Space-based data management for high-performance distributed simulation

Abstract

There is a rapidly growing demand to model and simulate complex large-scale distributed systems and to collaboratively share geographically dispersed data assets and computing resources to perform such distributed simulation with reasonable communication and computation resources. Interest management schemes have been studied in the literature. In this dissertation we propose an interest-based quantization scheme that is created by combining a quantization scheme and an interest management scheme. We show that this approach provides a superior solution to reduce message traffic and network data transmission load. As an environmental platform for data distribution management, we extended the DEVS/HLA distributed modeling and simulation environment. This environment allows us to study interest-based quantization schemes in order to achieve effective reduction of data communication in distributed simulation. In this environment, system modeling is provided by the DEVS (Discrete Event System Specification) formalism and supports effective modeling based on hierarchical and modular object-oriented technology. Distributed simulation is performed by a highly reliable facility using the HLA (High Level Architecture). The extended DEVS/HLA environment, called DEVS/GDDM (Generic Data Distribution Management), provides a high level abstraction to specify a set of interest-based quantization schemes. This dissertation presents a performance analysis of centralized and distributed configurations to study the scalability of the interest-based quantization schemes. These results illustrate the advantages of using space-based quantization in reducing both network load and overall simulation execution time. A real world application, relating to ballistic missiles simulation, demonstrates the operation of the DEVS/GDDM environment. Theoretical and empirical results of the ballistic missiles application show that the space-based quantization scheme, especially with predictive and multiplexing extensions, is very effective and scalable due to reduced local computation demands and extremely favorable communication data reduction with a reasonably small potential for error. This realistic case study establishes that the DEVS/GDDM environment can provide scalable distributed simulation for practical, real-world applications.