126 research outputs found

    Pioneering Doktormutter Remembering Ina-Maria Greverus

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    The author reconsiders German scholar Ina-Maria Greverus as a committed feminist supporter of female doctoral students and early career academics. Greverus acted as an innovator especially in the realms of anthropology and aesthetics, and initiated a new international dialogue forum with the Anthropological Journal or European Cultures, which she founded in 1990 together with Christian Giordano.</p

    Asymptotically optimal priority policies for indexable and non-indexable restless bandits

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    We study the asymptotic optimal control of multi-class restless bandits. A restless bandit is a controllable stochastic process whose state evolution depends on whether or not the bandit is made active. Since finding the optimal control is typically intractable, we propose a class of priority policies that are proved to be asymptotically optimal under a global attractor property and a technical condition. We consider both a fixed population of bandits as well as a dynamic population where bandits can depart and arrive. As an example of a dynamic population of bandits, we analyze a multi-class M/M/S+M queue for which we show asymptotic optimality of an index policy.We combine fluid-scaling techniques with linear programming results to prove that when bandits are indexable, Whittle's index policy is included in our class of priority policies. We thereby generalize a result of Weber and Weiss (1990) about asymptotic optimality of Whittle's index policy to settings with (i) several classes of bandits, (ii) arrivals of new bandits, and (iii) multiple actions. Indexability of the bandits is not required for our results to hold. For non-indexable bandits we describe how to select priority policies from the class of asymptotically optimal policies and present numerical evidence that, outside the asymptotic regime, the performance of our proposed priority policies is nearly optimal

    Asymptotically optimal priority policies for indexable and nonindexable restless bandits

    No full text
    We study the asymptotic optimal control of multi-class restless bandits. A restless bandit is a controllable stochastic process whose state evolution depends on whether or not the bandit is made active. Since finding the optimal control is typically intractable, we propose a class of priority policies that are proved to be asymptotically optimal under a global attractor property and a technical condition. We consider both a fixed population of bandits as well as a dynamic population where bandits can depart and arrive. As an example of a dynamic population of bandits, we analyze a multi-class M/M/S+M queue for which we show asymptotic optimality of an index policy. We combine fluid-scaling techniques with linear programming results to prove that when bandits are indexable, Whittle's index policy is included in our class of priority policies. We thereby generalize a result of Weber and Weiss (1990) about asymptotic optimality of Whittle's index policy to settings with (i) several classes of bandits, (ii) arrivals of new bandits, and (iii) multiple actions. Indexability of the bandits is not required for our results to hold. For non-indexable bandits we describe how to select priority policies from the class of asymptotically optimal policies and present numerical evidence that, outside the asymptotic regime, the performance of our proposed priority policies is nearly optimal

    Asymptotically optimal priority policies for indexable and non-indexable restless bandits

    No full text
    We study the asymptotic optimal control of multi-class restless bandits. A restless bandit is a controllable stochastic process whose state evolution depends on whether or not the bandit is made active. Since finding the optimal control is typically intractable, we propose a class of priority policies that are proved to be asymptotically optimal under a global attractor property and a technical condition. We consider both a fixed population of bandits as well as a dynamic population where bandits can depart and arrive. As an example of a dynamic population of bandits, we analyze a multi-class M/M/S+M queue for which we show asymptotic optimality of an index policy.We combine fluid-scaling techniques with linear programming results to prove that when bandits are indexable, Whittle's index policy is included in our class of priority policies. We thereby generalize a result of Weber and Weiss (1990) about asymptotic optimality of Whittle's index policy to settings with (i) several classes of bandits, (ii) arrivals of new bandits, and (iii) multiple actions. Indexability of the bandits is not required for our results to hold. For non-indexable bandits we describe how to select priority policies from the class of asymptotically optimal policies and present numerical evidence that, outside the asymptotic regime, the performance of our proposed priority policies is nearly optimal

    Asymptotic Optimal Control of Markov-Modulated Restless Bandits

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    International audienceThis paper studies optimal control subject to changing conditions. This is an area that recently received a lot of attention as it arises in numerous situations in practice. Some applications being cloud computing systems where the arrival rates of new jobs fluctuate over time, or the time-varying capacity as encountered in power-aware systems or wireless downlink channels. To study this, we focus on a restless bandit model, which has proved to be a powerful stochastic optimization framework to model scheduling of activities. In particular, it has been extensively applied in the context of optimal control of computing systems. This paper is a first step to its optimal control when restless bandits are subject to changing conditions, the latter being modeled by Markov-modulated environments. We consider the restless bandit problem in an asymptotic regime, which is obtained by letting the population of bandits grow large, and letting the environment change relatively fast. We present sufficient conditions for a policy to be asymptotically optimal and show that a set of priority policies satisfies these. Under an indexability assumption, an averaged version of Whittle's index policy is proved to be inside this set of asymptotic optimal policies. The performance of the averaged Whittle's index policy is numerically evaluated for a multi-class scheduling problem in a wireless downlink subject to changing conditions. While keeping the number of bandits constant, we observe that the average Whittle index policy becomes close to optimal as the speed of the modulated environment increases
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