Tight Bounds for Dynamic Storage Allocation
SIAM Journal on Discrete Mathematics
Online computation and competitive analysis
Online computation and competitive analysis
Algorithms for compile-time memory optimization
Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms
An Estimate of the Store Size Necessary for Dynamic Storage Allocation
Journal of the ACM (JACM)
Bounds for Some Functions Concerning Dynamic Storage Allocation
Journal of the ACM (JACM)
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Dynamic Storage Allocation: A Survey and Critical Review
IWMM '95 Proceedings of the International Workshop on Memory Management
Approximation Algorithms for Dynamic Storage Allocations
ESA '96 Proceedings of the Fourth Annual European Symposium on Algorithms
Dynamic Storage Allocation with Known Durations
ESA '97 Proceedings of the 5th Annual European Symposium on Algorithms
Single and multiple device DSA problems, complexities and online algorithms
Theoretical Computer Science
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For the classic dynamic storage/spectrum allocation problem, we show that knowledge of the durations of the requests is of no great use to an online algorithm in the worst case. This answers an open question posed by Naor, Orda, and Petruschka [9]. More precisely, we show that the competitive ratio of every randomized algorithm against an oblivious adversary is Ω(log x/log log x), where x may be any of several different parameters used in the literature. It is known that First Fit, which does not require knowledge of the durations of the task, is logarithmically competitive in these parameters.