大规模潮流计算应用的需求,具有较好的加速比和运行效率。     

P2P系统中面向异构平台的信任模型

针对P2P系统中可信计算平台与传统的非可信计算平台所组成的异构系统间跨平台信任建立的问题,基于可信计算技术,提出一种全新的信任模型,对其架构和认证流程进行研究。仿真结果表明,系统中节点具有较高的匿名度,同时该模型具有良好的抵抗恶意节点行为的能力。     

基于神威·太湖之光的非结构网格计算加速算法

在国产异构众核平台神威·太湖之光上的非结构网格计算具有稀疏存储、离散访存、数据依赖等特点,严重制约了众核处理器的性能发挥。为解决稀疏存储和离散访存问题,提出一种N阶对角染色算法,以有效平衡主从核计算并利用从核将全局访存转化为LDM访问。针对数据依赖造成的计算竞争问题,采用自适应和无依赖的任务划分方法,避免并行计算时的数据冲突。为对处理器架构和非结构网格计算进行优化,采用主核与从核异步并行的方式,差异化使用主从核以充分利用硬件资源,同时,取消处理器提供的寄存器通信机制,降低从核阵列的同步开销同时便于扩展到新一代神威平台。此外,使用计算访存异步重叠技术来充分隐藏访存延迟。利用SpMV、Integration、calcLudsFcc算子进行实验,结果表明,相比主核实现,组合加速算法在不同算例规模下平均取得了10倍的加速效果,加速比最高可达24倍,N阶对角染色算法相比非染色分块算法取得了超过5.8倍的性能加速,有效提升了数据局部性和计算并行度。该算法对有依赖关系的计算冲突算子同样具有良好的加速性能,验证了自适应和无依赖任务划分方法的有效性。     

基于赋权有向超图的云计算依赖任务调度研究

如何对依赖任务进行高效合理的调度是云计算急需解决的关键问题之一。对云计算环境下的依赖任务调度系统进行了形式化描述。采用赋权有向无环超图来构造依赖任务调度问题的数学模型,结点对应于依赖任务,有向超边对应于任务之间的执行先后依赖关系。将云计算依赖任务调度问题转换为赋权有向超图的优化划分问题,提出了基于多水平方法和赋权有向超图的依赖任务划分优化算法。设计并实现了基于多水平方法的云计算依赖任务调度原型系统。在CloudSim云计算仿真实验平台下,与Min-Min算法、Max-Min算法进行了对比实验,实验数据对比表明该算法在减少依赖任务执行时间的同时,优化了资源负载均衡性能。     

基于幂律分布的P2P信誉评估机制

P2P网络具有良好的可扩展性和健壮性,但其匿名性、开放性等特征成为恶意行为的理想环境。针对上述问题,提出一种基于幂律分布的信誉评估机制识别网络中的良性行为,抵制恶意行为。仿真测试表明,运用该机制后计算节点信誉分值具有较小的计算开销和良好的准确度,支持节点的动态加入和退出,对于恶意节点的骚扰具有很强的健壮性。     

Introducing artificial evolution into peer-to-peer networks with the distributed remodeling framework

A peer-to-peer (P2P) network is a complex system whose elements (peer nodes, or simply peers) cooperate to implement scalable distributed services. From a general point of view, the activities of a P2P system are consequences of external inputs coming from the environment, and of the internal feedbacks among nodes. The reaction of a peer to direct or indirect inputs from the environment is dictated by its functional structure, which is usually defined in terms of static rules (protocols) shared among peers. The introduction of artificial evolution mechanisms may improve the efficiency of P2P networks, with respect to resource consumption, while preserving high performance in response to the environmental needs. In this paper, we propose the distributed remodeling framework (DRF), a general approach for the design of efficient environment-driven peer-to-peer networks. As a case study, we consider an ultra-large-scale storage and computing system whose nodes perform lookups for resources provided by other nodes, to cope with task execution requests that cannot be fulfilled locally. Thanks to the DRF, workload modifications trigger reconfigurations at the level of single peers, from which global system adaptation emerges without centralized control.     

H-Trust: A Group Trust Management System for Peer-to-Peer Desktop Grid

Peer-to-Peer Desktop Grid (P2PDG) has emerged as a pervasive cyber-infrastructure tackling many large-scale applications with high impacts. As a burgeoning research area, P2PDG can support numerous applications, including scientific computing, file sharing, web services, and virtual organization for collaborative activities and projects. To handle trustworthiness issues of these services, trust and reputation schemes are proposed to establish trust among peers in P2PDG. In this paper, we propose a robust group trust management system, called H-Trust, inspired by the H-index aggregation technique. Leveraging the robustness of the H-index algorithm under incomplete and uncertain circumstances, H-Trust offers a robust personalized reputation evaluation mechanism for both individual and group trusts with minimal communication and computation overheads. We present the H-Trust scheme in five phases, including trust recording, local trust evaluation, trust query phase, spatial-temporal update phase, and group reputation evaluation phases. The rationale for its design, the analysis of the algorithm are further investigated. To validate the performance of H-Trust scheme, we designed the H-Trust simulator HTrust-Sim to conduct multi-agent-based simulations. Simulation results demonstrate that H-Trust is robust and can identify and isolate malicious peers in large scale systems even when a large portion of peers are malicious.     

