2,219 research outputs found
A Novel Placement Algorithm for the Controllers Of the Virtual Networks (COVN) in SD-WAN with Multiple VNs
The escalation of communication demands and the emergence of new telecommunication concepts such as 5G cellular system and smart cities requires the consolidation of a flexible and manageable backbone network. These requirements motivated the researcher to come up with a new placement algorithm for the Controller of Virtual Network (COVN). This is because SDN and network virtualisation techniques (NFV and NV), are integrated to produce multiple virtual networks running on a single SD-WAN infrastructure, which serves the new backbone. One of the significant challenges of SD-WAN is determining the number and the locations of its controllers to optimise the network latency and reliability. This problem is fairly investigated and solved by several controller placement algorithms where the focus is only on physical controllers. The advent of the sliced SD-WAN produces a new challenge, which necessitates the SDWAN controllers (physical controller/hosted server) to run multiple instances of controllers (virtual controllers). Every virtual network is managed by its virtual controllers. This calls for an algorithm to determine the number and the positions of physical and virtual controllers of the multiple virtual SD-WANs. According to the literature review and to the best of the author knowledge, this problem is neither examined nor yet solved. To address this issue, the researcher designed a novel COVN placement algorithm to compute the controller placement of the physical controllers, then calculate the controller placement of every virtual SD-WAN independently, taking into consideration the controller placement of other virtual SD-WANs. COVN placement does not partition the SD-WAN when placing the physical controllers, unlike all previous placement algorithms. Instead, it identifies the nodes of the optimal reliability and latency to all switches of the network. Then, it partitions every VN separately to create its independent controller placement. COVN placement optimises the reliability and the latency according to the desired weights. It also maintains the load balancing and the optimal resources utilisation. Moreover, it supports the recovering of the controller failure. This novel algorithm is intensively evaluated using the produced COVN simulator and the developed Mininet emulator. The results indicate that COVN placement achieves the required optimisations mentioned above. Also, the implementations disclose that COVN placement can compute the controller placement for a large network ( 754 switches) in very small computation time (49.53 s). In addition, COVN placement is compared to POCO algorithm. The outcome reveals that COVN placement provides better reliability in about 30.76% and a bit higher latency in about 1.38%. Further, it surpasses POCO by constructing the balanced clusters according to the switch loads and offering the more efficient placement to recover controller-failure
Artificial Intelligence for Elastic Management and Orchestration of 5G Networks
The emergence of 5G enables a broad set of diversified and heterogeneous services with complex and potentially conflicting demands. For networks to be able to satisfy those needs, a flexible, adaptable, and programmable architecture based on network slicing is being proposed. A softwarization and cloudification of the communications networks is required, where network functions (NFs) are being transformed from programs running on dedicated hardware platforms to programs running over a shared pool of computational and communication resources. This architectural framework allows the introduction of resource elasticity as a key means to make an efficient use of the computational resources of 5G systems, but adds challenges related to resource sharing and efficiency. In this article, we propose Artificial Intelligence (AI) as a built-in architectural feature that allows the exploitation of the resource elasticity of a 5G network. Building on the work of the recently formed Experiential Network Intelligence (ENI) industry specification group of the European Telecommunications Standards Institute (ETSI) to embed an AI engine in the network, we describe a novel taxonomy for learning mechanisms that target exploiting the elasticity of the network as well as three different resource elastic use cases leveraging AI. This work describes the basis of a use case recently approved at ETSI ENI.Part of this work has been performed within the 5G-MoNArch project (Grant Agreement No. 761445), part of the Phase II of the 5th Generation Public Private Partnership (5G-PPP) program partially funded by the European Commission within the Horizon 2020 Framework Program. This work was also supported by the the 5G-Transformer project (Grant Agreement No. 761536)
An Overview on Application of Machine Learning Techniques in Optical Networks
Today's telecommunication networks have become sources of enormous amounts of
widely heterogeneous data. This information can be retrieved from network
traffic traces, network alarms, signal quality indicators, users' behavioral
data, etc. Advanced mathematical tools are required to extract meaningful
information from these data and take decisions pertaining to the proper
functioning of the networks from the network-generated data. Among these
mathematical tools, Machine Learning (ML) is regarded as one of the most
promising methodological approaches to perform network-data analysis and enable
automated network self-configuration and fault management. The adoption of ML
techniques in the field of optical communication networks is motivated by the
unprecedented growth of network complexity faced by optical networks in the
last few years. Such complexity increase is due to the introduction of a huge
number of adjustable and interdependent system parameters (e.g., routing
configurations, modulation format, symbol rate, coding schemes, etc.) that are
enabled by the usage of coherent transmission/reception technologies, advanced
digital signal processing and compensation of nonlinear effects in optical
fiber propagation. In this paper we provide an overview of the application of
ML to optical communications and networking. We classify and survey relevant
literature dealing with the topic, and we also provide an introductory tutorial
on ML for researchers and practitioners interested in this field. Although a
good number of research papers have recently appeared, the application of ML to
optical networks is still in its infancy: to stimulate further work in this
area, we conclude the paper proposing new possible research directions
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Interference Aware Cognitive Femtocell Networks
Femtocells Access Points (FAP) are low power, plug and play home base stations which are designed to extend the cellular radio range in indoor environments where macrocell coverage is generally poor. They offer significant increases in data rates over a short range, enabling high speed wireless and mobile broadband services, with the femtocell network overlaid onto the macrocell in a dual-tier arrangement. In contrast to conventional cellular systems which are well planned, FAP are arbitrarily installed by the end users and this can create harmful interference to both collocated femtocell and macrocell users. The interference becomes particularly serious in high FAP density scenarios and compromises the ensuing data rate. Consequently, effective management of both cross and co-tier interference is a major design challenge in dual-tier networks.
Since traditional radio resource management techniques and architectures for single-tier systems are either not applicable or operate inefficiently, innovative dual-tier approaches to intelligently manage interference are required. This thesis presents a number of original contributions to fulfill this objective including, a new hybrid cross-tier spectrum sharing model which builds upon an existing fractional frequency reuse technique to ensure minimal impact on the macro-tier resource allocation. A new flexible and adaptive virtual clustering framework is then formulated to alleviate co-tier interference in high FAP densities situations and finally, an intelligent coverage extension algorithm is developed to mitigate excessive femto-macrocell handovers, while upholding the required quality of service provision.
This thesis contends that to exploit the undoubted potential of dual-tier, macro-femtocell architectures an interference awareness solution is necessary. Rigorous evidence confirms that noteworthy performance improvements can be achieved in the quality of the received signal and throughput by applying cognitive methods to manage interference
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