Tight bounds and probabilistic analysis of two heuristics for parallel processor scheduling
Studies the partitioning problem, consisting in partitioning a sublist of n positive numbers into m disjoint sublists such that the maximum sublist is minimized. This is equivalent to minimizing the completion time of n jobs on m parallel identical processors. The author establishes upper bounds on the deviation from optimality of two heuristics: the well-known LPT heuristic, and the online RLP heuristic. These bounds serve to establish a probabilistic analysis of these heuristics; for both of them, the absolute deviation from optimality remains finite, when the size of the list of numbers becomes infinite. This is a stronger result than previous convergence theorems, and it is valid whenever the processing times are IID random variables with finite mean and arbitrary distributions. 12 references.
- Research Organization:
- McGill Univ., Montreal, Canada
- OSTI ID:
- 6190404
- Journal Information:
- Math. Oper. Res.; (United States), Vol. 1
- Country of Publication:
- United States
- Language:
- English
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