Online computation and competitive analysis
Online computation and competitive analysis
Competitive analysis of randomized paging algorithms
Theoretical Computer Science
Design and Analysis of Randomized Algorithms: Introduction to Design Paradigms (Texts in Theoretical Computer Science. An EATCS Series)
Online Computation with Advice
ICALP '09 Proceedings of the 36th International Colloquium on Automata, Languages and Programming: Part I
On the Advice Complexity of Online Problems
ISAAC '09 Proceedings of the 20th International Symposium on Algorithms and Computation
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
On online algorithms with advice for the k-server problem
WAOA'11 Proceedings of the 9th 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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Recently, a new measurement - the advice complexity - was introduced for measuring the information content of online problems. The aim is to measure the bitwise information that online algorithms lack, causing them to perform worse than offline algorithms. Among a large number of problems, a well-known scheduling problem, job shop scheduling with unit length tasks, and the paging problem were analyzed within this model. We observe some connections between advice complexity and randomization. Our special focus goes to barely random algorithms, i. e., randomized algorithms that use only a constant number of random bits, regardless of the input size. We adapt the results on advice complexity to obtain efficient barely random algorithms for both the job shop scheduling and the paging problem. Furthermore, so far, it has not been investigated for job shop scheduling how good an online algorithm may perform when only using a very small (e. g., constant) number of advice bits. In this paper, we answer this question by giving both lower and upper bounds, and also improve the best known upper bound for optimal algorithms.