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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 performancemaximizing; 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.

Network slicing markets have the potential to increase significantly the utilization of virtualized network resources and facilitate the low-cost deployment of over-the-top services. However, their success is conditioned on the service providers (SPs) being able to bid effectively for the virtualized resources. In this paper, we consider a hybrid advancereservation and spot slice market and study how the SPs should reserve resources to maximize their services' performance while not violating a time-average budget threshold. 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 an oracle that has full access to all future demand and prices. We extend the framework to the scenario where the SP decides dynamically its slice orchestration and hence needs to learn the performance-maximizing resource composition; and we further develop a mixed-time scale scheme that allows the SP to leverage spot-market information that is revealed between successive reservations. The proposed learning framework is evaluated using representative simulation scenarios that highlight its efficacy as well as the impact of key system and algorithm parameters.

In this paper, we propose a solution that allows the SP to decide its online reservation policy, which aims to maximize its service utility and minimize its cost of reservation simultaneously. We design the Optimistic Online Learning for Reservation (OOLR) solution, a decision algorithm built upon the Follow-the-Regularized Leader (FTRL), that incorporates key predictions to assist the decision-making process. Our solution achieves a O( √ T ) regret bound where T represents the horizon. We integrate a prediction model into the OOLR solution and we demonstrate through numerical results the efficacy of the combined models' solution against the FTRL baseline.

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