Amortized efficiency of list update and paging rules
Communications of the ACM
New algorithms for an ancient scheduling problem
STOC '92 Proceedings of the twenty-fourth annual ACM symposium on Theory of computing
A lower bound for randomized on-line scheduling algorithms
Information Processing Letters
Better bounds for online scheduling
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Optimal time-critical scheduling via resource augmentation (extended abstract)
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
A lower bound for randomized on-line multiprocessor scheduling
Information Processing Letters
STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
A better algorithm for an ancient scheduling problem
SODA '94 Proceedings of the fifth annual ACM-SIAM symposium on Discrete algorithms
Trade-offs between speed and processor in hard-deadline scheduling
Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms
Generating adversaries for request-answer games
SODA '00 Proceedings of the eleventh annual ACM-SIAM symposium on Discrete algorithms
Maximizing Job Completions Online
ESA '98 Proceedings of the 6th Annual European Symposium on Algorithms
Speed is more powerful than clairvoyance
Nordic Journal of Computing
Speed is as powerful as clairvoyance [scheduling problems]
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
On-Line Load Balancing of Temporary Tasks on Identical Machines
ISTCS '97 Proceedings of the Fifth Israel Symposium on the Theory of Computing Systems (ISTCS '97)
A lower bound for on-line scheduling on uniformly related machines
Operations Research Letters
An optimal algorithm for preemptive on-line scheduling
Operations Research Letters
Extending the Accommodating Function
COCOON '02 Proceedings of the 8th Annual International Conference on Computing and Combinatorics
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We consider load balancing in the following setting. The online algorithm is allowed to use n machines, whereas the optimal off-line algorithm is limited to m machines, for some fixed m n. We show that while the greedy algorithm has a competitive ratio which decays linearly in the inverse of n/m, the best on-line algorithm has a ratio which decays exponentially in n/m. Specifically, we give an algorithm with competitive ratio of 1 + 1/2n/m(1-o(1)), and a lower bound of 1 + 1/en/m(1+o(1)) on the competitive ratio of any randomized algorithm. We also consider the preemptive case. We show an on-line algorithm with a competitive ratio of 1 + 1/en/m(1+o(1)). We show that the algorithm is optimal by proving a matching lower bound. We also consider the non-preemptive model with temporary tasks. We prove that for n = m + 1, the greedy algorithm is optimal. (It is not optimal for permanent tasks).