Extending the Accommodating Function
COCOON '02 Proceedings of the 8th Annual International Conference on Computing and Combinatorics
On-Line Maximizing the Number of Items Packed in Variable-Sized Bins
COCOON '02 Proceedings of the 8th Annual International Conference on Computing and Combinatorics
The competitive ratio for on-line dual bin packing with restricted input sequences
Nordic Journal of Computing
On-line maximizing the number of items packed in variable-sized bins
Acta Cybernetica
Tight bounds for online class-constrained packing
Theoretical Computer Science - Latin American theorotical informatics
Seat reservation allowing seat changes
Journal of Algorithms
The relative worst order ratio applied to paging
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
SIGACT news online algorithms column 8
ACM SIGACT News
The relative worst order ratio for online algorithms
ACM Transactions on Algorithms (TALG)
The relative worst-order ratio applied to paging
Journal of Computer and System Sciences
The relative worst order ratio applied to seat reservation
ACM Transactions on Algorithms (TALG)
Paging and list update under bijective analysis
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Introduction to the SIGACT news online algorithms column
ACM SIGACT News
On Developing New Models, with Paging as a Case Study
ACM SIGACT News
The relative worst order ratio for on-line algorithms
CIAC'03 Proceedings of the 5th Italian conference on Algorithms and complexity
Closing the gap between theory and practice: new measures for on-line algorithm analysis
WALCOM'08 Proceedings of the 2nd international conference on Algorithms and computation
Paging and list update under bijective analysis
Journal of the ACM (JACM)
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A new measure, the accommodating function, for the quality of on-line algorithms is presented. The accommodating function, which is a generalization of both the competitive ratio and the competitive ratio on accommodating sequences, measures the quality of an on-line algorithm as a function of the resources that would be sufficient for an optimal off-line algorithm to fully grant all requests. More precisely, if we have some amount of resources n, the function value at $\alpha$ is the usual ratio (still on some fixed amount of resources n), except that input sequences are restricted to those where the optimal off-line algorithm will not obtain a better result by having more than the amount $\alpha n$ of resources.The accommodating functions for three specific on-line problems are investigated: a variant of bin packing in which the goal is to maximize the number of items put in n bins, the seat reservation problem, and the problem of optimizing total flow time when preemption is allowed.We also show that when trying to distinguish between two algorithms, the decision as to which one performs better cannot necessarily be made from the competitive ratio or the competitive ratio on accommodating sequences alone. For the variant of bin-packing considered, we show that Worst-Fit has a strictly better competitive ratio than First-Fit, while First-Fit has a strictly better competitive ratio on accommodating sequences than Worst-Fit.