Optimized Task Allocation on Private Cloud for Hybrid Simulation of Large-Scale Critical Systems

Simulation represents a powerful technique for the analysis of dependability and performance aspects of distributed systems. For large-scale critical systems, simulation demands complex experimentation environments and the integration of different tools, in turn requiring sophisticated modeling skills. Moreover, the criticality of the involved systems implies the set-up of expensive testbeds on private infrastructures. This paper presents a middleware for performing hybrid simulation of large-scale critical systems. The services offered by the middleware allow the integration and interoperability of simulated and emulated subsystems, compliant with the reference interoperability standards, which can provide greater realism of the scenario under test. The hybrid simulation of complex critical systems is a research challenge due to the interoperability issues of emulated and simulated subsystems and to the cost associated with the scenarios to set up, which involve a large number of entities and expensive long running simulations. Therefore, a multi-objective optimization approach is proposed to optimize the simulation task allocation on a private cloud.