基于灰色预测的分布式系统动态故障检测服务

针对已有故障检测服务不能有效满足分布式系统需求问题,设计了一种适用于分布式系统的动态故障检测服务.根据分布式系统的特点,在定义分布式系统模型的基础上,提出了动态故障检测服务架构.结合心跳策略和灰色预测方法,设计了一种动态心跳机制,并给出了预测模型和动态预测策略,提出了基于该动态心跳机制的分布式系统的故障检测算法.最后,仿真实验验证了该算法的正确性和有效性.     

Parameterized Time Warp (PTW): An Integrated Adaptive Solution to Optimistic PDES

Time Warp is an optimistic synchronization protocol used for parallel discrete event simulation. While Time Warp has the potential to reduce the execution time of large simulations, it has been plagued by a variety of problems, namely: 1. Instability due to thrashing effects caused by echoing and cascading rollbacks. 2. Memory bottlenecks due to state saving and excessive optimism. 3. Inefficient scheduling algorithms for scheduling Time Warp processes on each processing node. These problems have inhibited the widespread use of Time Warp as a general purpose synchronization algorithm. The general trend of researchers attempting to solve these problems has been to statically limit the optimism of Time Warp. Unfortunately, these attempts have achieved only limited success. This is because a static set of parameters may perform well for one simulation but not for another. This paper attacks the problem using adaptive mechanisms to control optimism, using an index of performance called useful work. This research presents solutions for the above mentioned problems, by: 1. Stabilizing Time Warp using adaptive bounded time windows. 2. Reducing memory usage and overall execution time by using an adaptive mechanism to vary the checkpoint interval. 3. Scheduling Time Warp processes with the useful work parameter to favor more productive processes. Using this new performance index called Useful Work, several modifications to Time Warp are implemented to stabilize and improve Time Warp. Thus, this new improved Time Warp synchronization mechanism termed Parameterized Time Warp provides an integrated adaptive solution to optimistic Parallel Discrete Event Simulation. Empirical work showing that PTW outperforms an equivalent Time Warp simulation executing under similar partitioning and load conditions is also presented.     

Performance of a Time Warp based simulator of large scale PCS networks

This paper describes an object-oriented Time Warp (TW) mechanism which supports general parallel simulation on a distributed, possibly heterogeneous, computing environment. As a significant application of the developed TW, a simulation model adequate for large personal communication services (PCS) networks is proposed and its performance results given. Special attention is paid to such TW critical issues as load balancing and checkpointing interval tuning which strongly affect the achievement of good speedups. The experimental results confirm that good performance can be obtained on an heterogeneous distributed system provided an accurate parameter tuning is accomplished.     

节点重要程度感知的分布式DTN结构检测机制

间断连接无线网络具有较强的社会属性,感知网络结构能够有效改善网络性能。提出一种节点重要程度感知的网络结构检测机制,节点根据转发消息数量和邻居数量估计自身的重要程度,并以分布式的方式选取社区中心节点,进而依据与中心节点的共同邻居数确定本社区的邻居节点,完成网络结构检测。仿真结果表明,本机制检测准确率相较于HCDA提高大约45%,且所提出的方法扩展性较强,适用于各种混杂网络场景。     

Dynamic balancing of communication and computation load for HLA-based simulations on large-scale distributed systems

Dynamic balancing of computation and communication load is vital for the execution stability and performance of distributed, parallel simulations deployed on the shared, unreliable resources of large-scale environments. High Level Architecture (HLA) based simulations can experience a decrease in performance due to imbalances that are produced initially and/or during run time. These imbalances are generated by the dynamic load changes of distributed simulations or by unknown, non-managed background processes resulting from the non-dedication of shared resources. Due to the dynamic execution characteristics of elements that compose distributed applications, the computational load and interaction dependencies of each simulation entity change during run time. These dynamic changes lead to an irregular load and communication distribution, which increases overhead of resources and latencies. A static partitioning of load is limited to deterministic applications and is incapable of predicting the dynamic changes caused by distributed applications or by external background processes. Therefore, a scheme for balancing the communication and computational load during the execution of distributed simulations is devised in a scalable hierarchical architecture. The proposed balancing system employs local and cluster monitoring mechanisms in order to observe the distributed load changes and identify imbalances, repartitioning policies to determine a distribution of load and minimize imbalances. A migration technique is also employed by this proposed balancing system to perform reliable and low-latency load transfers. Such a system successfully improves the use of shared resources and increases distributed simulations’ performance by minimizing communication latencies and partitioning the load evenly. Experiments and comparative analyses were conducted in order to identify the gains that the proposed balancing scheme provides to large-scale distributed simulations.     

