Challenges for Enhanced Network Self-Manageability in the Scope of Future Internet Development

The management and operation of a Self-Organizing Network (SON)-enabled mobile network still requires considerable human effort. On the one hand, SON Functions need to be configured through low-level parameters in order to control the optimization of the network. On the other hand, an operator wants to steer the system with solely technical objectives, and the underlying network should be adapted accordingly. This opens up a gap in network management that is currently closed manually. This paper presents an approach that overcomes the manual gap between technical objectives and SON Functions by choosing the best values for the SON Functions' configurations automatically. Main advantage of this approach is that it allows to manage a system at a high level of abstraction and, at the same time, reduces manual effort. The approach is explained by applying it in a case study in the field of mobile networks with four SON Functions, namely Mobility Load Balancing (MLB), Coverage and Capacity Optimization (CCO), Energy Savings Management (ESM) and Mobility Robustness Optimization (MRO).

Ultra-dense Heterogeneous networks (UDHN) are emerging as the inevitable approach to cope with the imminent cellular network capacity crunch. However, load imbalance and widely disproportionate SINR distribution between macro and small cells, remains the key hurdle in harnessing the full potential of UDHN. In this paper we address this problem by proposing and analysing a novel load-aware user association methodology that offers a mechanism to simultaneously optimize network capacity, load distribution and coverage. The solution concurrently leverages the three key optimization parameters for Coverage and Capacity Optimization (CCO) and Load Balancing (LB) SON functions i.e. antenna tilts, transmit powers and cell individual offsets (CIOs). The method incorporates exponential-weighting based prioritization of CCO and LB SON functions within the user association process. The results suggest that the proposed approach offers a distribution of load between macro and small cells that yields more gain in terms of both network capacity and user quality of service than conventional max signal strength or max SINR based association methods.

To accommodate big data, new technologies and features have started to be used in core network. Consequently, optical transport network has evolved towards Elastic Optical Network (EON). Despite the effort made towards efficient and reliable network management, interoperability problem in multi-vendor setting of the EON remains unsolved and it hinders the network management and monitoring. Hence, Network operators need to rely on the proprietary management solutions. This phenomenon poses complexity in network configuration, control, monitoring and management and also in turn results in higher Capital Expenditure (CAPEX) and Operational Expenditure (OPEX) for the service providers. In this paper, standard data definition model of network elements of EON is presented for configuration, management and monitoring purpose. Standard YANG data models for the network elements, namely, Reconfigurable Optical Add/drop Multiplexer (ROADM), Bandwidth Variable Transponder (BVT) and Optical amplifier (OA) are presented. The implementation scenario for the developed data definition models are demonstrated for managing state of the art EON. It is contended that the adoption of these standard YANG data models within the NETCONF protocol not only facilitates the network configuration and management, but also improves the multi-vendor interoperability in the control and management plane of the EON.

In this paper we introduce a mathematical model in the framework of long term evolution (LTE) to solve the problem of coverage and capacity optimization by employing multi-level random Taguchi's Method. The optimization process runs in a centralized manner with no human intervention required and interacts with environment situation automatically. Under the mathematical model which combines coverage and capacity optimization by a coefficient factor, conventional Taguchi's Method transcends traditional trial-and-error approach in convergence speed and simple implementation, to offer even more search capacity, Gaussian shrink coefficient and random optimization offset are applied to the level-to-tilts mapping function. Antenna tilt is an effective interference reduction technique, which has been adopted as the tuning parameter. The simulation results turn out to be better than traditional Taguchi's Method and trial-and-error approach. The system model presented and evaluated also gives an insight to the mathematical model especially the trade-off between coverage and capacity in homogeneous scenario.

