A Complete LTE Mathematical Framework for the Network Slice Planning of the EPC

Prados-Garzon, Jonathan; Laghrissi, Abdelquoddouss; Bagaa, Miloud; Taleb, Tarik; López-Soler, Juan M.

doi:10.1109/tmc.2018.2890235

Cited by 18 publications

(24 citation statements)

References 45 publications

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“…Leland et al studied characteristics of network traffic and found that it has self‐similarity 51 . Other scholars also confirmed this conclusion 52,53 . The Hurst exponent can measure the self‐similarity of the time series.…”

Section: Related Workmentioning

confidence: 90%

Network traffic prediction method based on wavelet transform and multiple models fusion

Tian

2020

Int J Communication

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Summary Accurate prediction of network traffic is an important premise in network management and congestion control. In order to improve the prediction accuracy of network traffic, a prediction method based on wavelet transform and multiple models fusion is presented. Mallat wavelet transform algorithm is used to decompose and reconstruct the network traffic time series. The approximate and detailed components of the original network traffic can be obtained. The characteristics of approximate components and detail components are analyzed by Hurst exponent. Then, according to the different characteristics of the components, autoregressive integrated moving average model (ARIMA) is chosen as the prediction model for the approximate component. Least squares support vector machine (LSSVM) is used to predict detail component. Meanwhile, an improved particle swarm optimization (PSO) algorithm is proposed to optimize the parameters of the LSSVM model. Gauss‐Markov estimation algorithm is adapted to fuse the predicted values of multiple prediction models. The variance of fusion prediction error is smaller than that of single prediction model, and the prediction accuracy is improved. Two actual datasets of network traffic are studied. Compared with other state‐of‐the‐art models, the case study results indicate that the proposed prediction method has a better prediction effect.

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Section: Related Workmentioning

confidence: 90%

Network traffic prediction method based on wavelet transform and multiple models fusion

Tian

2020

Int J Communication

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“…One of the most exciting aspects of the adoption of the NS concept is that it enables the automation of the management operations and orchestration of the future networks [2], thus reducing the Operating Expenditures (OPEXs) of the network. Such management operations include to automatically deploy (e.g., SNSs planning) [3] and scale on-demand (e.g., Dynamic Resource Provisioning (DRP)) [4]- [7] network services to cope with the workload fluctuations while guaranteeing the performance requirements. It involves increasing and reducing resources allocated to the services as needed.…”

Section: Single Virtualization Container Like a Virtual Machine (Vm)mentioning

confidence: 99%

“…An initial version of the performance model proposed in this paper was described in our previous work [14]. After, it has been applied to the planning and DRP for specific scenarios in [3] and [6], [7], respectively. All those previous works have reported satisfactory simulation results on the accuracy and usefulness of the model.…”

Section: Contributionsmentioning

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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“…M. Rahman and S. S. Heydari [65] model the number of messages generated at the MME to recover failed sessions in order to evaluate the performance of the self-healing schemes for the failed elements in the CN. J. Prados et al [67], model the control plane traffic of the CN as a G/G/m queue and then calculate the response time of the CN entities in order to resource dimensioning for providing network slice planning. In [68] the authors focus on the MME load and model it with a queuing network.…”

Section: Cn Load Modelingmentioning

confidence: 99%

A new scalable authentication and access control mechanism for 5G-based IoT

Behrad

Bertin

Tuffin

et al. 2020

Future Generation Computer Systems

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The fifth generation of mobile networks, 5G, is expected to support a set of many requirements and use cases such as handling connectivity for a massive number of IoT (Internet of Things) devices. Authenticating IoT devices and controlling their access to the network plays a vital role in the security of these devices and of the whole cellular system. In current cellular networks, as well as in 3GPP specifications release 16 on 5G, the AAC (Authentication and Access Control) of IoT devices is done in the same manner as the AAC of MBB (Mobile Broadband) UE (User Equipment). Considering the expected growth of IoT devices, this will likely induce a very high load on the connectivity provider's CN (Core Network) and cause network failures.To manage the AAC of this massive number of devices, we propose an SSAAC (Slice Specific Authentication and Access Control) mechanism that makes use of the flexibility provided by virtualization technologies. This mechanism allows the authentication and access control of IoT devices to be delegated to the 3rd parties providing these devices, thereby decreasing the load of the connectivity provider's CN, while increasing the flexibility and modularity of the whole 5G network. We evaluate the feasibility of our proposal with the OAI (Open Air Interface) open-source platform. Next, we provide a security analysis of the proposal and highlight the security requirements to use with this proposal. We also evaluate the impact of this delegation approach on the network load considering the anticipated number of AAC signaling messages compared to the existing AAC mechanisms in cellular networks. According to these evaluations, our approach is feasible and it would provide cellular networks the opportunity to overcome the security shortcomings in their AAC mechanisms. It also considerably reduces the AAC signaling load on the connectivity provider's CN. IntroductionAlong with mobility, security is one of the most important aspects of cellular systems. AAC (Authentication and access control) plays a vital role in ensuring the expected security level. In 3G and 4G, authentication and access control of subscribers are done through AKA (authentication and key agreement) protocols. These protocols (UMTS-AKA protocol in 3G and EPS-AKA in 4G) are based on the unique identities of subscribers and symmetric cryptographic algorithms [1,2] The system subscribers' identities and the secret keys (that are used in symmetric cryptographic algorithms) are provisioned in secured elements (e.g., SIM cards or embedded SIM) and stored in cellular system's database as well. Executing these AKA protocols to establish a secure connection with the cellular system is mandatory for each UE (composed of a mobile device and a secured element) to obtain its cellular connectivity [1,2]. However, these well-established principles may prevent cellular systems from supporting the connectivity of a massive number of devices [3], in particular when considering the context of the IoT-where a high growth rate of connected devices...

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A Complete LTE Mathematical Framework for the Network Slice Planning of the EPC

Cited by 18 publications

References 45 publications

Network traffic prediction method based on wavelet transform and multiple models fusion

Network traffic prediction method based on wavelet transform and multiple models fusion

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

A new scalable authentication and access control mechanism for 5G-based IoT

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