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The efficient scheduling of independent computational tasks in aheterogeneous computing environment is an important problem that occurs indomains such as Grid and Cloud computing. Finding optimal schedules is anNP-hard problem in general, so we have to rely on approximate algorithms tocome up schedules that are as near to optimal as possible. In our previous workon this problem, we applied a fast, effective local search to generatereasonably good schedules in a short amount of time and used ant colonyoptimisation (ACO) to incrementally improve those schedules over a longer timeperiod. In this work, we replace the ACO component with a random disruptionalgorithm and find that this produces results which are competitive with thecurrent state of the art over a 90 second execution time. We also ran ouralgorithm for a longer time period on 12 well-known benchmark instances and asa result provide new upper bounds for these instances.

New Results for the Heterogeneous Multi-Processor Scheduling Problem using a Fast, Effective Local Search and Random Disruption