The electric power transmission network management is changing from decentralized to province-centralized mode to adapt to the development trend of power grid. For the current hierarchical management situation of transmission system, the paper mainly studies the centralized mode. Considering the difficulty of transform, security, the demand of bandwidth and augmentability factors, the paper proposes a hierarchical province-centralized mode based on Resource pool and states its architecture, realization methods and deployment mode of Network Manager System in Cloud, realizing the unified management, configuration and data sharing of the whole province. Deploying the network management system in the Clout is taking full use of the resource sharing, efficient management and on-demand resource of the Cloud to realize the intensification, promoting the scheduling intensive management level of Information and transmission. Introduction For a long time, the layered, graded, zoned management mode of electric power communication networks makes the network management system built and maintained by the way of multi-level scheduling mechanism. In order to fulfill the development trend of power grid, power transmission network management should be developed from the provinces and cities separated pattern to the province-centralized mode to realize unified management, unified configuration and sharing of the communication network data within province and achieve the purpose of concentrate and highly active management, reasonable resource-configuration and optimizing the structure of the network resources. Deploying the communication network management centrally is an effective way to enhance the level of intensive management, which, whereas, will cause the load that systems deal with increase sharply. However, Cloud computing technology integrates resources into form Cloud in order to get higher utilization of computing and storage, having a strong computing power and storage capacity. With the development of Cloud construction, deploying communication network management in the pool can improve data reliability and security. This paper puts forward a hierarchical province-centralized mode based on Cloud by researching on concentrated-model of Transmission Network Management, and describes the model in detail. Discussed Problems Power system communication network is the important technology support and infrastructure platform of the grid. As an important guarantee of power grid security, stability and economic running, communication network management system is the important technology means of the communication network management and maintenance. International Power, Electronics and Materials Engineering Conference (IPEMEC 2015) © 2015. The authors Published by Atlantis Press 596 Communication network management system At present, the general transmission network management system is divided into Element Management System(EMS), Subnet Management System(SNMS) and Network Management (NMS) in terms of the management level. EMS is based on the device manager and realizes the management of safety, configuration, performance and alarm for specific equipment; SNMS can divide the network elements into various subnets, not only manage the elements equipments but also the topological relation between them. NMS is a higher level than the SNMS and manage multiple SNMS systems at the same time through network. It configures the router for each business level, and also shows the network elements’ circumstances of each time slot and the business relationships at all levels in details. The relationship of different level network management system is showed as the figure 1.

The 2nd IEEE Workshop on Autonomic Communications and Network Management (ACNM 2008, http://www.nw.neclab.eu/acnm08) was held in Salvador-Brazil, April 11, 2008 in conjunction with the IEEE/IFIP Network Operations & Management Symposium (NOMS 2008). It followed the success of the previous meeting, ACNM 2007 (http://www.nw.neclab.eu/acnm07), held in Munich together with the IFIP/IEEE International Symposium on Integrated Network Management (IM 2007). The purpose of ACNM 2008 was to present and to discuss new theories, mechanisms, and applications of Autonomic Communication—a new paradigm for forthcoming networks—in the fixed as well as the mobile worlds. The focus of ACNM is on intelligent communication, network control and management paradigms based on smart network and service elements that are capable of automatically configuring and organizing themselves. Autonomic networks will be able to sense their environment to perceive changes, to understand the meaning of these changes, and to react in an intelligent manner

This paper addresses the management of photonic networks. It presents the first achievements of the ACTS-MEPHISTO project. The project's general objective is to extend the principles of network management (TMN) to generic all-optical networks, in which wavelength division multiplexing (WDM) will be implemented far transmission and routing. This includes the development of information models for optical networks and network elements, of management applications and test platforms to demonstrate fault, configuration and performance management of optical networks, and the promotion of the developed models within ACTS and standardisation bodies. The functional architecture and early information models for the management of optical networks and network elements are described.

Future wireless networks (LTE and beyond) will experience a continuous growth regarding the number of network elements with increasingly complex interrelations between the configuration of multiple network elements (NEs). A related trend is the seamless integration of multiple radio technologies into a single heterogeneous wireless network. Both developments increase network management complexity and require new management concepts with a very high degree of automation such as Self-Organizing Network (SON) concepts, which are currently discussed in the network operator (NGMN), research, and standardization (3GPP) communities. SON functions have to be coordinated and supervised in an automated way in order to enable a stable system operation with tight control over the system behavior by the network operator together with a high degree of automation. Based on a detailed analysis of the requirements for the coordination, a policy-based approach to realize the coordination-related decision making based on the network configuration and SON function context is presented. Results for two use cases (fully automatic hardware to site mapping and coverage & capacity optimization) are presented to show the applicability of the developed approach to diverse SON use cases.

