A Genetic Algorithm for VNF Provisioning in NFV-Enabled Cloud/MEC RAN Architectures

Ruiz, Lidia; Durán, Ramón J.; Miguel, Ignacio de; Khodashenas, Pouria Sayyad; Pedreño-Manresa, Jose-Juan; Merayo, Noemí; Aguado, Juan Carlos; Pavón‐Mariño, Pablo; Siddiqui, Shuaib; Mata, Javier; Fernández, Patricia; Lorenzo, Rubén M.; Abril, Evaristo J.

doi:10.3390/app8122614

Cited by 24 publications

(43 citation statements)

References 30 publications

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“…Users can request one of the services with a probability of 30%, 20% and 50%, respectively [9]. The corresponding SC and bandwidth requirements for each network service are listed in Table 2 [3], [9], [13], [17], [18], [34].…”

Section: Simulation Scenario and Resultsmentioning

confidence: 99%

Genetic Algorithm for Holistic VNF-Mapping and Virtual Topology Design

et al. 2020

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Next generation of Internet of Things (IoT) services imposes stringent requirements to the future networks that current ones cannot fulfill. 5G is a technology born to give response to those requirements. However, the deployment of 5G is also accompanied by profound architectural changes in the network, including the introduction of technologies like multi-access edge computing (MEC), software defined networking (SDN), and network function virtualization (NFV). In particular, NFV poses diverse challenges like virtual network function (VNF) placement and chaining, also called VNF-mapping. In this paper, we present an algorithm that solves VNF-placement and chaining in a metro WDM optical network equipped with MEC resources. Therefore, it solves the VNF-mapping in conjunction with the virtual topology design of the underlying optical backhaul network. Moreover, a version of the method providing protection against node failures is also presented. A simulation study is presented to show the importance of designing the three problems jointly, in contrast to other proposals of the literature that do not take the design of the underlying network into consideration when solving that problem. Furthermore, this paper also shows the advantages of using collaboration between MEC nodes to solve the VNF-mapping problem and the advantage of using shared protection schemes. The new algorithm outperforms other proposals in terms of both service blocking ratio, and number of active CPUs (thus reducing energy consumption). Finally, the impact of deploying different physical topologies for the optical backhaul network is also presented.

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Section: Simulation Scenario and Resultsmentioning

confidence: 99%

Genetic Algorithm for Holistic VNF-Mapping and Virtual Topology Design

et al. 2020

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“…Moreover, for a better flexibility, network services can be quickly scaled up or scaled down by provisioning the required resources to VNFs. Extensive studies on improving flexibility in NFV-enabled MEC are performed in [38]- [45]. For Ultra-Reliable Low-Latency Communications (uRLLC) services, authors in [38] propose a VNF placement scheme.…”

Section: Flexibility and Fast Deployment Of New Servicesmentioning

confidence: 99%

Multi-Access Edge Computing: A Survey

et al. 2020

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Multi-access Edge Computing (MEC) is a key solution that enables operators to open their networks to new services and IT ecosystems to leverage edge-cloud benefits in their networks and systems. Located in close proximity from the end users and connected devices, MEC provides extremely low latency and high bandwidth while always enabling applications to leverage cloud capabilities as necessary. In this paper, we illustrate the integration of MEC into a current mobile networks' architecture as well as the transition mechanisms to migrate into a standard 5G network architecture. We also discuss SDN, NFV, SFC and network slicing as MEC enablers. Then, we provide a state-of-the-art study on the different approaches that optimize the MEC resources and its QoS parameters. In this regard, we classify these approaches based on the optimized resources and QoS parameters (i.e., processing, storage, memory, bandwidth, energy and latency). Finally, we propose an architectural framework for a MEC-NFV environment based on the standard SDN architecture.

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“…There are also examples of the use of metaheuristics to find a solution to the RD problem in the context of NFV. In [19], the authors propose a genetic algorithm to solve the RD problem. The algorithm tries to minimize the service blocking rate and CPU usage in Cloud/Mobile Edge Computing (MEC) Radio Access Network (RAN)-based 5G architectures.…”

Section: Related Workmentioning

confidence: 99%

Closed-Form Expression for the Resources Dimensioning of Softwarized Network Services

Prados-Garzon

Taleb

Marai

et al. 2019

2019 IEEE Global Communications Conference (GLOBECOM)

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Network Function Virtualization (NFV) ecosystem enables the automation of deployment and scaling of softwarized network services (SNSs), thus reducing their operational expenditures. This enables operators to handle workload fluctuations, to keep the desired performance, with great agility and reduced costs. However, to realize the automation of such management practices, it is needed to determine the amount of required resources to allocate the SNS so that its performance requirements are met. This problem is commonly referred to as resources dimensioning problem. In this paper, we address the derivation of a closed-form expression for the optimal resources dimensioning of an SNS in terms of cost or energy efficiency. The performance requirement considered for the SNS is a limit on its mean response time. The performance model considered for the SNS is practical and accurate. The usefulness of the derived closedform expression is successfully validated by means of simulation. The scenario considered for the validation is a video optimization chain located at the SGi-LAN of a mobile network.

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A Genetic Algorithm for VNF Provisioning in NFV-Enabled Cloud/MEC RAN Architectures

Cited by 24 publications

References 30 publications

Genetic Algorithm for Holistic VNF-Mapping and Virtual Topology Design

Genetic Algorithm for Holistic VNF-Mapping and Virtual Topology Design

Multi-Access Edge Computing: A Survey

Closed-Form Expression for the Resources Dimensioning of Softwarized Network Services

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