Approximation algorithms for scheduling unrelated parallel machines
Mathematical Programming: Series A and B
The competitiveness of on-line assignments
Journal of Algorithms
Theoretical Computer Science - Special issue on dynamic and on-line algorithms
On-line load balancing of temporary tasks
Journal of Algorithms
Better bounds for online scheduling
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
An improved lower bound for load balancing of tasks with unknown duration
Information Processing Letters
Greedy strikes back: improved facility location algorithms
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
On-line algorithms for the channel assignment problem in cellular networks (extended abstract)
DIALM '00 Proceedings of the 4th international workshop on Discrete algorithms and methods for mobile computing and communications
On-Line Load Balancing in a Hierarchical Server Topology
SIAM Journal on Computing
Developments from a June 1996 seminar on Online algorithms: the state of the art
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)
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We provide a new simpler approach to the on-line load balancing problem in the case of restricted assignment of temporary weighted tasks. The approach is very general and allows to derive online distributed algorithms whose competitive ratio is characterized by some combinatorial properties of the underlying graph representing the problem. The effectiveness of our approach is shown by the hierarchical server model introduced by Bar-Noy et al '99. In this case, our method yields simpler and distributed algorithms whose competitive ratio is at least as good as the existing ones. Moreover, the resulting algorithms and their analysis turn out to be simpler. Finally, in all cases the algorithms are optimal up to a constant factor. Some of our results are obtained via a combinatorial characterization of those graphs for which our technique yields O(√n)-competitive algorithms.