Performance analysis of an adaptive dynamic grid-based approach to data distribution management

Data distribution management (DDM) plays a key role in traffic control for large-scale distributed simulations. In recent years, several solutions have been devised to make DDM more efficient and adaptive to different traffic conditions. Examples of such systems include the region-based, fixed grid-based, and dynamic grid-based (DGB) schemes, as well as grid-filtered region-based and agent-based DDM schemes. However, less effort has been directed toward improving the processing performance of DDM techniques. This paper presents a novel DDM scheme called the adaptive dynamic grid-based (ADGB) scheme that optimizes DDM time through the analysis of matching performance. ADGB uses an advertising scheme in which information about the target cell involved in the process of matching subscribers to publishers is known in advance. An important concept known as the distribution rate (DR) is devised. The DR represents the relative processing load and communication load generated at each federate. The DR and the matching performance are used as part of the ADGB method to select, throughout the simulation, the devised advertisement scheme that achieves the maximum gain with acceptable network traffic overhead. If we assume the same worst case propagation delays, when the matching probability is high, the performance estimation of ADGB has shown that a maximum efficiency gain of 66% can be achieved over the DGB scheme. The novelty of the ADGB scheme is its focus on improving performance, an important (and often forgotten) goal of DDM strategies.     

An Adaptive Dynamic Grid-Based approach to DDM for large-scale distributed simulation systems

Data Distribution Management (DDM) plays a key role in traffic volume control of large-scale distributed simulations. In recent years, several solutions have been devised to make DDM more efficient and adaptive to different traffic conditions. Examples of such systems include the Region-Based, Fixed Grid-Based, Hybrid, and Dynamic Grid-Based (DGB) schemes. However, less effort has been directed toward improving the processing performance of DDM techniques. This paper presents a novel DDM scheme called the Adaptive Dynamic Grid-Based (ADGB) scheme that optimizes DDM time through analysis of matching performance. ADGB uses an advertising scheme in which information about the target cell involved in the process of matching subscribers to publishers is known in advance. An important concept known as the Distribution Rate (DR) is devised. The distribution rate represents the relative processing load and communication load generated at each federate. The matching performance and the distribution rate are used as part of the ADGB method to select, throughout the simulation, the devised advertisement scheme that achieves the maximum gain with acceptable network traffic overhead. If we assume the same worst case propagation delays, when the matching probability is high, the performance estimation of ADGB has shown that a maximum efficiency gain of 66% can be achieved over the Dynamic Grid-Based scheme. The novelty of the ADGB scheme is its focus on improving performance, an important (and often forgotten) goal of DDM strategies.     

Scalability and Performance Evaluation of DDM-Based Aggregation/Dissaggregation Protocols for Large-Scale Distributed Interactive Simulations Systems

Aggregation/disaggregation is a method for implementing multi-resolution simulations within a High Level Architecture (HLA) federation. HLA is a U.S. Department of Defense (DoD) developed standard to facilitate linking different types of simulations, in various locations, to form an or interactive, full-scale simulation, called a federation. Data Distribution Management (DDM) is a High Level Architecture/Run-time Infrastructure (HLA/RTI) service that manages the distribution of state updates and interaction information and controls the volume of data exchanged, in large-scale distributed simulations. The purpose of HLA is to promote interoperability and reuse among heterogenous simulations, including those simulations that offer varied levels of resolution, to provide practical training to military personnel of different ranks. The purpose of Aggregation/disaggregation is to ensure consistency in state updates between federates simulating objects at various levels of resolution. This paper focuses on the scalability of aggregation/disaggregation with different DDM implementations and examines the effects, on performance of large-scale simulations. We implement a federate-based aggregation/disaggregation scheme, originally introduced in [TAN01], with a tank dogfight scenario, aggregating five tanks into one tank battalion and disaggregating the battalion back into five individual entities (tanks). The DDM methods we analyze consist of the Fixed Grid-Based method, the Dynamic Grid-Based method and the Region-Based method. In [TAN01], testing of this federate-based aggregation/disaggregation was limited to a dual federation and a single DDM scheme. In an effort to determine the scalability of aggregation/disaggregation, with three methods of DDM, we measure the communication overhead and analyze performance during a federation execution. We present the results of extensive testing, varying the number of aggregation/disaggregation requests, the number of multi-resolution federates participating in the federation, the number of objects, the number/size of the grids and report on the performance evlauation of our protocols using an extensive set of simulation experiments. This work was partially supported by Grants from NSERC, Canada Research Chairs Program, Canada Foundation for Innovation, OIT/Distinguished Researcher Award.     

