Distributed Resource Allocation Optimization in 5G Virtualized Networks

Halabian, Hassan

doi:10.1109/jsac.2019.2894305

Cited by 114 publications

(86 citation statements)

References 26 publications

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“…The resource allocation problem among competing slices in an heterogeneous cloud infrastructure is addressed in [27]. Slice resource demands are aggregated in a vector of VNF resource demands in the slice multiplied by a coefficient linked to the number of services to be processed per time unit.…”

Section: B Related Workmentioning

confidence: 99%

“…The first approach involves a centralized convex optimization problem, which objective is to maximize the total slice utility. Nevertheless, as pointed out in [27], such centralized lacks of scalability, is not robust to a failure of the central optimizer, and is prone to non-collaborative slice providers which may harm the system. For these reasons, a distributed method based on game theory is considered to improve robustness and scalability.…”

Section: B Related Workmentioning

confidence: 99%

“…Nevertheless, the placement of VNFs in data centers is predetermined by the MNO and again, wireless resources are not considered. A resource aggregation scheme similar to that in [27] has been introduced in our previous work [28], where infrastructure resources are provisioned to satisfy slice resource demand constraints. Radio resources are considered, but radio coverage constraints are still ignored.…”

Section: B Related Workmentioning

confidence: 99%

“…This work borrows the slice resource provisioning approach introduced in [28], and adapts it to the joint radio and network infrastructure resource provisioning. Constraints related to the infrastructure network considered in [17,18,27,28] are combined with coverage and radio resource constraints introduced in [29,30,31,32].…”

Section: Main Contributionsmentioning

confidence: 99%

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Aggregated Resource Provisioning for Network Slices

Luu

Kieffer

Mouradian

et al. 2018

2018 IEEE Global Communications Conference (GLOBECOM)

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Network slicing appears as a key enabler for the future 5G networks. Mobile Network Operators create various slices for Service Providers (SP) to accommodate customized services. As network slices are operated on a common network infrastructure owned by some Infrastructure Provider (InP), sharing the resources across a set of network slices is important for future deployment. Moreover, in many situations, slices have to be deployed over some geographical area: coverage as well as minimum per-user rate constraints have then to be taken into account.Usually, the various Service Function Chains (SFCs) belonging to a slice are deployed on a best-effort basis. Nothing ensures that the InP will be able to allocate enough resources to cope with the increasing demands of some SP. This paper takes the InP perspective and proposes a slice resource provisioning approach to cope with multiple slice demands in terms of computing, storage, coverage, and rate constraints.The resource requirements of the various Service Function Chains to be deployed within a slice are aggregated within a graph of Slice Resource Demands (SRD). Coverage and rate constraints are also taken into account in the SRD. Infrastructure nodes and links have then to be provisioned so as to satisfy all types of resource demands. This problem leads to a Mixed Integer Linear Programming formulation. A two-step deployment approach is considered, with several variants, depending on whether the constraints of each slide to be deployed are taken into account sequentially or jointly. Once provisioning has been performed, any slice deployment strategy may be considered on the reduced-size infrastructure graph representing the nodes and links on which resources have been provisioned.Simulation results demonstrate the effectiveness of the proposed approach compared to a more classical direct slice embedding approach.

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Section: B Related Workmentioning

confidence: 99%

Section: B Related Workmentioning

confidence: 99%

Section: B Related Workmentioning

confidence: 99%

Section: Main Contributionsmentioning

confidence: 99%

See 2 more Smart Citations

Aggregated Resource Provisioning for Network Slices

Luu

Kieffer

Mouradian

et al. 2018

2018 IEEE Global Communications Conference (GLOBECOM)

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“…Recently, distributed multi-agent resource allocation optimization has received much attention from various fields such as control and optimization (Xiao & Boyd 2006, Lakshmanan & De Farias 2008, Nedić, Olshevsky & Shi 2018, Yuan, Ho & Jiang 2018, Zhu, Ren, Yu & Wen 2019, Xu, Zhu, Soh & Xie 2019, identification (Guo, Mu, Wang, Yin & Xu 2017), communication (Halabian 2019), management (Bandi, Trichakis & Vayanos 2018), and power system (Yang, Lu, Wu, Wu, Shi, Meng & Johansson 2017). Many continuous-time algorithms have been developed to solve these problems.…”

Section: Introductionmentioning

confidence: 99%

Distributed sub-optimal resource allocation over weight-balanced graph via singular perturbation

Liang

Zeng

2018

Automatica

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Distributed optimization for resource allocation problems is investigated and a sub-optimal continuous-time algorithm is proposed. Our algorithm has lower order dynamics than others to reduce burdens of computation and communication, and is applicable to weight-balanced graphs. Moreover, it can deal with both local constraints and coupled inequality constraints, and remove the requirement of twice differentiability of the cost function in comparison with the existing sub-optimal algorithm. However, this algorithm is not easy to be analyzed since it involves singular perturbation type dynamics with projected non-differentiable righthand side. We overcome the encountered difficulties and obtain results including the existence of an equilibrium of the algorithm, the sub-optimality in the sense that the solution approaches to the optimal solution as an adjustable parameter tends to zero, and the convergence of the algorithm.

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