Using guided local search for adaptive resource reservation in large-scale embedded systems

To maintain a predictable execution environment, an embedded system must ensure that applications are, in advance, provided with sufficient resources to process tasks, exchange information and to control peripherals. The problem of assigning tasks to processing elements with limited resources, and routing communication channels through a capacitated interconnect is combined into an integer linear programming formulation. We describe a guided local search algorithm to solve this problem at run-time. This algorithm allows for a hybrid strategy where configurations computed at design-time may be used as references to lower the computational overhead at runtime. Computational experiments on a dataset with 100 tasks and 20 processing elements show the effectiveness of this algorithm compared to state-of-the-art solvers CPLEX and Gurobi. The guided local search algorithm finds an initial solution within 100 milliseconds, is competitive for small platforms, scales better with the size of the platform, and has lower memory usage (2-19%).