A study of achievable speedup in distributed simulation via NULLmessages

The results of an experimental study on distributed simulation of three open queuing networks are reported. The distributed simulation scheme considered is a simple variation of the scheme given by K.M. Chandy and J. Misra (1979) using NULL messages. A new approach is used to study the relationship between the overhead and performance of a distributed simulator, and the approach is illustrated by studying these three example networks. Two measures of ideal speedup of distributed simulation over sequential simulation are defined and measured. These values of ideal speedup are much less than simply the number of processors, and hence provide a more realistic value for the ideal speedup     

Offloading Data Distribution Management to Network Processors in HLA-Based Distributed Simulations

The high-level architecture (HLA) standard developed by the Department of Defense in the United States is a key technology to perform distributed simulation. Inside the HLA framework, many different simulators (termed federates) may be interconnected to create a single more complex simulator (federation). Data distribution management (DDM) is an optional subset of services that controls which federates should receive notification of state modifications made by other federates. A simple DDM implementation will usually generate much more traffic than needed, whereas a complex one might introduce too much overhead. In this work, we describe an approach to DDM that delegates a portion of the DDM computation to a processor on the network card in order to provide more CPU time for other federate and Runtime Infrastructure (RTI) computations while still being able to exploit the benefits of a complex DDM implementation to reduce the amount of information exchange.     

Quantization-based filtering in distributed discrete event simulation

Quantization is an approach to distributed logical simulation in which the value space is quantized and trajectories are represented by the crossings of a set of thresholds. This is an alternative to the common approach which discretizes the time base of a continuous trajectory to obtain a finite number of equally spaced sampled values over time. In distributed simulation, a quantizer checks for threshold crossings whenever an output event occurs and sends this value across to a receiver thereby reducing the number of messages exchanged among federates in a federation. This may increase performance in various ways such as decreasing overall execution time or allowing a larger number of entities to be simulated. Predictive quantization is a more advanced approach that sends just one bit of information instead of the actual real value size with the consequence that not only the number of messages, but also the message size, can be significantly reduced in this approach. In this paper, we present an approach to packaging individual bits into a large message packet, called multiplexed predictive quantization. We demonstrate that this approach can save significant overhead (thereby maximizing data transmission) and can reach close to 100% efficiency in the limit of large numbers of simultaneous message sources encapsulated within individual federates. We also discuss the tradeoff between message bandwidth utilization and the error incurred in the quantization. The results relate bandwidth utilization and error to quantum size for federations executing in the HLA-compliant discrete event distributed simulation environment, DEVS/HLA. The theoretical and empirical results indicate that quantization can be very scaleable due to reduced local computation demands as well as having extremely favorable network load reduction/simulation fidelity tradeoffs.     

大型分布交互式仿真系统的DDM策略研究

分布交互式仿真是未来仿真技术发展的方向.大型分布交互式仿真应用具有仿真实体数目较大、实体间信息交互频繁等特点,而分布仿真应用基于的网络带宽资源又十分有限,因此如何降低网络冗余数据,充分利用网络带宽资源以提高网络上的有效数据的传输效率成为一个重要的研究方向.数据分发管理DDM的目标是滤除仿真运行中网络上的无用数据,减少网络上的数据.文中简单地介绍了HLA的基本思想,阐述了路径空间的概念,并结合多目组通讯技术,描述了几种实用的DDM策略,比较了它们的异同点,为选用合适的数据滤除方法管理大型分布仿真的大规模数据提供了依据.     

Event based distributed kalman filter for limited resource multirobot cooperative localization

This paper presents a multirobot cooperative event based localization scheme with improved bandwidth usage in a heterogeneous group of mobile robots. The proposed method relies on an agent based framework that defines the communications between robots and on an event based Extended Kalman Filter that performs the cooperative sensor fusion from local, global and relative sources. The event is generated when the pose error covariance exceeds a predefined limit. By this, the robots update the pose using the relative information available only when necessary, using less bandwidth and computational resources when compared to the time based methods, allowing bandwidth allocation for other tasks while extending battery life. The method is tested using a simulation platform developed in the programming language JAVA with a group of differential mobile robots represented by an agent in a JADE framework. The pose estimation performance, error covariance and number of messages exchanged in the communication are measured and used to compare the traditional time based approach with the proposed event based algorithm. Also, the compromise between the accuracy of the localization method and the bandwidth usage is analyzed for different event limits. A final experimental test with two SUMMIT XL robots is shown to validate the simulation results.     