A key component of wireless sensor networks (WSN) is optimal coverage. For WSN to have network lifetime and optimal resource utilization, maximum coverage must be guaranteed. The unequal distribution of sensor nodes in densely populated regions contributes to the build-up of network coverage. This research suggests a revolutionary intelligent deep learning-based optimization approach for WSN coverage optimization to tackle this problem. Here, the optimal network coverage of WSN is performed by the novel enhanced deep Q-network (EDQN) algorithm, where the parameters of DQN are tuned by nature inspired optimization algorithm called hippopotamus optimization (HO) with the intention of attaining the fitness function. In order to maximize coverage, the node position is changed in the developed methodology to represent the spatial properties of the network. Along with lowering latency, the increased coverage also increases throughput and network longevity. The outcome of several network coverage optimization trials is computed, and the EDQN-HO’s impact on network coverage optimization is also determined by adjusting the parameters. The network coverage optimization studies’ simulated findings demonstrate that the suggested EDQN-HO may be effectively utilized in a variety of settings. The proposed EDQN-HO for the WSN coverage model returns superior outcomes with 18.31%, 78.95%, 10.65%, 87.5%, 83.33%, and 38.20% than the existing methods in terms of coverage rate, energy consumption, computing time, positioning error, network lifetime, and average moving distance respectively.

The rapid growth and complexity of integrated broadband networks has engendered a broad repertoire of sophisticated network management systems. Network management is the process of initializing, configuring, monitoring, and modifying the operations of complex service provider and enterprise networks to maximize their efficiency and productivity. Element management, a part of network management, focuses on proper operation of individual network elements or a collection of network elements. Due to the element management system's close association with individual network elements, its design, functionality, and architecture are heavily influenced by underlying networking technologies. Lucent Technologies' 7 R/E™ Trunk Access Gateway (TAG), previously known as the PacketStar™ Voice Gateway, integrates the functionality and features of traditional circuit switching with advanced packet switching techniques to provide end-to-end converged multimedia solutions. Accordingly, the TAG Element Management System (EMS) must satisfy the operational requirements of both voice and data service providers. This paper discusses the various functional requirements and standardized technologies and the implementation architecture of element management, focusing on the 7 R/E TAG EMS. It describes the challenges of managing an integrated gateway element and explains how the flexibility and efficiency of the TAG EMS help to support service provider operations.

We have previously proposed and implemented a small form factor and low cost 100G optical transmission system using inverse-multiplexing technology and MLD (Multiplex-Lane Distribution) mechanism [1]. Based on this 100G system, we will further implement a transmission and network management system for multiple 100G application via stacking technology. This technology can incrementally realize the standard 100G OTN or 100G Ethernet capacity expansion with a centralized network element management at one node. The stacking system contains both network management port stacking and optical wavelength stacking. Our test results show that we can stack at least four devices with stable performance, which means a maximum transmission capacity of 400G, but with one IP address or one NE (network element) network management. This technology has a significant advantage comparing with a large rack equipment in term of device cost and flexible 100G bandwidth addition, as well as simple management function.

The available 5G network is going to have various industrial applications. Mobile communication operators will be able more effectively to utilize their spectral assets, provide higher-speed data communication services, and increase network capacity. Traditional network parameters are manually set or adjusted by the network management system, so substantial room exists for improvement in operational speed. Self-organizing networks (SON) provide an automated means of simplifying operational tasks and improving operational efficiency. In future 5G networks, especially, in congested urban areas, difficulties of network management and load balancing will arise. The proposed novel SON management mechanism influences the overall network behavior by arranging the SON function parameters and adjusting the configuration of devices, user-equipment, and small cells. Simulation results indicate that implementing the proposed mechanism increases the throughput by an average of 4% and a maximum of 4.5%.

