Competitive algorithms for server problems
Journal of Algorithms
Competitive paging with locality of reference
STOC '91 Proceedings of the twenty-third annual ACM symposium on Theory of computing
An optimal on-line algorithm for metrical task system
Journal of the ACM (JACM)
Journal of the ACM (JACM)
Randomized and multipointer paging with locality of reference
STOC '95 Proceedings of the twenty-seventh annual ACM symposium on Theory of computing
Randomized algorithms for metrical task systems
Theoretical Computer Science
A competitive analysis of the list update problem with lookahead
Theoretical Computer Science
Online computation and competitive analysis
Online computation and competitive analysis
On competitive on-line paging with lookahead
Theoretical Computer Science
Online bin packing with lookahead
Proceedings of the sixth annual ACM-SIAM symposium on Discrete algorithms
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Better Algorithms for Unfair Metrical Task Systems and Applications
SIAM Journal on Computing
A tight bound on approximating arbitrary metrics by tree metrics
Journal of Computer and System Sciences - Special issue: STOC 2003
Proceedings of the twenty-fourth annual ACM symposium on Principles of distributed computing
Distributed verification of minimum spanning trees
Proceedings of the twenty-fifth annual ACM symposium on Principles of distributed computing
Oracle size: a new measure of difficulty for communication tasks
Proceedings of the twenty-fifth annual ACM symposium on Principles of distributed computing
Ramsey-type theorems for metric spaces with applications to online problems
Journal of Computer and System Sciences - Special issue on FOCS 2001
Informative labeling schemes for graphs
Theoretical Computer Science - Mathematical foundations of computer science 2000
Local MST computation with short advice
Proceedings of the nineteenth annual ACM symposium on Parallel algorithms and architectures
Trade-offs between the size of advice and broadcasting time in trees
Proceedings of the twentieth annual symposium on Parallelism in algorithms and architectures
SIROCCO'07 Proceedings of the 14th international conference on Structural information and communication complexity
How much information about the future is needed?
SOFSEM'08 Proceedings of the 34th conference on Current trends in theory and practice of computer science
Label-guided graph exploration by a finite automaton
ICALP'05 Proceedings of the 32nd international conference on Automata, Languages and Programming
Tree exploration with an oracle
MFCS'06 Proceedings of the 31st international conference on Mathematical Foundations of Computer Science
Distributed computing with advice: information sensitivity of graph coloring
ICALP'07 Proceedings of the 34th international conference on Automata, Languages and Programming
On the Advice Complexity of Online Problems
ISAAC '09 Proceedings of the 20th International Symposium on Algorithms and Computation
On the additive constant of the k-server Work Function Algorithm
Information Processing Letters
Advice complexity and barely random algorithms
SOFSEM'11 Proceedings of the 37th international conference on Current trends in theory and practice of computer science
On the advice complexity of the k-server problem
ICALP'11 Proceedings of the 38th international colloquim conference on Automata, languages and programming - Volume Part I
Journal of Parallel and Distributed Computing
On the additive constant of the k-server work function algorithm
WAOA'09 Proceedings of the 7th international conference on Approximation and Online Algorithms
Advice complexity of online coloring for paths
LATA'12 Proceedings of the 6th international conference on Language and Automata Theory and Applications
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We consider a model for online computation in which the online algorithm receives, together with each request, some information regarding the future, referred to as advice . The advice provided to the online algorithm may allow an improvement in its performance, compared to the classical model of complete lack of information regarding the future. We are interested in the impact of such advice on the competitive ratio, and in particular, in the relation between the size b of the advice, measured in terms of bits of information per request, and the (improved) competitive ratio. Since b = 0 corresponds to the classical online model, and $ b = \lceil \log |\mathcal{A}| \rceil $, where $\mathcal{A}$ is the algorithm's action space, corresponds to the optimal (offline) one, our model spans a spectrum of settings ranging from classical online algorithms to offline ones. In this paper we propose the above model and illustrate its applicability by considering two of the most extensively studied online problems, namely, metrical task systems (MTS) and the k-server problem. For MTS we establish tight (up to constant factors) upper and lower bounds on the competitive ratio of deterministic and randomized online algorithms with advice for any choice of 1 ≤ b ≤ *** (logn ) , where n is the number of states in the system: we prove that any randomized online algorithm for MTS has competitive ratio *** ( log(n ) / b ) and we present a deterministic online algorithm for MTS with competitive ratio O (log(n ) / b ) . For the k -server problem we construct a deterministic online algorithm for general metric spaces with competitive ratio k O (1 / b ) for any choice of *** (1) ≤ b ≤ logk .