Author name cluster

Tim Oosterwijk

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3 papers

2 author rows

TCS Journal 2025 Journal Article

Competitive mechanisms for energy-efficient cloud computing

Antonios Antoniadis
Andrés Cristi
Tim Oosterwijk
Alkmini Sgouritsa

We present a general model for the operation of a cloud computing server comprised of one or more speed-scalable processors. Typically, agents submit tasks to such a cloud computing server in an online fashion, and the server operator has to schedule the tasks and decide on payments without knowledge of tasks arriving in the future. Moreover, the operator should take the different incentives of the agents into account and aim to minimize the energy expenditure. For both the offline and the online setting we provide mechanisms with several desirable properties: The induced game admits a Nash equilibrium, the mechanism is budget balanced, has low communication complexity, is computationally tractable, is intuitive to explain, but above all, has a constant Price of Anarchy. Therefore, the total costs are not too far off from the social optimum. We extend our results to the case of multiple processors and to the Bayesian setting.

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MFCS Conference 2023 Conference Paper

Exact and Approximation Algorithms for Routing a Convoy Through a Graph

Martijn van Ee
Tim Oosterwijk
René Sitters
Andreas Wiese

We study routing problems of a convoy in a graph, generalizing the shortest path problem (SPP), the travelling salesperson problem (TSP), and the Chinese postman problem (CPP) which are all well-studied in the classical (non-convoy) setting. We assume that each edge in the graph has a length and a speed at which it can be traversed and that our convoy has a given length. While the convoy moves through the graph, parts of it can be located on different edges. For safety requirements, at all time the whole convoy needs to travel at the same speed which is dictated by the slowest edge on which currently a part of the convoy is located. For Convoy-SPP, we give a strongly polynomial time exact algorithm. For Convoy-TSP, we provide an O(log n)-approximation algorithm and an O(1)-approximation algorithm for trees. Both results carry over to Convoy-CPP which - maybe surprisingly - we prove to be NP-hard in the convoy setting. This contrasts the non-convoy setting in which the problem is polynomial time solvable.

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SODA Conference 2021 Conference Paper

The Secretary Problem with Independent Sampling

José Correa 0001
Andrés Cristi
Laurent Feuilloley
Tim Oosterwijk
Alexandros Tsigonias-Dimitriadis

In the secretary problem we are faced with an online sequence of elements with values. Upon seeing an element we have to make an irrevocable take-it-or-leave-it decision. The goal is to maximize the probability of picking the element of maximum value. The most classic version of the problem is that in which the elements arrive in random order and their values are arbitrary. Here, the optimal algorithm picks the maximum value with probability at least 1/ e. However, by varying the available information, new interesting problems arise. For instance, in the full information variant of the secretary problem the values are i. i. d. samples from a known distribution. Naturally, the best possible success probability increases and turns out to be approximately 0. 58. Also, the case in which the arrival order is adversarial instead of random leads to interesting variants that have been considered in the literature. In this paper we study both the random order and adversarial order secretary problems with an additional twist. The values are arbitrary, but before starting the online sequence we independently sample each element with a fixed probability p. The sampled elements become our information or history set and the game is played over the remaining elements. We call these problems the random order secretary problem with p -sampling (ROS p for short) and the adversarial order secretary problem with p -sampling (AOS p for short). Our main result is to obtain best possible algorithms for both problems and all values of p. As p grows to 1 the obtained guarantees converge to the optimal guarantees in the full information case. In the adversarial order setting, the best possible algorithm turns out to be a simple fixed threshold algorithm in which the optimal threshold is a function of p only. Therefore, even knowledge of the total number of elements is unnecessary. Proving that this algorithm is optimal involves a novel technique, which boils down to analyzing a related game in a conflict graph over binary sequences. In the random order setting we prove that the best possible algorithm is characterized by a fixed sequence of time thresholds, dictating at which point in time we should start accepting a value that is both a maximum of the online sequence and has a given ranking within the sampled elements. Surprisingly, this sequence of time thresholds arises from a separable and convex optimization problem whose solution is independent of p.

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