Anonymity and Confidentiality in Secure Distributed Simulation

Research on data confidentiality, integrity and availability is gaining momentum in the ICT community, due to the intrinsically insecure nature of the Internet. While many distributed systems and services are now based on secure communication protocols to avoid eavesdropping and protect confidentiality, the techniques usually employed in distributed simulations do not consider these issues at all. This is probably due to the fact that many real-world simulators rely on monolithic, offline approaches and therefore the issues above do not apply. However, the complexity of the systems to be simulated, and the rise of distributed and cloud based simulation, now impose the adoption of secure simulation architectures. This paper presents a solution to ensure both anonymity and confidentiality in distributed simulations. A performance evaluation based on an anonymized distributed simulator is used for quantifying the performance penalty for being anonymous. The obtained results show that this is a viable solution.Comment: Proceedings of the IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications (DS-RT 2018

Fault Tolerant Adaptive Parallel and Distributed Simulation through Functional Replication

This paper presents FT-GAIA, a software-based fault-tolerant parallel and distributed simulation middleware. FT-GAIA has being designed to reliably handle Parallel And Distributed Simulation (PADS) models, which are needed to properly simulate and analyze complex systems arising in any kind of scientific or engineering field. PADS takes advantage of multiple execution units run in multicore processors, cluster of workstations or HPC systems. However, large computing systems, such as HPC systems that include hundreds of thousands of computing nodes, have to handle frequent failures of some components. To cope with this issue, FT-GAIA transparently replicates simulation entities and distributes them on multiple execution nodes. This allows the simulation to tolerate crash-failures of computing nodes. Moreover, FT-GAIA offers some protection against Byzantine failures, since interaction messages among the simulated entities are replicated as well, so that the receiving entity can identify and discard corrupted messages. Results from an analytical model and from an experimental evaluation show that FT-GAIA provides a high degree of fault tolerance, at the cost of a moderate increase in the computational load of the execution units.Comment: arXiv admin note: substantial text overlap with arXiv:1606.0731

Scalable and Efficient Parallel and Distributed Simulation of Complex, Dynamic and Mobile Systems.

In this work we illustrate the design and implementation guidelines of a recently developed middleware defined to support the parallel and distributed simulation of large scale, complex and dynamically interacting system models. The distributed simulation of complex system models, may suffer the communication and synchronization required to maintain the causality constraints between distributed model components. We designed and implemented the ART\uccS middleware as a new framework by incorporating a set of features that allow adaptive optimization by exploiting many complex and dynamic model and distributed simulation characteristics. As an example, a dynamic migration mechanism for the run-time adaptive allocation of model entities has been designed and exploited for dynamic load and communication balancing. Optimizations have been introduced to obtain the maximum advantage from heterogeneous and asymmetric communication systems, from shared memory to LAN and Internet communication. Other optimizations have been introduced by the exploitation of concurrent replications of parallel and distributed simulations, in order to increase the resources utilization and to maximize the speedup of simulation processes. Solutions have been designed, implemented and tuned to obtain a significant reduction in the communication and synchronization overheads between the physical execution units, and an increased model scalability and simulation speedup, even in worst-case modeling assumptions and simulation scenarios