Conservative distributed discrete-event simulation on the Amazon EC2 cloud: An evaluation of time synchronization protocol performance and cost efficiency

Distributed execution of simulation models comes into play when memory limitations of a single computational resource prohibit their execution. In addition, the potential for parallel execution of a model on a distributed platform through the integration of multiple computational cores, can potentially reduce the execution time of a simulation. However, such gains can be voided by the overhead that time synchronization protocols for parallel and distributed simulation induce. This overhead is determined by the protocol used, the characteristics of the simulation model, as well as the architectural and performance characteristics of the hardware platform used. Recently, Infrastructure-as-a-Service offerings in the cloud computing domain have introduced flexibility in acquiring access to virtualized hardware platforms on a pay-as-you-go basis. At present, it is however unclear to what extent these offerings are suited for the distributed execution of discrete-event simulations, and how the characteristics of different resource types impact the performance of distributed simulation under different time synchronization protocols. Likewise, it is unclear which type of resources are most cost-efficient for this type of workload. To our knowledge, this paper is the first to investigate these aspects through an assessment of the performance and cost efficiency of different conservative time synchronization protocols on a range of cloud resource types that are currently available on Amazon EC2. Our analysis shows that performance levels comparable to those realized on commodity hardware based-clusters are attainable, and that the relative performance of different synchronization protocols is retained on high-end IaaS resources. In terms of cost-efficiency, we find that IaaS products tailored to traditional cluster workloads do not necessarily constitute the optimal choice, and we assess the impact of different packing configurations for logical processes in this regard.     

Boosting Algorithms for Parallel and Distributed Learning

The growing amount of available information and its distributed and heterogeneous nature has a major impact on the field of data mining. In this paper, we propose a framework for parallel and distributed boosting algorithms intended for efficient integrating specialized classifiers learned over very large, distributed and possibly heterogeneous databases that cannot fit into main computer memory. Boosting is a popular technique for constructing highly accurate classifier ensembles, where the classifiers are trained serially, with the weights on the training instances adaptively set according to the performance of previous classifiers. Our parallel boosting algorithm is designed for tightly coupled shared memory systems with a small number of processors, with an objective of achieving the maximal prediction accuracy in fewer iterations than boosting on a single processor. After all processors learn classifiers in parallel at each boosting round, they are combined according to the confidence of their prediction. Our distributed boosting algorithm is proposed primarily for learning from several disjoint data sites when the data cannot be merged together, although it can also be used for parallel learning where a massive data set is partitioned into several disjoint subsets for a more efficient analysis. At each boosting round, the proposed method combines classifiers from all sites and creates a classifier ensemble on each site. The final classifier is constructed as an ensemble of all classifier ensembles built on disjoint data sets. The new proposed methods applied to several data sets have shown that parallel boosting can achieve the same or even better prediction accuracy considerably faster than the standard sequential boosting. Results from the experiments also indicate that distributed boosting has comparable or slightly improved classification accuracy over standard boosting, while requiring much less memory and computational time since it uses smaller data sets.     

Predicting Interactions Between Agents in Agent-Based Modeling and Simulation of Sociotechnical Systems

Agent-based modeling and simulation are a valuable research tools for the analysis of dynamic and emergent phenomena of large-scale complex sociotechnical systems. The dynamic behavior of such systems includes both the individual behavior of heterogeneous agents within the system and the emergent behavior arising from interactions between agents; both must be accurately modeled and efficiently executed in simulations. This paper provides a timing and prediction mechanism for the accurate modeling of interactions among agents, correspondingly increasing the computational efficiency of agent-based simulations. A method for assessing the accuracy of interaction prediction methods is described based on signal detection theory. An intelligent interaction timing agent framework that uses a neural network to predict the timing of interactions between heterogeneous agents is presented; this framework dramatically improves the accuracy of interaction timing without requiring detailed scenario-specific modeling efforts for each simulation configuration.      

The Design and Performance Evaluation of the DI-Multicomputer

In this paper, we propose a new multicomputer node architecture, theDI-multicomputerwhich uses packet routing on a uniform point-to-point interconnect for both local memory access and internode communication. This is achieved by integrating a router into each processor chip and eliminating the memory bus interface. Since communication resources such as pins and wires are allocated dynamically via packet routing, the DI-multicomputer is able to maximize the available communication resources, providing much higher performance for both intranode and internode communication. Multi-packet handling mechanisms are used to implement a high performance memory interface based on packet routing. The DI-multicomputer network interface provides efficient communication for both short and long messages, decoupling the processor from the transmission overhead for long messages while achieving minimum latency for short messages. Trace-driven simulations based on a suite of message passing applications show that the communication mechanisms of the DI-multicomputer can achieve up to four times speedup when compared to existing architectures.     

