Large-scale Distributed Simulation Systems Research Articles

Distributed Simulation System Hierarchical Design Model Based on Quotient Space Granular Computation

To answer the problem of ambiguous design levels for large-scale distributed simulation systems, this paper proposes a hierarchical system model based on the quotient space theory. This model consists of a system global design level, an architecture level, and a execution structure level to solve the problem of diverse levels on system design. Furthermore, we deeply analyze the relationship between the granules of different levels in both horizontal and vertical directions, and integratedly consider simulation tasks, resources and services. Finally, the model is verified based on a real simulation system design example, and the results illustrate that the flexibility and reusability of the system can be improved with the application of the hierarchical design model.

An Efficient Adaptive Transmission Control Scheme for Large-Scale Distributed Simulation Systems

Data Distribution Management (DDM) is one of the most critical component of any large-scale interactive distributed simulation systems. The aim of DDM is to reduce and control the volume of information exchanged among the simulated entities (federates) in a large-scale distributed simulation system. In order to fulfill its goal, a considerable amount of DDM messages needs to be exchanged within the simulation (federation). The question of whether each message should be sent immediately after it is generated or held until it can be grouped with other DDM messages needs to be investigated further. Our experimental results have shown that the total DDM time of a simulation varies considerably depending on which transmission strategy is used. Moreover, in the case of grouping, the DDM time depends on the size of the group. In this paper, we propose a novel DDM approach, which we refer to as Adaptive Grid-based (AGB) DDM. The AGB protocol is distinct from all existing DDM implementations, because it is able to predict the average amount of data generated in each time step of a simulation. Therefore, the AGB DDM approach controls a simulation running in the most appropriate mode to achieve a desired performance. This new DDM approach consists of two adaptive control parts: 1) the Adaptive Resource Allocation Control (ARAC) scheme and 2) the Adaptive Transmission Control (ATC) scheme. The focus of this paper is on the ATC scheme. We describe how to build a switching model to predict the average amount of DDM messages generated and how the ATC scheme uses this estimation result to optimize the overall DDM time. Our experimental results provide a clear evidence that the ATC scheme is able to achieve the best performance in DDM time when compared to all existing DDM protocols using an extensive set of experimental case studies.

DEVS Approach to Real-time RTI Design for Large-scale Distributed Simulation Systems

HLA/RTI is one of the dominant middleware for large-scale distributed simulation. However, traditional RTI design and resulting implementations have been facing several difficulties, especially in satisfying the requirements for real-time distributed simulations. Many improved or novel RTI designs have been proposed in recent years, which aim to improve the real-time capabilities of RTI systems. Such designs are in general non-formalized, and the realizations of the designs are highly time-consuming and error-prone practices. In this paper, we propose a formal real-time RTI (RT-RTI) design approach using Discrete Event System Specification (DEVS). We discuss the feasibility of using DEVS and, as an additional step, we consider the case study of two recently proposed RT-RTI designs through a formalized DEVS model system. Our focus is how a DEVS component-based formalized design approach can predict some of the key design factors before the design is realized, or can further validate and consolidate realized RT-RTI designs.

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.

An enhancement towards dynamic grid-based DDM protocol for distributed simulation using multiple levels of data filtering

In large-scale distributed simulation systems, participating federates may receive and send a large number of messages to share state data and update subscription and publication regions. In an effort to offer a scalable data distribution management solution (DDM), we propose two variants of DDM scheme, which we refer to as an optimized dynamic grid-based DDM and, grid-filtered region-based DDM. The main focus of data distribution management (DDM) is to limit and control the volume of data exchanged between participating simulated entities (hosts) in large scale distributed simulation systems. In the first variant, we propose an enhancement of a dynamic grid-based DDM scheme, which we refer to as an optimized dynamic grid-based DDM, to further reduce irrelevant data that might be received by simulation entities. In the second variant, we propose a grid-filtered region-based DDM scheme that determines the percentage of grids covered by the subscription or publication region using a grid overlay on the virtual space, and further filters irrelevant exchanged data, based on a percentage threshold, using a matching technique, like that of the region-based DDM scheme. We present our DDM schemes, discuss their implementation and report on their performance evaluation using an extensive set of simulation results.

Resource control for large-scale distributed simulation system over loosely coupled domains

Collaborative and distributed simulation environments that span across large interconnection networks, such as the Internet, are a promising way of supporting applications distributed over multiple computing sites. How-ever, communication-intensive distributed applications may suffer from variable communication delays, jitter, and latency experienced over the network. We term a loosely coupled domain the combination of multiple sites connected through an internetwork. In this paper, we focus on large-scale distributed simulations based upon local time warp deployed over several sites connected through an interconnection network (a loosely coupled domain). We propose and study a mechanism to tune elaboration according to varying communication performances, to reduce resource consumption and rollback costs that result from unpredictable network behavior. The mechanism is composed of two parts that operate in a completely distributed way: a local elaboration rate control and a message flow control. The experimental study is conducted through emulation to exert a control on injected delays. Our results show that the mechanism is able to globally tune the entire simulation, so as to reduce the escalation of allocated computational resources and the cost of rollbacks. As delays disappear, the mechanism allows elaboration to resume normal rate.