Linking identical neighborly partitions for efficient high-dimensional similarity search in unstructured peer-to-peer systems

Peer-to-Peer (P2P) computing has recently attracted a great deal of research attention. In a P2P system, a large number of nodes can potentially be pooled together to share their resources, information, and services. However, existing unstructured P2P systems lack support for content-based search over data objects which are generally represented by high-dimensional feature vectors. In this paper, we propose an efficient and effective indexing mechanism to facilitate high-dimensional similarity query in unstructured P2P systems, named Linking Identical Neighborly Partitions (LINP), which combines both space partitioning technique and routing index technique. With the aid of LINP, each peer can not only process similarity query efficiently over its local data, but also can route the query to the promising peers which may contain the desired data. In the proposed scheme, each peer summarizes its local data using the space partitioning technique, and exchanges the summarized index with its neighboring peers to construct routing indices. Furthermore, to improve the system performance with peer updates, we propose an extension of the LINP, named LINP⁺, where each peer can reconfigure its neighboring peers to keep relevant peers nearby. The performance of our proposed scheme is evaluated over both synthetic and real-life high-dimensional datasets, and experimental results show the superiority of our proposed scheme.     

UPStore:一个开放的P2P存储平台

PZP存储系统具有可扩展性好、容错性高等优势,是近年来学术界的研究热点.但是由于P2P存储系统的复杂性高,搭建起来比较困难,使得系统并没有随着研究发展而流行起来.尤其是在广域网中,由于节点的高动态性,使得系统的部署和维护更加困难.介绍了一个开放式的P2P存储平台架构,可以在封闭的机房系统里,利用稳定的服务器搭建存储系统,也可以在开放的广域网环境下,利用动态性高的个人电脑来搭建存储系统.上层应用开发者可方便地使用该平台来创建各种服务,从而能将P2P存储系统在实际应用中推广开来.     

雾计算平台的任务调度算法研究

雾计算平台中的任务调度问题是无法在多项式时间复杂度内求取精确解的NP-问题。本文在根据雾计算任务调度流程,构建雾计算平台任务调度数学模型基础上,采用改进人工蜂群算法,将任务调度映射为蜂群寻找蜜源的过程,在种群初始化阶段过引入混沌思想,改善了人工蜂群算法缺陷,扩大了蜂群搜索范围,避免陷入局部最优解。实验结果表明,改进后的人工蜂群算法具有更快的算法收敛速度,算法解析所对应的任务调度策略,也具有更高的任务处理总性能,表明本文所研究的改进人工蜂群算法,达到了提高雾计算资源利用率,提高雾计算任务处理效率的目的。     

Decentralized proactive resource allocation for maximizing throughput of P2P Grid

Peer-to-peer Desktop Grids provide integrated computational resources by leveraging autonomous desktop computers located at the edge of the Internet to offer high computing power. The arbitrary arrival and serving rates of tasks on peers impedes the high throughput in large-scale P2P Grids. We propose a novel autonomous resource allocation scheme, which can maximize the throughput of self-organizing P2P Grid systems. Our design possesses three key features: (1) high adaptability to dynamic environment by proactive and convex-optimal estimation of nodes’ volatile states; (2) minimized task migration conflict probability (upper bound can be limited to 2%) of over-utilized nodes individually shifting surplus loads; (3) a load-status conscious gossip protocol for optimizing distributed resource discovery effect. Based on a real-life user’s workload and capacity distribution, the simulation results show that our approach could get significantly improved throughput with 23.6–47.1% reduction on unprocessed workload compared to other methods. We also observe high scalability of our solution under dynamic peer-churning situations.     

改进蚁群算法的云计算任务调度方法

针对蚁群算法在云计算任务调度问题求解过程存在的不足,以找到最佳的云计算任务调度方案为目标,提出了一种基于改进蚁群算法的云计算任务调度方法.首先对当前云计算任务调度研究现状进行分析,并对问题进行了具体描述,然后采用蚁群算法对云计算任务调度问题进行求解,并针对标准蚁群算法缺陷进行改进,最后在CloudSim平台对该方法的性能进行测试.结果表明,改进蚁群算法可以找到较好的云计算任务问题调度方案,加快云计算任务完成速度,具有一定的实际应用价值.     

