26-28 Jun 2019 Bordeaux (France)
Reservation Strategies for Stochastic Jobs
Guillaume Aupy, Ana Gainaru  1  , Valentin Honoré, Padma Raghavan, Yves Robert  2  , Hongyang Sun  3  
1 : Mellanox
2 : ROMA  (ENS Lyon / CNRS / Inria Grenoble Rhône-Alpes)  -  Website
CNRS : UMR5668, Laboratoire d'informatique du Parallélisme, École Normale Supérieure (ENS) - Lyon, INRIA
Laboratoire de l'Informatique du Parallélisme 46 Allée d'Italie 69364 Lyon -  France
3 : Laboratoire de l'Informatique du Parallélisme  (LIP)  -  Website
École Normale Supérieure (ENS) - Lyon, INRIA, CNRS : UMR5668, PRES Université de Lyon, Université Claude Bernard - Lyon I (UCBL)
46 Allée d'Italie 69364 LYON CEDEX 07 -  France

We are interested in scheduling stochastic jobs on a reservation-based platform. Specifically, we consider jobs whose execution time follows a known probability distribution. The platform is reservation-based, meaning that the user has to request fixed-length time slots. The cost depends on both the request duration and the actual execution time of the job. A reservation strategy is a sequence of increasing-length reservations, which are paid for until one of them allows the job to successfully complete. The goal is to minimize the total expected cost of the strategy. I will present different scheduling strategies and properties of an optimal solution.

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