16 research outputs found
Modeling and Dimensioning of a Virtualized MME for 5G Mobile Networks
Network function virtualization is considered one of
the key technologies for developing future mobile networks. In this
paper, we propose a theoretical framework to evaluate the performance of a Long-Term Evolution (LTE) virtualized mobility management entity (vMME) hosted in a data center. This theoretical
framework consists of 1) a queuing network to model the vMME
in a data center and 2) analytic expressions to estimate the overall
mean system delay and the signaling workload to be processed by
the vMME. We validate our mathematical model by simulation.
One direct use of the proposed model is vMME dimensioning, i.e.,
to compute the number of vMME processing instances to provide
a target system delay given the number of users in the system.
Additionally, the paper includes a scalability analysis of the system. In our study, we consider the billing model and a data center
setup of Amazon Elastic Compute Cloud service and estimate the
processing time of MME processing instances for different LTE
control procedures experimentally. For the considered setup, our
results show that the vMME is scalable for signaling workloads
up to 37 000 LTE control procedures per second for a target mean
system delay of 1 ms. The system design and database performance
assumed imposes this limit in the system scalability.This work was supported in part by the Spanish Ministry of Economy
and Competitiveness and the European Regional Development Fund (project
TIN2013-46223-P) and in part by the Spanish Ministry of Education, Culture,
and Sport under FPU Grant 13/04833
Vehicular Data Cloud Services
The advance cloud computing has provided an opportunity to resolve the challenges which effects by increasing transportation issues. Two methods of cloud services are available these are parking and mining. Mobile cloud computing has improved the storage capacity, stand by time of mobile terminals by migrating data processing to the remote cloud. The introduction of smart phones, cloud computing the automotive system is shifting toward the internet of vehicles
SDN - Architectural Enabler for Reliable Communication over Millimeter-Wave 5G Networks
Millimeter-wave (mmWave) frequency bands offer a new frontier for
next-generation wireless networks, popularly known as 5G, to enable
multi-gigabit communication; however, the availability and reliability of
mmWave signals are significantly limited due to its unfavorable propagation
characteristics. Thus, mmWave networks rely on directional narrow-beam
transmissions to overcome severe path-loss. To mitigate the impact of
transmission-reception directionality and provide uninterrupted network
services, ensuring the availability of mmWave transmission links is important.
In this paper, we proposed a new flexible network architecture to provide
efficient resource coordination among serving basestations during user
mobility. The key idea of this holistic architecture is to exploit the
software-defined networking (SDN) technology with mmWave communication to
provide a flexible and resilient network architecture. Besides, this paper
presents an efficient and seamless uncoordinated network operation to support
reliable communication in highly-dynamic environments characterized by high
density and mobility of wireless devices. To warrant high-reliability and guard
against the potential radio link failure, we introduce a new transmission
framework to ensure that there is at least one basestation is connected to the
UE at all times. We validate the proposed transmission scheme through
simulations.Comment: This article has been accepted for publication at the IEEE GLOBECOM
2018 Workshops, Abu Dhabi, UAE, 9-13 December 201
An adaptive scaling mechanism for managing performance variations in network functions virtualization: A case study in an NFV-based EPC
The scaling is a fundamental task that allows addressing performance variations in Network Functions Virtualization (NFV). In the literature, several approaches propose scaling mechanisms that differ in the utilized technique (e.g., reactive, predictive and machine learning-based). The scaling in NFV must be accurate both at the time and the number of instances to be scaled, aiming at avoiding unnecessary procedures of provisioning and releasing of resources; however, achieving a high accuracy is a non-trivial task. In this paper, we propose for NFV an adaptive scaling mechanism based on Q-Learning and Gaussian Processes that are utilized by an agent to carry out an improvement strategy of a scaling policy, and therefore, to make better decisions for managing performance variations. We evaluate our mechanism by simulations, in a case study in a virtualized Evolved Packet Core, corroborating that it is more accurate than approaches based on static threshold rules and Q-Learning without a policy improvement strategy
An Optimization-enhanced MANO for Energy-efficient 5G Networks
5G network nodes, fronthaul and backhaul alike, will have both forwarding and computational capabilities. This makes energy-efficient network management more challenging, as decisions such as activating or deactivating a node impact on both the ability of the network to route traffic and the amount of processing it can perform. To this end, we formulate an optimization problem accounting for the main features of 5G nodes and the traffic they serve, allowing joint decisions about (i) the nodes to activate, (ii) the network functions they run, and (iii) the traffic routing. Our optimization module is integrated within the management and orchestration framework of 5G, thus enabling swift and high-quality decisions. We test our scheme with both a real-world testbed based on OpenStack and OpenDaylight, and a large-scale emulated network whose topology and traffic come from a real-world mobile operator, finding it to consistently outperform state-of-the art alternatives and closely match the optimum
Characterizing Delay and Control Traffic of the Cellular MME with IoT Support
One of the main use cases for advanced cellular networks is represented by massive Internet-of-things (MIoT), i.e., an enormous number of IoT devices that transmit data toward the cellular network infrastructure. To make cellular MIoT a reality, data transfer and control procedures specifically designed for the support of IoT are needed. For this reason, 3GPP has introduced the Control Plane Cellular IoT optimization, which foresees a simplified bearer instantiation, with the Mobility Management Entity (MME) handling both control and data traffic. The performance of the MME has therefore become critical, and properly scaling its computational capability can determine the ability of the whole network to tackle MIoT effectively. In particular, considering virtualized networks and the need for an efficient allocation of computing resources, it is paramount to characterize the MME performance as the MIoT traffic load changes. We address this need by presenting compact, closed-form expressions linking the number of IoT sources with the rate at which bearers are requested, and such a rate with the delay incurred by the IoT data. We show that our analysis, supported by testbed experiments and verified through large-scale simulations, represents a valuable tool to make effective scaling decisions in virtualized cellular core networks