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No-Regret Slice Reservation Algorithms

Authors: Monteil, Jean-Baptiste; Iosifidis, G.; DaSilva, Luiz;

No-Regret Slice Reservation Algorithms

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. 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-care Otherwise 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. Embedded Systems

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Ireland, Netherlands
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Subjects by Vocabulary

Microsoft Academic Graph classification: Operations research Computer science computer.software_genre Order (exchange) Leverage (statistics) Orchestration (computing) Reservation Regret Service provider Virtualization Convex optimization computer

Keywords

network slicing market, Virtualization, Resource reservation, SP utility maximization, Network slicing markets, virtualization, constrained optimization, resource reservation, duality, Online convex optimization, SP utility maxi- mization, network slicing markets

24 references, page 1 of 3

[1] X. Foukas, G. Patounas, A. Elmokashfi, and M. K. Marina, “Network Slicing in 5G: Survey and Challenges,” IEEE Communications Magazine, vol. 55, no. 5, pp. 94-100, 2017. [OpenAIRE]

[2] K. Samdanis, X. Costa-Perez, and V. Sciancalepore, “From Network Sharing to Multi-tenancy: The 5G Network Slice Broker,” IEEE Communications Magazine, vol. 54, pp. 32-39, 2016.

[3] S. Vassilaras, L. Gkatzikis, N. Liakopoulos, I. N. Stiakogiannakis, M. Qi, L. Shi, L. Liu, M. Debbah, and G. S. Paschos, “The Algorithmic Aspects of Network Slicing,” IEEE Communications Magazine, vol. 55, no. 8, pp. 112-119, 2017. [OpenAIRE]

[4] U. Habiba, and E. Hossain, “Auction Mechanisms for Virtualization in 5G Cellular Networks: Basics, Trends, and Open Challenges,” IEEE Communication Surveys and Tutorials, vol. 20, 2018.

[5] “Compute Engine Pricing,” 2021, Google Cloud Platform. [Online]. Available: https://cloud.google.com/compute/

[6] “Google Cloud Platform,” 2021, Preemptible Virtual Machines. [Online]. Available: https://cloud.google.com/preemptible-vms/

[7] “Amazon EC2,” 2021, Reserved Instances. [Online]. Available: https://aws.amazon.com/ec2/purchasing-options/reserved-instances/

[8] “Amazon EC2,” 2021, Spot Instances. [Online]. Available: https://aws.amazon.com/ec2/spot/

[9] M. Jiang, M. Condoluci, and T. Mahmoodi, “Network slicing in 5G: an Auction-Based Model,” in Proc. of IEEE ICC, 2017.

[10] M. Leconte, G. S. Paschos, P. Mertikopoulos, and U. C. Kozat, “A Resource Allocation Framework for Network Slicing,” in Proc. of IEEE INFOCOM, 2018, pp. 2177-2185.

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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