Engineering Optimization Models at Runtime for Dynamically Adaptive Systems

Dynamically adaptive systems (DAS), such as smart grids, cloud computing applications, sensor networks and P2P networks tend to change their structure at runtime. Therefore, design-time modeling for such systems are sometimes not enough for self-management. To this end, we have developed a dynamic mathematical modeling framework for runtime modeling for DAS. In this paper, we describe how our system engineers a linear programming model for self-optimization by using a smart-grid application for power distribution as a case-study. At runtime whenever, an optimization is desired this modeling framework captures the state of the system, converts it into an appropriate linear programming model, plan the changes using mathematical manipulations and apply the changes to the actual system. Our initial simulation results show that this framework is able to capture accurate runtime models of large power systems and is able to adapt itself with the change in the size or structure of the system by constructing a succinct model which is faster and more efficient than a design time model.