We present a multi-heuristic evolutionary task allocation algorithm to dynamically map tasks to processors in a heterogeneous distributed system. It utilizes a genetic algorithm, combined with eight common heuristics, in an effort to minimize the total execution time. It operates on batches of unmapped tasks and can preemptively remap tasks to processors. The algorithm has been implemented on a Java distributed system and evaluated with a set of six problems from the areas of bioinformatics, biomedical engineering, computer science and cryptography. Experiments using up to 150 heterogeneous processors show that the algorithm achieves better efficiency than other state-of-the-art heuristic algorithms.
Page et al. REF developed a task scheduler for dynamic heterogeneous systems based on a multi-heuristic genetic algorithm.
Multi-heuristic dynamic task allocation using genetic algorithms in a heterogeneous distributed system
Journal of Parallel and Distributed Computing
Journal of Parallel and Distributed Computing
Computer Science Medicine