Design and Implementation of a Distributed Mobility Management Entity on OpenStack

Premsankar, Gopika; Ahokas, Kimmo; Luukkainen, Sakari

doi:10.1109/cloudcom.2015.54

Cited by 18 publications

(16 citation statements)

References 6 publications

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“…That is the main reason why this kind of VNF decomposition has been considered mainly to virtualize control plane network entities, where the delay constraints are less stringent than in the data plane. Several works consider the 1:N mapping architecture to virtualize an LTE MME [12], [38], [13]. It is also applied to virtualize the IP Multimedia Subsystem (IMS) entities [39].…”

Section: A Vmme Decompositionmentioning

confidence: 99%

Performance Modeling of Softwarized Network Services Based on Queuing Theory With Experimental Validation

Prados-Garzon

Ameigeiras

Ramos-Muñoz

et al. 2021

IEEE Trans. on Mobile Comput.

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Network Functions Virtualization facilitates the automation of the scaling of softwarized network services (SNSs). However, the realization of such a scenario requires a way to determine the needed amount of resources so that the SNSs performance requisites are met for a given workload. This problem is known as resource dimensioning, and it can be efficiently tackled by performance modeling. In this vein, this paper describes an analytical model based on an open queuing network of G/G/m queues to evaluate the response time of SNSs. We validate our model experimentally for a virtualized Mobility Management Entity (vMME) with a three-tiered architecture running on a testbed that resembles a typical data center virtualization environment. We detail the description of our experimental setup and procedures. We solve our resulting queueing network by using the Queueing Networks Analyzer (QNA), Jackson's networks, and Mean Value Analysis methodologies, and compare them in terms of estimation error. Results show that, for medium and high workloads, the QNA method achieves less than half of error compared to the standard techniques. For low workloads, the three methods produce an error lower than 10%. Finally, we show the usefulness of the model for performing the dynamic provisioning of the vMME experimentally.Network Softwarization, NFV, performance modeling, queuing theory, queuing model, softwairzed network services, resource dimensioning, dynamic resource provisioning.

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Section: A Vmme Decompositionmentioning

confidence: 99%

Performance Modeling of Softwarized Network Services Based on Queuing Theory With Experimental Validation

Prados-Garzon

Ameigeiras

Ramos-Muñoz

et al. 2021

IEEE Trans. on Mobile Comput.

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“…Since the main concern for this article is the ability of individual vEPC/vIMS entities that are installed in separate VMs to handle operations with 4 very limited resources within a geo-datacenter, the following section explains a new service chaining model that leans more towards interconnected distributed clouds. It is also necessary to highlights that scaling resources using elasticity is only successful for clouds preserving a significant amount of resources to process arrival traffic and there still a need to incorporate clouds as one resources rather than segregated model [14] CHAINING SERVICES BETWEEN GEO-DATACENTERS In a cloud geo-datacenters model, calls between local UEs within the same cluster are handled by local geo-datacenter VNFs. Theoretically, each cluster/geo-datacenter combination is considered as a logic network since it consists of RAN and cloud backbone.…”

Section: Network Provisioning Using Software-defined Datacentermentioning

confidence: 99%

A Framework of Network Connectivity Management in Multi-Clouds Infrastructure

Al‐Dulaimi

Mumtaz

Al‐Rubaye

et al. 2019

IEEE Wireless Commun.

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The network function virtualization (NFV) transformation is gaining an incredible momentum from mobile operators as one of the significant solutions to improve the resource allocation and system scalability in fifth-generation (5G) networks. However, the ultra-dense deployments in 5G create high volumes of traffic that pushes the physical and virtual resources of cloud-based networks to edge limits. Consider a distributed cloud, replacing the core network with virtual entities in the form of virtual network functions (VNFs) still requires efficient integration with various underlying hardware technologies. Therefore, orchestrating the distribution of load between cloud geo-datacenters starts by instantiating a virtual and physical network typologies that connect involved fronthaul with relevant VNFs. In this article, we provide a framework to manage calls within 5G network clusters for efficient utilization of computational resources and also to prevent unnecessary signaling. We also propose a new scheme to instantiate virtual tunnels for call forwarding between network clusters leading to new logic networks that combine geo-datacenters and fronthaul. To facilitate service chaining in cloud, we propose a new enhanced management and orchestration (E-MANO) architecture that brings network traffic policies from the application layer to the fronthaul for instant monitoring of available resources. We provide analysis and testbed results in support of our proposals.

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“…Authors in [12] and [13] propose a stateless vMME that is split into three logical components: 1) front-end (FE), 2) MME service logic/Worker (SL), and 3) state database (SDB). The authors assume that MME SL can handle any request for any UE as they are stateless.…”

Section: Related Workmentioning

confidence: 99%

On the scalability of 5G core network: The AMF case

Alawe

Hadjadj‐Aoul

Ksentini

et al. 2018

2018 15th IEEE Annual Consumer Communications &Amp; Networking Conference (CCNC)

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Abstract-One of the requirement of 5G is to support massive number of connected devices, considering many use-cases such as IoT and massive Machine Type Communication (MTC). While this represents an interesting opportunity for operators to grow their business, it will need new mechanisms to scale and manage the envisioned high number of devices and their generated traffic. Particularity, the signaling traffic, which will overload the 5G core Network Function (NF) in charge of authentication and mobility, namely Access and Mobility Management Function (AMF). The objective of this paper is to provide an algorithm based on Control Theory allowing: (i) to equilibrate the load on the AMF instances in order to maintain an optimal response time with limited computing latency; (ii) to scale out or in the AMF instance (using NFV techniques) depending on the network load to save energy and avoid wasting resources. Obtained results via computer system indicate the superiority of our algorithm in ensuring fair load balancing while scaling dynamically with the traffic load.

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Design and Implementation of a Distributed Mobility Management Entity on OpenStack

Cited by 18 publications

References 6 publications

Performance Modeling of Softwarized Network Services Based on Queuing Theory With Experimental Validation

Performance Modeling of Softwarized Network Services Based on Queuing Theory With Experimental Validation

A Framework of Network Connectivity Management in Multi-Clouds Infrastructure

On the scalability of 5G core network: The AMF case

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