Scheduling parallel machines on-line

The authors study the problem of scheduling jobs on parallel machines when the existence of a job is not known until an unknown release date and the processing requirement of a job is not known until the job is processed to completion. They demonstrate two general algorithmic techniques for converting existing polynomial-time algorithms that require complete knowledge about the input data into algorithms that need less advance knowledge. They prove information-theoretic lower bounds on the lengths of online schedules for several basic parallel machine models and then show that the algorithms construct schedules with lengths that either match or come within a constant factor of the lower bounds.<>