Abstract
Current theoretical attempts towards understanding real-life leasing scenarios assume the following leasing model. Demands arrive with time and need to be served by leased resources. Different types of leases are available, each with a fixed duration and price, respecting economy of scale (longer leases cost less per unit time). An online algorithm is to serve each arriving demand while minimizing the total leasing costs and without knowing future demands. In this paper, we generalize this model into one in which lease prices fluctuate with time and are not known to the algorithm in advance. Hence, an online algorithm is to perform under the uncertainty of both demands and lease prices. We consider different adversarial models and provide online algorithms, evaluated using standard competitive analysis. For each of these models, we give deterministic matching upper and lower bounds.
This work was partially supported by the German Research Foundation (DFG) within the Collaborative Research Centre “On-The-Fly Computing” (SFB 901).
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Feldkord, B., Markarian, C., Meyer Auf der Heide, F. (2017). Price Fluctuation in Online Leasing. In: Gao, X., Du, H., Han, M. (eds) Combinatorial Optimization and Applications. COCOA 2017. Lecture Notes in Computer Science(), vol 10628. Springer, Cham. https://doi.org/10.1007/978-3-319-71147-8_2
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DOI: https://doi.org/10.1007/978-3-319-71147-8_2
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