Controlling Negative Emergent Behavior by Graph Analysis at Runtime

Self-organized systems typically consist of distributed autonomous entities. An increasing part of such systems is characterized by openness and heterogeneity of participants. For instance, open desktop computing grids provide a framework for unrestrictedly joining in. However, openness and heterogeneity present severe challenges to the overall system’s stability and efficiency since uncooperative and even malicious participants are free to join. A promising solution for this problem is to introduce technical trust as a basis; however, in turn, the utilization of trust opens space for negative emergent behavior. This article introduces a system-wide observation and control loop that influences the self-organized behavior to provide a performant and robust platform for benevolent participants. Thereby, the observation part is responsible for gathering information and deriving a system description. We introduce a graph-based approach to identify groups of suspicious or malicious agents and demonstrate that this clustering process is highly successful for the considered stereotype agent behaviors. In addition, the controller part guides the system behavior by issuing norms that make use of incentives and sanctions. We further present a concept for closing the control loop and show experimental results that highlight the potential benefit of establishing such a control loop.