Amortized efficiency of list update and paging rules
Communications of the ACM
An optimal online algorithm for metrical task systems
STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
Approximation of Pareto optima in multiple-objective, shortest-path problems
Operations Research
Competitive algorithms for server problems
Journal of Algorithms
An optimal on-line algorithm for K-servers on trees
SIAM Journal on Computing
e-approximations with minimum packing constraint violation (extended abstract)
STOC '92 Proceedings of the twenty-fourth annual ACM symposium on Theory of computing
Many birds with one stone: multi-objective approximation algorithms
STOC '93 Proceedings of the twenty-fifth annual ACM symposium on Theory of computing
Journal of the ACM (JACM)
Online computation and competitive analysis
Online computation and competitive analysis
Improving spanning trees by upgrading nodes
Theoretical Computer Science
Better Bounds for Online Scheduling
SIAM Journal on Computing
Combining fairness with throughput: online routing with multiple objectives
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
Existence theorems, lower bounds and algorithms for scheduling to meet two objectives
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
Service-Constrained Network Design Problems
SWAT '96 Proceedings of the 5th Scandinavian Workshop on Algorithm Theory
The Constrained Minimum Spanning Tree Problem (Extended Abstract)
SWAT '96 Proceedings of the 5th Scandinavian Workshop on Algorithm Theory
Bicriteria Network Design Problems
ICALP '95 Proceedings of the 22nd International Colloquium on Automata, Languages and Programming
Experimental analysis of online algorithms for the bicriteria scheduling problem
Journal of Parallel and Distributed Computing
Pareto approximations for the bicriteria scheduling problem
Journal of Parallel and Distributed Computing
Competitive algorithms for the bicriteria k-server problem
Discrete Applied Mathematics - Special issue: International symposium on combinatorial optimization CO'02
Competitive k-server algorithms
SFCS '90 Proceedings of the 31st Annual Symposium on Foundations of Computer Science
Rapid rumor ramification: approximating the minimum broadcast time
SFCS '94 Proceedings of the 35th Annual Symposium on Foundations of Computer Science
Online Balancing Two Independent Criteria
NPC '08 Proceedings of the IFIP International Conference on Network and Parallel Computing
Operations Research Letters
A new approach to solve the k-server problem based on network flows and flow cost reduction
Computers and Operations Research
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In this article we consider multicriteria formulations of classical online problems in which an algorithm must simultaneously perform well with respect to two different cost measures. Every strategy for serving a sequence of requests is characterized by a pair of costs and therefore there can be many different minimal or optimal incomparable solutions. The adversary is assumed to choose from one of these minimal strategies and the performance of the algorithm is measured with respect to the costs the adversary pays servicing the sequence according to its determined choice of strategy. We consider a parametric family of functions which includes all the possible selections for such strategies. Then, starting from a simple general method that combines any multicriteria instance into a single-criterion one, we provide a universal multicriteria algorithm that can be applied to different online problems. In the multicriteria k-server formulation with two different edge weightings, for each function class, such a universal algorithm achieves competitive ratios that are only an O(log W) multiplicative factor away from the corresponding determined lower bounds, where W is the maximum ratio between the two weights associated to each edge. We then extend our results to two specific functions, for which nearly optimal competitive algorithms are obtained by exploiting more knowledge of the selection properties. Finally, we show how to apply our framework to other multicriteria online problems sharing similar properties.