Discrete optimization using decision-directed learning for distributed networked computing

Decision-directed learning (DDL) is an iterative discrete approach to finding a feasible solution for large-scale combinatorial optimization problems. DDL is capable of efficiently formulating a solution to network scheduling problems that involve load limiting device utilization, selecting parallel configurations for software applications and host hardware using a minimum set of resources, and meeting time-to-result performance requirements in a dynamic network environment. The paper quantifies the algorithms that constitute DDL and compares its performance to other popular combinatorial optimization techniques. This is done within the context of self-directed real-time networked resource configuration for dynamically building a mission specific signal-processor for real-time distributed and parallel applications.