Robust Model Reference Adaptive Intelligent Control

In this paper a Neural Network based Model Reference Adaptive Control scheme (NNMRAC) is proposed. In this scheme, the controller is designed by using parallel combination of the conventional Model Reference Adaptive Control (MRAC) scheme and Neural Network (NN) controller. In the conventional MRAC scheme, the controller is designed to realize plant output converging to reference model output based on the plant which is linear. This scheme is used to control linear plant effectively with unknown parameters. However, it is difficult for a nonlinear system to control the plant output in real time applications. In order to overcome the above limitations, the NN-MRAC scheme is proposed to improve the system performances. The control input of the plant is given by the sum of the MRAC output and NN controller output. The NN controller is used to compensate the nonlinearities and disturbances of the plant that are not taken into consideration in the conventional MRAC. The simulation results clearly show that the proposed NN-MRAC scheme have better steady state and transient performances than those of the current adaptive control schemes. Thus, the proposed NN-MRAC scheme named as Robust Model Reference Adaptive Intelligent Control (RMRAIC) is found to be extremely effective, efficient and useful in the field of control system.     

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．     

LoI: Efficient relevance evaluation and filtering for distributed simulation

In large-scale distributed simulation, thousands of objects keep moving and interacting in a virtual environment, which produces a mass of messages. High level architecture (HLA) is the prevailing standard for modeling and simulation. It specifies two publish-subscribe mechanisms for message filtering: class-based and value-based. However, the two mechanisms can only judge whether a message is relevant to a subscriber or not. Lacking of the ability to evaluate the relevance, all relevant messages are delivered with the same priority even when congestion occurs. It significantly limits the scalability and performance of distributed simulation. Aiming to solve the relevance evaluation problem, speed up message filtering, and filter more unnecessary messages, a new relevance evaluation mechanism Layer of Interest (LoI) was proposed by this paper. LoI defines a relevance classifier based on the impact of spatial distance on receiving attributes and attribute values. An adaptive publish-subscribe scheme was built on the basis of LoI. This scheme can abandon most irrelevant messages directly. Run-time infrastructure (RTI) can also apply congestion control by reducing the frequency of sending or receiving object messages based on each objects’ LoI. The experiment results verify the efficiency of message filtering and RTI congestion control. Supported by the National Basic Research Program of China (Grant No. 2009CB320805), the National Natural Science Foundation of China (Grant No. 60603084), and the National High-Tech Research & Development Program of China (Grant No. 2006AA01Z331)     

HLA/RTI仿真平台的数据分发管理

在基于 HLA/RTI 的大规模交互仿真中,如何高效地实现数据分发管理的信息交互和传递机制是分布式交互仿真的重要内容。介绍了 HLA 仿真中的联邦开发和执行过程模型和数据分发管理,提出了一种新的数据分发管理算法,并阐述了该算法的理论和具体实现方法。通过分析采用该算法的数据分发管理仿真系统,证明该算法有效的提高了数据过滤效率,从而缩短系统仿真时间。     

Active Disturbance Rejection Control in fully distributed Automatic Generation Control with co-simulation of communication delay

This paper compares Active Disturbance Rejection Control (ADRC), Proportional–Integral (PI) and ultra-local adaptive controller (also labeled intelligent-PID) when applied to Automatic Generation Control (AGC) for a multi-area power system with co-simulation of the communication system. The co-simulation platform PiccSIM integrates Simulink/Matlab and Network Simulator Ver. 2 (NS2) with real-time simulation data exchanged through Local Area Network (LAN). Instead of the traditional control center, automatic generation controller is embedded in each participating generation unit locally to avoid communication delay for the control signals. This new control scheme is referred to as fully distributed AGC. The performance of the proposed scheme is tested on a nonlinear model of New England (NE) 39-bus system with the co-simulated tie-line power flow communication delays. The simulation results present very promising performance and good robustness of ADRC in the presence of communication delays, suggesting that the stability of the overall cyber–physical system is enhanced significantly.     