一种基于实测的自动负载建模算法

负载平衡是影响大规模并行计算效率的一个关键因素,准确的负载建模是负载平衡的基础.提出了一种基于实测的自动负载建模算法.该算法无需用户提供信息,具有良好的理论保证以及近似线性的计算复杂度和完全的并行性.2400个进程上的分子动力学模拟表明,该算法执行速度快,同时能够保证60％以上的负载平衡效率.     

Distributing RePast agent‐based simulations with HLA

Large, experimental multi‐agent system (MAS) simulations are highly demanding tasks, both computationally and developmentally. Agent toolkits provide reliable templates for the design of even the largest MAS simulations, without offering a solution to computational limitations. Conversely, distributed simulation architectures offer performance benefits, but the introduction of parallel logic can complicate the design process significantly. The motivations of distribution are not limited to this question of processing power. True interoperation of sequential agent‐simulation platforms would allow agents designed using different toolkits to transparently interact in common abstract domains. This paper discusses the design and implementation of a system capable of harnessing the computational power of a distributed simulation infrastructure with the design efficiency of an agent toolkit. The system permits integration, through a higher‐level architecture (HLA) federation, of multiple instances of the Java‐based lightweight agent‐simulation toolkit RePast. This paper defines abstractly the engineering process necessary in creating such middleware, and reports on the experience in the specific case of the RePast toolkit. The paper also presents performance results that illustrate that significant speedup can be achieved through the integration of RePast with HLA. Copyright © 2008 John Wiley & Sons, Ltd.     

基于时窗熵的冲击波到时提取方法研究

针对冲击波超压场大区域、全过程重建的需求,开展冲击波到达时间的高精度提取方法研究;首先,分析了STA/LTA的冲击波到达时间提取模型,其次结合信息论理论提出了基于时窗熵的冲击波到达时间提取方法,再次通过仿真实验比较了不同噪声条件下本文的到达时间提取精度,对比了两种方法在同一噪声条件下的提取精度,分析结果表明本方法的提取精度在不同噪声条件下基本保持在0.77%,在低信噪比条件下,本方法的提取精度高于STA/LTA约6%,保证了低信噪比条件下冲击波到达时间的提取精度,解决了STA/LTA方法对于信号变化幅度小而发生漏拾的情况,避免了不必要的提取误差,实现了更高精度的冲击波到达时间的提取具有更高的提取精度,能够为大区域的冲击波超压场高精度重建提供有效的到达时间特征参数,在高价值弹药毁伤效能参数中具有一定的理论意义和工程使用价值。     

基于多特征融合的航空发动机剩余寿命预测

针对航空发动机剩余可用寿命(RUL)预测任务中代表性特征提取不充分导致RUL预测精度较低等问题, 提出了一种基于多特征融合的航空发动机RUL预测方法. 利用指数平滑法(ES)降低原始数据中的噪声干扰, 得到相对平稳的特征数据. 使用双向长短期记忆网络(Bi-LSTM)提取特征数据的时序特征, 利用多头注意力机制(Multi-attention)为时序特征赋予权重; 设计卷积长短期记忆网络(Conv-LSTM)提取特征数据的时空特征; 提取特征数据的手工特征并使用Softmax函数计算权重. 设计一个特征融合框架将上述特征进行融合, 然后通过全连接网络回归实现最终RUL预测. 使用C-MAPSS数据集对模型进行仿真验证, 与Bi-LSTM等模型进行对比, 模型RUL预测精度更高, 适应性更好.     

车联网中基于车辆行为预测的身份认证方案

车联网中传统基于密码学的身份认证方案可满足车辆身份认证的基本要求,但其作为静态防御机制不能有效解决车辆身份盗用和认证低时延问题。在基于移动边缘计算框架的软件定义车联网体系结构下,提出一种基于车辆行为预测的身份认证方案。在车辆历史行为数据的基础上,使用前缀树确定认证基站,采用决策树算法和多元非线性回归模型提前对车辆到达站点和时间进行预测,并通过对比车辆到达站点和时间的真实值与预测值实现车辆身份认证。实验结果表明,该方案利用软件定义网络的集中式全局控制能力和移动边缘计算的分布式计算能力对车辆身份认证任务进行管理和分配,可在保证较高车辆认证准确率的同时满足车联网的低时延需求。     

