An Efficient Model for Mobile Network Slice Embedding under Resource Uncertainty

2021 IEEE 22nd International Conference on High Performance Switching and Routing (HPSR)

2021

Network slicing has emerged as a pivotal concept in 5G systems, allowing mobile operators to build isolated logical networks (slices) on top of shared infrastructure networks. Within a network slice, several Service Function Chains are usually deployed on a best-effort premise. Nevertheless, this approach does not guarantee the availability of enough infrastructure resources to accommodate the uncertain and timevarying slice resource demands.This paper investigates two adaptive slice resource provisioning methods accounting for the evolution with time of the slice resource demands. A probabilistic guarantee of meeting the slice resource requirements can be obtained, while being robust against uncertainties. The myopic approach accounts for the past demands when provisioning the current demands, while the foresighted approach accounts for both past and future demands. These two methods lead to MILP problems. Their performance is compared with a quasi-static method, where provisioning is agnostic of the past and future demands.

Section: A Provisioning Constraintsmentioning

confidence: 99%

“…The topic of dynamic slice deployment has received significant attention, see, e.g., [6][7][8]. Taking the dynamic nature of slice requests as a starting point, these works aim to design adaptive resource allocation and reconfiguration algorithms for the deployment of network slices or Service Function Chains (SFCs).…”

Section: Introductionmentioning

confidence: 99%

Foresighted Resource Provisioning for Network Slicing

2021 IEEE 22nd International Conference on High Performance Switching and Routing (HPSR)

2021

“…As in [13], enough resources are dedicated to a service so as to satisfy the mean plus γ times the standard deviation of the demands. In [14], additionally, a similar approach is considered to account for the uncertainty in the available resources. A probability of feasibility, depending on γ, is then evaluated for the slice embedding to measure the risk of having a degraded service for some users.…”

Section: Introductionmentioning

confidence: 99%

“…A network slice embedding problem is considered in [14], where available resources and resource demands are assumed to be partly uncertain. They are described by normal distributions built upon the data history on mobile network resource availability as well as slice resource utilization.…”

Section: Introductionmentioning

confidence: 99%

“…The provisioning approach may be performed prior to the resource allocation at the time of deployment described, e.g., in [19,20], where virtual nodes and links are mapped on the infrastructure network. Moreover, instead of considering uncertainties in the available network resource, as in [14], here, we consider best-effort background services running in parallel with the network slices on the infrastructure network. The proposed scheme is able to maintain the impact of resource provisioning on those background services at a prescribed level.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Uncertainty-Aware Resource Provisioning for Network Slicing

IEEE Trans. Netw. Serv. Manage.

2021

Network slicing allows Mobile Network Operators to split the physical infrastructure into isolated virtual networks (slices), managed by Service Providers to accommodate customized services. The Service Function Chains (SFCs) belonging to a slice are usually deployed on a best-effort premise: nothing guarantees that network infrastructure resources will be sufficient to support a varying number of users, each with uncertain requirements.Taking the perspective of a network Infrastructure Provider (InP), this paper proposes a resource provisioning approach for slices, robust to a partly unknown number of users with random usage of the slice resources. The provisioning scheme aims to maximize the total earnings of the InP, while providing a probabilistic guarantee that the amount of provisioned network resources will meet the slice requirements. Moreover, the proposed provisioning approach is performed so as to limit its impact on low-priority background services, which may co-exist with slices in the infrastructure network.A Mixed Integer Linear Programming formulation of the slice resource provisioning problem is proposed. Optimal joint and suboptimal sequential solutions are proposed. These solutions are compared to a provisioning scheme that does not account for best-effort services sharing the common infrastructure network.

Admission Control and Resource Reservation for Prioritized Slice Requests With Guaranteed SLA Under Uncertainties

IEEE Trans. Netw. Serv. Manage.

2022

Network slicing has emerged as a key concept in 5G systems, allowing Mobile Network Operators (MNOs) to build isolated logical networks (slices) on top of shared infrastructure networks managed by Infrastructure Providers (InP). Network slicing requires the assignment of infrastructure network resources to virtual network components at slice activation time and the adjustment of resources for slices under operation. Performing these operations just-in-time, on a besteffort basis, comes with no guarantee on the availability of enough infrastructure resources to meet slice requirements.This paper proposes a prioritized admission control mechanism for concurrent slices based on an infrastructure resource reservation approach. The reservation accounts for the dynamic nature of slice requests while being robust to uncertainties in slice resource demands. Adopting the perspective of an InP, reservation schemes are proposed that maximize the number of slices for which infrastructure resources can be granted while minimizing the costs charged to the MNOs. This requires the solution of a max-min optimization problem with a nonlinear cost function and non-linear constraints induced by the robustness to uncertainties of demands and the limitation of the impact of reservation on background services. The cost and the constraints are linearized and several reduced-complexity strategies are proposed to solve the slice admission control and resource reservation problem. Simulations show that the proportion of admitted slices of different priority levels can be adjusted by a differentiated selection of the delay between the reception and the processing instants of a slice resource request.