On Optimal Orchestration of Virtualized Cellular Networks With Statistical Multiplexing

Chatterjee, Shubhajeet; Abdel-Rahman, Mohammad J.; MacKenzie, Allen B.

doi:10.1109/twc.2021.3095231

Cited by 5 publications

(2 citation statements)

References 31 publications

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“…An example is [19], where heuristic formulas are based on linear programming. Another alternative is statistic approaches like [8] where a dynamic statistic multiplexing governs the network orchestrator. Finally, we find network intelligence as the new trend [2], where an example from DAEMON partners is Nuberu [20] based on Bayesian algorithms.…”

Section: Qos In Software-defined Networkingmentioning

confidence: 99%

Quality-aware analysis and optimisation of virtual network functions

Munoz

Pinto

Fuentes

2022

Proceedings of the 26th ACM International Systems and Software Product Line Conference - Volume A

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The softwarisation and virtualisation of network functionality is the last milestone in the networking industry. Software-Defined Networks (SDN) and Network Function Virtualization (NFV) offer the possibility of using software to manage computer and mobile networks and build novel Virtual Network Functions (VNFs) deployed in heterogeneous devices. To reason about the variability of network functions and especially about the quality of a software product defined as a set of VNFs instantiated as part of a service (i.e., Service Function Chaining), a variability model along with a quality model is required.However, this domain imposes certain challenges to qualityaware reasoning of service function chains, such as numerical features or configuration-level Quality Attributes (QAs) (e.g., energy consumption). Incorporating numerical reasoning with quality data into SPL analyses is challenging and tool support is rare. In this work, we present 3 groups of operations: model report, aggregate functions to dynamically convert QAs at the feature-level into the configuration-level, and quality-aware optimisation. Our objective is to test the most complete reasoning tools to exploit the extended variability with quality attributes needed for VNFs. CCS CONCEPTS• Software and its engineering → Abstraction, modeling and modularity; Software product lines; Software performance; Requirements analysis; • Theory of computation → Automated reasoning; • Computing methodologies → Representation of mathematical objects.

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Section: Qos In Software-defined Networkingmentioning

confidence: 99%

Quality-aware analysis and optimisation of virtual network functions

Munoz

Pinto

Fuentes

2022

Proceedings of the 26th ACM International Systems and Software Product Line Conference - Volume A

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“…However, these endeavors did not incorporate considerations for the uncertainty in users' mobility patterns, which results in variations in the control plane workload. Stochastic programming methods have proven to be powerful mathematical tools for optimizing resource allocation in various types of wireless networks operating under uncertainties (examples include [18]- [21]). Therefore, this paper adopts chance-constrained and twostage stochastic programming [22] to address the CPP, specifically taking into account the uncertainty in users' mobility patterns leading to variations in control workload.…”

Section: Introductionmentioning

confidence: 99%

On Robust Optimal Joint Deployment and Assignment of RAN Intelligent Controllers in O-RANs

Abdel-Rahman,

Mazied,

Hassan

et al. 2024

IEEE Open J. Commun. Soc.

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The open radio access network (O-RAN) architecture is consolidating the concept of softwaredefined cellular networks beyond 5G networks, mainly through the introduction of the near-real-time radio access network (RAN) intelligent controller (Near-RT RIC) and the xApps. The deployment of the Near-RT RICs and the assignment of RAN nodes to the deployed RICs play a crucial role in optimizing the performance of O-RANs. In this paper, we develop a robust optimization framework for joint RIC deployment and assignment, considering the uncertainty in user locations. Specifically, our contributions are as follows. First, we develop C 3 P 2 , a robust static joint RIC placement and RAN node-RIC assignment scheme. The objective of C 3 P 2 is to minimize the number of RICs needed to control all RAN nodes while ensuring that the response time to each RAN node will not exceed δ milliseconds with a probability greater than β. Second, we develop CPPA, a robust joint RIC placement and adaptive RAN node-RIC assignment scheme. In contrast to C 3 P 2 , CPPA enjoys a recourse capability, where the RAN node-RIC assignment adapts to the variations in the user locations. We use chance-constrained stochastic optimization combined with several linearization techniques to develop a mixed-integer linear (MIL) formulation for C 3 P 2 . Two-stage stochastic optimization with recourse, combined with several linearization techniques, is used to develop an MIL formulation for CPPA. The optimal performance of C 3 P 2 and CPPA has been examined under various system parameter values. Furthermore, sample average approximation has been employed to design efficient approximate algorithms for solving C 3 P 2 and CPPA. Our results demonstrate the robustness of the proposed stochastic resource allocation schemes for O-RANs compared to existing deterministic allocation schemes. They also show the merits of adapting the allocation of resources to the network uncertainties compared to statically allocating them.INDEX TERMS Open radio access networks (O-RANs), RAN intelligent controller (RIC) placement, RIC assignment, chance-constrained stochastic optimization, two-stage stochastic optimization with recourse, sample average approximation.

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