一种基于K-means聚类分组的P2P超结点模型

本文面向网格计算、协同工作等特定P2P应用、提出了一种基于超节点的P2P混合网络模型。该模型的主要特点是采用K-means聚类算法对网络中的节点进行聚类,从而得到分组虚拟子网,由各子网内的超节点负责临近节点的加入与注销。该模型既克服P2P集中模型伸缩性不强,分散性P2P模型发送消息量大等缺点,又可以灵活地组织对等节点进行网格计算或组内协同工作等分布式应用。文章最后使用仿真实验表明K-means算法在网络节点聚类中的有效性。     

A distributed data clustering algorithm in P2P networks

Clustering is one of the important data mining issues, especially for large and distributed data analysis. Distributed computing environments such as Peer-to-Peer (P2P) networks involve separated/scattered data sources, distributed among the peers. According to unpredictable growth and dynamic nature of P2P networks, data of peers are constantly changing. Due to the high volume of computing and communications and privacy concerns, processing of these types of data should be applied in a distributed way and without central management. Today, most applications of P2P systems focus on unstructured P2P systems. In unstructured P2P networks, spreading gossip is a simple and efficient method of communication, which can adapt to dynamic conditions in these networks. Recently, some algorithms with different pros and cons have been proposed for data clustering in P2P networks. In this paper, by combining a novel method for extracting the representative data, a gossip-based protocol and a new centralized clustering method, a Gossip Based Distributed Clustering algorithm for P2P networks called GBDC-P2P is proposed. The GBDC-P2P algorithm is suitable for data clustering in unstructured P2P networks and it adapts to the dynamic conditions of these networks. In the GBDC-P2P algorithm, peers perform data clustering operation with a distributed approach only through communications with their neighbours. The GBDC-P2P does not need to rely on a central server and it performs asynchronously. Evaluation results demonstrate the superior performance of the GBDC-P2P algorithm. Also, a comparative analysis with other well-established methods illustrates the efficiency of the proposed method.     

Indexing views to route queries in a PDMS

P2P computing gains increasing attention lately, since it provides the means for realizing computing systems that scale to very large numbers of participating peers, while ensuring high autonomy and fault-tolerance. Peer Data Management Systems (PDMS) have been proposed to support sophisticated facilities in exchanging, querying and integrating (semi-)structured data hosted by peers. In this paper, we are interested in routing graph queries in a very large PDMS, where peers advertise their local bases using fragments of community RDF/S schemes (i.e., views). We introduce an original encoding for these fragments, in order to efficiently check whether a peer view is subsumed by a query. We rely on this encoding to design an RDF/S view lookup service featuring a statefull and a stateless execution over a DHT-based P2P infrastructure. We finally evaluate experimentally our system to demonstrate its scalability for very large P2P networks and arbitrary RDF/S schema fragments, and to estimate the number of routing hops required by the two versions of our lookup service. Work done when T. Dalamagas was a postdoc researcher in NTUA.     

一种关键任务系统自律可信性模型与量化分析

将现有入侵容忍、自毁技术与自律计算相结合,提出了一种基于SM-PEPA(semi-Markov performance evaluation process algebra)的关键任务系统自律可信性模型以支持形式化分析和推理.该模型具有一定程度的自管理能力,采用分级处理的方式应对各种程度的可信性威胁,满足了关键任务系统对可信性的特殊需求.在此基础上,从稳态概率角度提出了一种自律可信性度量方法.最后,结合具体实例对模型参数对自律可信性的影响进行了初步分析.实验结果表明,增大关键任务系统可信性威胁检测率和自恢复成功率,可在较大范围内提高系统的自律可信 特性.     

面向国产CPU的可重构计算系统设计及性能探究

为了提升国产平台的计算性能,采用国产CPU+FPGA的异构架构,设计了基于国产CPU的可重构计算系统。该系统包括基于国产CPU的主机单元和FPGA可重构加速单元,主机单元负责逻辑判断与管理调度等任务,FPGA负责对计算密集型任务进行加速,并采用OpenCL框架模型进行编程,以缩短FPGA的开发周期。为了验证该系统的性能,采用AES加密算法来测试该系统的计算性能,通过对不同长度的明文进行AES加密测试,并与CPU串行处理结果进行对比,得出：相比于单核FT-1500A CPU串行加密方式,采用可重构计算系统并行加密能够获得120多倍的加速比,且此加速比会随着明文长度的增加而成非线性增大。实验结果表明：基于国产CPU的可重构计算系统能够大幅提升国产平台的计算性能。