A Novel Optimization Framework for C-RAN BBU Selection Based on Resiliency and Price

Lyazidi, Mohammed Yazid; Giupponi, Lorenza; Mangues‐Bafalluy, Josep; Aitsaadi, Nadjib; Langar, Rami

doi:10.1109/vtcfall.2017.8288046

Cited by 12 publications

(11 citation statements)

References 9 publications

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“…Reference [18] introduced an efficient and proactive restoration mechanism to ensure service resilience under the tremendous mobile traffic in carrier clouds. The authors in [19] used ILP and Branchand-Price algorithms to optimize virtualized BBU selection problems based on resiliency and price. Reference [20] proposed a survivable fronthaul scheme against single hotel failure.…”

Section: Related Workmentioning

confidence: 99%

Deep Reinforcement Learning-Based Policy for Baseband Function Placement and Routing of RAN in 5G and Beyond

Gao

Yan

Zhang

et al. 2022

J. Lightwave Technol.

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In this paper, we propose a deep reinforcement learning (DRL)-based algorithm to generate policies of Baseband Function (BBF) placement and routing. In order to explore the performance of the proposed algorithm in practical systems, the online scenario with the completely random requests is used in the simulation considering C-RAN and NG-RAN architectures. Besides, an Integer Linear Programming (ILP) model is formulated to generate the optimal solution as the benchmark. The simulation results show that DRL-based algorithm converges in a short time, and its performance closes to the optimal benchmark obtained by ILP in terms of latency and bandwidth for the online scenarios. In addition, the performance of the generated policies based on DRL is compared with a classic heuristic algorithm, i.e., first-fit algorithm. The performance of DRL-based algorithm is superior to the first-fit algorithm from above two perspectives. The fast convergence and the nearoptimal performance prove that the DRL-based algorithm is a promising approach for the BBF placement and routing of RAN in 5G and Beyond.

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Section: Related Workmentioning

confidence: 99%

Deep Reinforcement Learning-Based Policy for Baseband Function Placement and Routing of RAN in 5G and Beyond

Gao

Yan

Zhang

et al. 2022

J. Lightwave Technol.

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“…Equation (18) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 the maximum computational effort for that pool. Equations (19) and (20) ensure that the number of RRHs served by pool i equals the sum of RRHs assigned to that pool i. Equation (21) guarantees that capacity of virtual links routed over a certain physical link does not exceed its capacity.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Ref. [19] formulates an ILP problem named cost-resilience BBU selection, where a mobile network operator has to select BBU equipment from several cloud providers with different failure probability and cost. The objective function minimizes the BBU pool processing power and maximizes the resiliency.…”

Section: Introductionmentioning

confidence: 99%

Resilient BBU placement in 5G C-RAN over optical aggregation networks

2019

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The huge data demand envisioned for the 5G requires radically changes in the mobile network architecture and technology. Centralized Radio Access Network (C-RAN) is introduced as a novel mobile network architecture, designed to effectively support the challenging requirements of the future 5G mobile networks. In C-RAN, BaseBand Units (BBUs) are physically separated from their corresponding Radio Remote Heads (RRHs) and located in a central single physical location called BBU pool. The RRHs are connected to the BBU pool via the socalled fronthaul network. The "centralization" demonstrates remarkable benefits in terms of computational resources as well as power savings. Following this centralization, designing a survivable C-RAN becomes crucial as BBU pool and link failures might cause service outage for large number of users. In this paper, we propose three different approaches for the survivable BBU pool placement problem and traffic routing in C-RAN deployment over a 5G optical aggregation network. Namely we define the following protection scenarios: i) Dedicated Path protection, ii) Dedicated BBU protection and iii) Dedicated BBU and path protection. The three approaches are formalized as Integer Linear Programming (ILP) problems. The ILPs objectives are to minimize the number of BBU pools, the number of used wavelengths and the baseband processing computational resources, in terms of Giga operations Per Second (GOPS). We provide numerical results to compare the aforementioned protection strategies considering different network topologies. The results show the effect of the latency and the transport-network capacity on the BBU placement. We show the trade-off between the centralization degree and the tight latency requirements. Moreover, we discuss important insights about considering the different objective functions for each protection approach.

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“…More specifically, the work proposes an ILP formulation for the following three subproblems: dedicated path protection, dedicated BBU protection, and dedicated BBU and path protection. In [8], the authors derive an ILP formulation to select the best BBU in terms of failure probability and energy efficiency, while maximizing the amount of traffic processed by the C-RAN.…”

Section: Introductionmentioning

confidence: 99%

A Shared-Path Shared-Compute Planning Strategy for a Resilient Hybrid C-RAN

Lashgari

Wosinska

Monti

2019

2019 21st International Conference on Transparent Optical Networks (ICTON)

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One key challenge in 5G networks is to guarantee the survivability of services in the event of failures. This paper focuses on the hybrid cloud radio access network (H-CRAN) architecture. The proposed strategy guarantees survivability in the presence of failures affecting nodes/links in the midhaul segment (i.e., connecting the radio aggregation unit (RAU) nodes to their respective radio cloud center (RCC) nodes) as well as compute resources (i.e., servers) in the RCC nodes. In the envisioned strategy each RAU node is connected to a primary and a backup RCC node (i.e., with backup compute resources) via two node disjoint connectivity paths in the midhaul. The proposed strategy, called Shared-Path Shared-Compute Planning (SPSCP), lowers the overall design cost by trying to share as much as possible backup connectivity and compute resources among RAU nodes. This is made possible by introducing a shareability metric early into the RCC node selection process so that the chance of sharing backup resources is maximized. Simulation results show that the SPSCP strategy can lead to up to 28% cost savings when compared to conventional resilient design strategies.

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A Novel Optimization Framework for C-RAN BBU Selection Based on Resiliency and Price

Cited by 12 publications

References 9 publications

Deep Reinforcement Learning-Based Policy for Baseband Function Placement and Routing of RAN in 5G and Beyond

Deep Reinforcement Learning-Based Policy for Baseband Function Placement and Routing of RAN in 5G and Beyond

Resilient BBU placement in 5G C-RAN over optical aggregation networks

A Shared-Path Shared-Compute Planning Strategy for a Resilient Hybrid C-RAN

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