Provided by: University of Stirling
Date Added: Oct 2012
The efficient scheduling of independent computational tasks in a heterogeneous computing environment is an important problem that occurs in domains such as grid and cloud computing. Finding optimal schedules is an NP-hard problem in general, so the authors have to rely on approximate algorithms to come up schedules that are as near to optimal as possible. In their previous work on this problem, they applied a fast, effective local search to generate reasonably good schedules in a short amount of time and used Ant Colony Optimization (ACO) to incrementally improve those schedules over a longer time period.