ABSTRACT
Symbiosis has its origins in biology where it refers to organisms of different species which exist in a long-lasting and close association with each other. The partners in a symbiosis, also referred to as symbionts, are highly dependent upon each other in regard to their outcome. Depending on the definition of symbiosis, this relationship is either mutually beneficial to both (i.e, mutualism) or at least beneficial to one of the symbionts involved [Wilkinson, 2001; Douglas, 1994]. Similarly, symbiotic simulation describes a paradigm in which a simulation system and a physical system are closely associated with each other. In such a relationship, the simulation system benefits from sensor data while the physical system may benefit from conclusions drawn, based on the simulation results. Symbiotic simulation has been an active
field of research since the term was coined at the Dagstuhl Seminar on Grand Challenges for Modeling and Simulation in 2002 [Fujimoto et al., 2002]. Although the original definition of symbiotic simulation is more precisely referring to mutualism we will use the wider definition of symbiotic simulation [Aydt et al., 2008a] which is based on the original meaning of symbiosis [Douglas, 1994]. The extended definition emphasizes the close association between a simulation system and a physical system which is to the benefit of at least one of them. In addition, control feedback to the physical system is optional. This distinguishes the extended definition from the original one, in which a control feedback to the physical system is considered mandatory.