基于并行仿真的MTBA算法研究

混合策略在一定程度上避免了过分保守或极端乐观的缺点。本文首先分析了现有同步机制存在的不足,然后论述了视界概念及最小时间桶算法的设计与实现,接着对最小时间桶算法的性能进行了定性定量的深入探讨,最后采用PHOLD仿真应用模型在MTBA算法、保守算法和TW乐观算法之间进行了性能对比实验。实验结果表明,MTBA算法在某些条件下具有更小的回退开销和更快的事件处理效率。     

大规模联邦仿真中实体迁移及其时间同步研究(二)--实体迁移中的时间同步

实体迁移技术有利于改善大规模分布式仿真系统的性能,其中迁移期间的时间同步是其关键与难点．基于HLA联邦仿真框架,研究了实体迁移中的时间同步机制;分析了迁移期间邦元的状态变迁情况;给出了旧邦元转发消息以及新邦元接收和处理消息时所需的同步策略;保证了按时戳序处理消息．迁移期间新旧邦元逻辑时间同步,防止了丢失消息或重复处理同一消息等现象,也能撤销消息,以便乐观同步．     

能量收集分布式检测系统中基于门限的节点能量管理策略

针对能量收集分布式检测系统由于环境能量到达随机造成系统检测性能不稳定的问题,在同时考虑节点能量不确定性、探测能量不确定性、通信能量不确定的前提下,给出了一种基于节点能量使用门限的能量管理策略的确定方法。该方法首先优化探测和通信的能量分配方式,然后根据能量到达强度,对有限能量区间进行穷举搜索,确定合理的节点能量使用门限。该方法的优势是确定的策略是一种离线策略,不占用节点通信资源,电池存储能量是唯一执行条件,简单易行。仿真表明该方法确定的节点能量管理策略与“能量到达即使用”的策略相比,有效降低了能量随机到达对系统检测性能的影响,提升了检测系统平均检测性能。     

TOA-based passive localization of multiple targets with inaccurate receivers based on belief propagation on factor graph

Location awareness is now becoming a vital requirement for many practical applications. In this paper, we consider passive localization of multiple targets with one transmitter and several receivers based on time of arrival (TOA) measurements. Existing studies assume that positions of receivers are perfectly known. However, in practice, receivers' positions might be inaccurate, which leads to localization error of targets. We propose factor graph (FG)-based belief propagation (BP) algorithms to locate the passive targets and improve the position accuracy of receivers simultaneously. Due to the nonlinearity of the likelihood function, messages on the FG cannot be derived in closed form. We propose both sample-based and parametric methods to solve this problem. In the sample-based BP algorithm, particle swarm optimization is employed to reduce the number of particles required to represent messages. In parametric BP algorithm, the nonlinear terms in messages are linearized, which results in closed-form Gaussian message passing on FG. The Bayesian Cramér–Rao bound (BCRB) for passive targets localization with inaccurate receivers is derived to evaluate the performance of the proposed algorithms. Simulation results show that both the sample-based and parametric BP algorithms outperform the conventional method and attain the proposed BCRB. Receivers' positions can also be improved via the proposed BP algorithms. Although the parametric BP algorithm performs slightly worse than the sample-based BP method, it could be more attractive in practical applications due to the significantly lower computational complexity.     

Learning based brain emotional intelligence as a new aspect for development of an alarm system

The multi criteria and purposeful prediction approach has been introduced and is implemented by the fast and efficient behavioral based brain emotional learning method. On the other side, the emotional learning from brain model has shown good performance and is characterized by high generalization property. New approach is developed to deal with low computational and memory resources and can be used with the largest available data sets. The scope of paper is to reveal the advantages of emotional learning interpretations of brain as a purposeful forecasting system designed to warning; and to make a fair comparison between the successful neural (MLP) and neurofuzzy (ANFIS) approaches in their best structures and according to prediction accuracy, generalization, and computational complexity. The auroral electrojet (AE) index are used as practical examples of chaotic time series and introduced method used to make predictions and warning of geomagnetic disturbances and geomagnetic storms based on AE index.     

Revisiting conservative time synchronization protocols in parallel and distributed simulation

Computer simulations have become an indispensable tool for the empirical study of large‐scale systems. The timely simulation of these systems, however, is not without its challenges. Simulators have to be able to harness the full computational power of modern multicore architectures through parallel execution and overcome the memory limitations of a single computer. In this paper, we evaluate the performance of a parallel and distributed simulator using several conventional time synchronization protocols executed on modern multicore hardware. In addition, we comprehensively analyze a hybrid approach, combining two traditional protocols, increasing robustness, and enabling improved performance in a wider range of simulation scenarios. Finally, an adaptive algorithm to automatically configure this hybrid protocol is introduced and evaluated, eliminating manual user intervention and further improving robustness with respect to varying simulation conditions. Copyright © 2013 John Wiley & Sons, Ltd.