Wireless networks’ models differ from wired ones at least in the innovative dynamic effects of host-mobility and open broadcast nature of the wireless medium. Topology changes due to simulated hosts ’ mobility map on causality effects in the “areas of influence ” of each mobile device. The analysis of wireless networks of interest today may include a potentially high number of simulated hosts, resulting in performance and scalability problems for discrete-event sequential simulation tools and methods, on a single physical execution unit (PEU). In a distributed simulation, the main bottleneck becomes the communication and synchronization required to maintain the causality constrains between distributed model components. In this work we propose a HLA-based, dynamic mechanism for the runtime management and allocation of model entities in a distributed simulation of wireless networks models, over a cluster of PEUs. By adopting a runtime evaluation of causal bindings between model entities we map the causal effects of virtual topology changes to dynamic migration of data structures. A prototype migration-heuristic is proposed to dynamically evaluate and balance the migration overheads, and the load distribution, with respect to the reduction in the “external” communication. Preliminary results demonstrate that the mechanism’s heuristics lead to a reduction in the percentage of external communication between the PEUs, limited overheads and performance enhancements for a worst-case scenario.

The simulation of ad hoc and sensor networks often requires a large amount of computation, memory and time to obtain significant results. The parallel and distributed simulation approach can be a valuable solution to reduce the computation time, and to support model components’ modularity and reuse. In this work we perform a testbed evaluation of a new middleware for the simulation of large scale wireless systems. The proposed middleware has been designed to adapt and to scale over a heterogeneous distributed execution infrastructure. To realize a testbed evaluation of the considered framework we implemented and investigated a set of wireless systems ’ models. Specifically, we identified two classes of widely investigated wireless models: mobile ad hoc, and static sensor networks. In this work we present the performances of the simulation framework, with respect to the heterogeneous set of execution architectures, and the modeled systems’ characteristics. Results demonstrate that the framework leads to increased model scalability and speed-up, by transparently adapting and managing at runtime the communication and synchronization overheads, and the load balancing.

Simulation of large-scale networks remains to be a challenge, although various network simulators are in place. In this paper, we identify fundamental issues for large-scale network simulation, and propose new techniques that address them. First, we exploit optimistic parallel simulation techniques to enable fast execution on inexpensive hyper-threaded, multiprocessor systems. Second, we provide a compact, light-weight implementation framework that greatly reduces the amount of state required to simulate large-scale network models. Based on the proposed techniques, we provide sample simulation models for two networking protocols: TCP and OSPF. We implement these models in a simulation environment ROSSNet, which is an extension to the previously developed optimistic simulator ROSS. We perform validation experiments for TCP and OSPF and present performance results of our techniques by simulating OSPF and TCP on a large and realistic topology, such as AT&T's US network based on Rocketfuel data. The end result of these innovations is that we are able to simulate million node network topologies using inexpensive commercial off-the-shelf hyperthreaded multiprocessor systems consuming less than 1.4 GB of RAM in total.

In this work we define and test a new framework obtained as the integration of two recently developed middlewares defined to support the parallel and distributed simulation of large scale, complex and dynamically interacting system models (like wireless and mobile network systems). In a distributed simulation of highly interacting system models, the main bottleneck may become the communication and synchronization required to maintain the causality constrains between distributed model components. We designed and implemented the ARTÌS middleware as a new framework incorporating a set of features that allow an adaptive optimization of the communication layer management in a distributed simulation scenario. ARTÌS has been integrated with GAIA, a dynamic mechanism for the runtime management and adaptive allocation of model entities in a distributed simulation. By adopting a runtime evaluation of causal bindings between model entities GAIA adapts the dynamic and time-persistent causal effects of model interactions to dynamic migration of model entities. Preliminary results demonstrate that the combined effect of ARTÌS management and GAIA heuristics leads to a significant reduction in the communication and synchronization overheads between the physical execution units. Simulation performance enhancements have been obtained also in worst-case modelling assumptions and simulation scenarios. 1.

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 ARTÌS 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. 1.