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The model of advice complexity offers an alternative measurement allowing for a more fine-grained analysis of the hardness of online problems than standard competitive analysis. Here, one measures the amount of information an online algorithm is lacking about the yet unrevealed parts of the input. This model was successfully used for many online problems including the k-server problem. We extend the analysis of the k-server problem by giving a lower bound on the advice necessary to obtain optimality, and upper bounds on online algorithms for the general and the Euclidean case. For the general case, we improve the previous results by an exponential factor; for the Euclidean case, we achieve a constant competitive ratio using constantly many advice bits per request. Furthermore, for many online problems, we show how lower bounds on the advice complexity can be transformed into lower bounds on the expected competitive ratio of randomized online algorithms.

The model of advice complexity offers an alternative measurement allowing for a more fine-grained analysis of the hardness of online problems than standard competitive analysis. Here, one measures the amount of information an online algorithm is lacking about the yet unrevealed parts of the input. This model was successfully used for many online problems including the k-server problem. We extend the analysis of the k-server problem by giving a lower bound on the advice necessary to obtain optimality, and upper bounds on online algorithms for the general and the Euclidean case. For the general case, we improve the previous results by an exponential factor; for the Euclidean case, we achieve a constant competitive ratio using constantly many advice bits per request. Furthermore, for many online problems, we show how lower bounds on the advice complexity can be transformed into lower bounds on the expected competitive ratio of randomized online algorithms.

We study the advice complexity and the random bit complexity of the online knapsack problem. Given a knapsack of unit capacity, and n items that arrive in successive time steps, an online algorithm has to decide for every item whether it gets packed into the knapsack or not. The goal is to maximize the value of the items in the knapsack without exceeding its capacity. In the model of advice complexity of online problems, one asks how many bits of advice about the unknown parts of the input are both necessary and sufficient to achieve a specific competitive ratio. It is well-known that even the unweighted online knapsack problem does not admit any competitive deterministic online algorithm.For this problem, we show that a single bit of advice helps a deterministic online algorithm to become 2-competitive, but that Ω(log n) advice bits are necessary to further improve the deterministic competitive ratio. This is the first time that such a phase transition for the number of advice bits has been observed for any problem. Additionally, we show that, surprisingly, instead of an advice bit, a single random bit allows for a competitive ratio of 2, and any further amount of randomness does not improve this. Moreover, we prove that, in a resource augmentation model, i. e., when allowing the online algorithm to overpack the knapsack by some small amount, a constant number of advice bits suffices to achieve a near-optimal competitive ratio.We also study the weighted version of the problem proving that, with O(log n) bits of advice, we can get arbitrarily close to an optimal solution and, using asymptotically fewer bits, we are not competitive. Furthermore, we show that an arbitrary number of random bits does not permit a constant competitive ratio.