Dynamic Grid-Based Approach to Data Distribution Management

Data distribution management (DDM) is one of the services defined by the DoD High Level Architecture and is necessary to provide efficient, scalable mechanisms for distributing state updates and interaction information in large scale distributed simulations. In this paper, we focus on data distribution management mechanisms (also known as filtering) used for real time training simulations. We propose a new method of DDM, which we refer to as the dynamic grid-based approach. Our scheme is based on a combination of a fixed grid-based method, known for its scalability, and a region-based strategy, which provides greater accuracy than the fixed grid-based method. We describe our DDM algorithm, its implementation, and report on the performance results that we have obtained using the RTI-Kit framework. Our results clearly indicate that our scheme is scalable and that it reduces the message overhead by 40%, and the number of multicast groups used by 98% when compared to the fixed grid-based allocation scheme using 10 nodes, 1000 objects, and 20,000 grid cells.     

车联网中beacon消息传输频率研究

车联网(VANETs)提供车与车之间的车间通信(V2V)和车与路旁设施(V2I)间的通信。VANETs中存在两类消息:beacon消息和安全消息。车辆周期地交互车辆的beacon消息,仅在紧急情况下才广播安全消息;而beacon消息的传输频率受多个因素影响。为此,提出基于模糊逻辑的自适应beacon传输频率方案,利用模糊逻辑系统处理多输入决策系统的优势,将信道忙碌因子、车辆移动因子和数据包传递率作为系统输入,通过模糊逻辑系统决策算出最优的beacon传输频率。使用NS-3仿真软件分析VAB方案的平均等待时间,总体传输时延和平均邻居数等参数。仿真实验表明,VAB方案的性能明显优于JSRC和BRAIN-F方案。     

HLA中基于Agent的层次过滤机制

数据分发管理是HLA接口规范中定义的六大服务之一。为解决仿真应用中网络带宽、处理资源受限和提高系统可扩缩性提供了可能。文章介绍了HLA中数据分发管理DDM的基本内容；研究分析了最新发展的基于移动Agent的实现方法，提出了基于Agent的层次过滤机制对其进行了改进，解决了仿真结点计算能力受限的问题。     

A dynamic information-structure mutual exclusion algorithm fordistributed systems

A dynamic information-structure mutual exclusion algorithm is presented for distributed systems whose information-structure evolves with time as sites learn about the state of the system through messages. An interesting feature of the algorithm is that it adapts itself to heterogeneous or fluctuating traffic conditions to optimize the performance (the number of messages exchanged). The performance of the algorithm is studied by simulation technique and compared to the performance of a well-known mutual exclusion algorithm. The impact message loss and site failures on the algorithm is discussed and methods to tolerate these failures are proposed     

Using a novel message-exchanging optimization (MEO) model to reduce energy consumption in distributed systems

The concept of optimizing energy efficiency in distributed systems has gained particular interest. Most of these efforts are focused on the core management concepts like resource discovery, scheduling and allocation without focusing on the actual communication method among system entities. Specifically, these do not consider the number of exchanged messages and the energy that they consume. In this work, we propose a model to optimize the energy efficiency of message-exchanging in distributed systems by minimizing the total number of messages when entities communicate. So we propose an efficient messaging-exchanging optimization (MEO) model that aims to minimize the sum of requests and responses as a whole rather than only the number of requests. The view is to optimize firstly the energy for communication (e.g. latency times) and secondly the overall system performance (e.g. makespan). To demonstrate the effectiveness of MEO model, the experimental analysis using the SimIC is based on a large-scale inter-cloud setting where the implemented algorithms offer optimization of various criteria including turnaround times and energy consumption rates. Results obtained are very supportive.     

数据分发管理机制中层次化组播地址分配策略

高层体系结构HLA(high level architecture)支持仿真应用间的互操作和可重用,受资源的限制,基于因特网的分布仿真面临着系统可扩缩性的挑战.HLA提供数据分发管理机制,为提高系统可扩缩性提供了可能.分析了HLA中数据分发管理机制的实现途径,针对存在的问题提出了层次化的组播地址分配策略,解决了组播地址数量有限与仿真结点机冗余数据接收之间的矛盾,同时也为仿真数据的可靠发送及数据打包提供了有力的支持.