The motivation behind exploring anomaly detection in self-organizing networks lies in the evolving landscape of telecommunications and network management. Conventional methods for identifying network anomalies often struggle to adapt to the dynamic and complex nature of modern self-organizing networks. The problem addressed in this research is the efficacy of anomaly detection methods in self-organizing networks (SONs) within the context of telecommunications and network management. As SONs become increasingly prevalent to meet the demands of modern, highly dynamic wireless communication systems, the need for robust anomaly detection mechanisms is paramount. Conventional anomaly detection approaches in SONs are often based on predefined rules and thresholds, which may struggle to adapt to the intricate and rapidly evolving network behaviors. These methods can result in false alarms, missed anomalies, and inefficient resource allocation. Furthermore, emerging SONs incorporate a multitude of diverse technologies, including 5G, IoT, and edge computing, compounding the complexity of anomaly detection. Contemporary machine learning techniques hold promise in addressing these challenges by enabling the automatic and adaptive detection of anomalies, leveraging the abundance of data generated in SONs. However, the suitability, performance, and scalability of these methods in dynamic and large-scale SON environments remain critical concerns. This research aims to compare and evaluate conventional anomaly detection methods against contemporary machine learning approaches in SONs to assess their accuracy, efficiency, and adaptability. The goal is to provide insights into the most effective anomaly detection strategies, ultimately enhancing network stability, minimizing downtime, and ensuring the secure and efficient operation of modern telecommunications systems.

Today’s competitive market place has forced the telecommunications industry to improve their service and reliability. One step that telecommunications companies have taken to reduce network failures is the installation of operations centers to collect data from network elements. These centers are staffed by network managers who monitor network activity by correlating alarms across various operational disciplines (switch, facility, traffic) and relating them to a common cause. Accurate analysis is often difficult due to the volume of data and complexity of problems.ECXpert is a product developed recently at AT&T to help network managers monitor and analyze alarms, take corrective actions, and minimize disruptions to the network. Successful implementation of event correlation has increased customer revenue since trouble isolation can be done faster, resulting in quicker restoration of service.The essence of ECXpert is a high level language with which users can specify network events and their correlation with alarms. The system is written in Prolog and C++, a powerful combination which facilitated development to occur on time and in budget. It has been deployed in network management centers throughout the U.S. and is currently being marketed overseas. ECXpert is a success story for Prolog within AT&T.KeywordsNetwork ManagementTNMEvent CorrelationC++PrologMeta-Languages

The evolution of communication networks has witnessed a remarkable transformation with the advent of 5G technology. As the latest generation of cellular networks, 5G promises to revolutionize the way we connect, communicate, and interact with the digital world. This paper explores the architecture, management, and implications of 5G communication networks, shedding light on the key features, challenges, and opportunities presented by this transformative technology. At the core of 5G networks lies a sophisticated architecture designed to deliver unprecedented levels of speed, reliability, and connectivity. The 5G New Radio (NR) architecture encompasses three primary components: the User Equipment (UE), the Radio Access Network (RAN), and the Core Network (CN). These components work in tandem to facilitate seamless communication between user devices and the broader network infrastructure. One of the defining features of 5G networks is their ability to support significantly higher data speeds and lower latency compared to previous generations. With the potential to deliver data rates of several gigabits per second and latency as low as a few milliseconds, 5G opens the door to a plethora of innovative applications and services, ranging from immersive virtual reality experiences to real-time remote surgeries. Effective management of 5G networks is paramount to ensuring optimal performance, reliability, and security. Network management systems employ advanced algorithms for dynamic resource allocation, quality of service (QoS) optimization, and network slicing. Network slicing, in particular, enables operators to create virtual networks tailored to specific use cases, such as enhanced mobile broadband, massive IoT deployments, and mission-critical communications. However, the deployment and management of 5G networks pose several challenges and considerations. Infrastructure deployment requires substantial investment and coordination, while ensuring interoperability between different vendors' equipment and standards remains a pressing issue. Spectrum availability and allocation also play a critical role in determining the performance and capacity of 5G networks, necessitating careful regulatory oversight and spectrum management strategies. Despite these challenges, the implications of 5G technology are far-reaching and profound. From empowering smart cities and autonomous vehicles to enabling the widespread adoption of IoT devices and applications, 5G networks have the potential to reshape industries, drive innovation, and enhance the quality of life for billions of people around the world. 5G communication networks represent a paradigm shift in the way we connect and communicate. With their advanced architecture, dynamic management capabilities, and transformative potential, 5G networks are poised to unlock new opportunities, accelerate digital transformation, and usher in a new era of connectivity and innovation.