Abstract
Emerging network slicing markets promise to boost the utilization of expensive network resources and to unleash the potential of over-the-top services. Their success, however, is conditioned on the service providers (SPs) being able to bid effectively for the virtualized resources. In this paper we consider a hybrid advance-reservation and spot slice market and study how the SPs should reserve slices in order to maximize their performance while not exceeding their budget. We consider this problem in its general form, where the SP demand and slice prices are time-varying and revealed only after the reservations are decided. We develop a learning-based framework, using the theory of online convex optimization, that allows the SP to employ a no-regret reservation policy, i.e., achieve the same performance with a hypothetical policy that has knowledge of future demand and prices. We extend our framework for the scenario the SP decides dynamically its slice orchestration, where it additionally needs to learn which resource composition is performance - maximizing; and we propose a mixed-time scale scheme that allows the SP to leverage any spot-market information revealed between its reservations. We evaluate our learning framework and its extensions using a variety of simulation scenarios and following a detailed parameter sensitivity analysis.
Original language | English |
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Title of host publication | ICC 2021 - IEEE International Conference on Communications |
Subtitle of host publication | Proceedings |
Place of Publication | Piscataway |
Publisher | IEEE |
Pages | 1-7 |
Number of pages | 7 |
ISBN (Electronic) | 978-1-7281-7122-7 |
ISBN (Print) | 978-1-7281-7123-4 |
DOIs | |
Publication status | Published - 2021 |
Event | ICC 2021 - IEEE International Conference on Communications - Virtual at Montreal, Canada Duration: 14 Jun 2021 → 23 Jun 2021 |
Conference
Conference | ICC 2021 - IEEE International Conference on Communications |
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Country/Territory | Canada |
City | Virtual at Montreal |
Period | 14/06/21 → 23/06/21 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Keywords
- Online convex optimization
- network slicing markets
- virtualization
- resource reservation
- SP utility maximization
- constrained optimization
- duality