A configuration management assessment method for SON verification

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).

<p>WiFi networks based on the IEEE 802.11 standard are widely used indoors or outdoors as simple and cost-effective wireless technology. However, the data connection is significantly disrupted when mobile stations (STAs) switch between access points (APs). Furthermore, high packet loss occurs during the switching period. Therefore, mobility is a critical issue that needs to be solved in WiFi networks. In cellular networks, handover is used to keep ongoing data transfer when network clients switch between base stations. However, the handover algorithm is not supported in the 802.11 standard for WiFi networks. Self-Organizing Network (SON) functionality enables seamless handover in cellular networks, improving network performance. However, the SON functionality has not been fully researched in WiFi networks, especially for mobility management. Motivated by the SON functionalities, a SON approach is proposed to automatically optimize the handover algorithms for WiFi networks. This approach focuses on the SON functionalities including self-configuration, self-optimization and self-healing using machine learning techniques to develop new algorithms for WiFi mobility management. The overall goal of this thesis is to optimize handover performance as well as enhance the network’s capabilities.</p>

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 Self-Organizing Network (SON) performs automated network management through the coordinated execution of autonomous functions, each aiming to achieve a specific objective like the optimization of a network Key Performance Indicator (KPI) value. However, there are situations in which a SON function cannot achieve its objective which can lead to disturbed SON operation and inferior performance. We present a SON Operational Troubleshooting (SONOT) SON function that is able to detect such problematic situations and trigger respective countermeasures. Thereby, it can exploit regular SON functions as probes in order to improve problem detection. Simulations show that the tight integration of the SONOT function with SON coordination provides means to automatically overcome the problems and improve overall network performance.KeywordsSelf-Organizing NetworkSON operationSON coordinationTroubleshooting

The Self-Organizing Network (SON) paradigm enables automated management of next generation mobile communication networks by introducing a set of autonomous functions that jointly achieve self-configuration, self-optimization, and self-healing. Each SON function is driven by a specific objective to optimize one or more Key Performance Indicators (KPIs). If a function encounters situations in which the given targets cannot be achieved, it can produce undesired behavior, resulting in an impaired SON. This paper presents the idea of SON Operational Troubleshooting function and outlines a concept for the automatic detection, analysis, and mitigation of operational problems.

The deployment, operation and maintenance of complex cellular networks are managed autonomously by multiple concurrently executing Self-Organizing Network (SON) functions with dedicated objectives, that can often negatively impact the functioning of each other. It is essential to avoid the blinkered view to their individual targets and consider a holistic approach towards identifying the best possible coordination between them, in order to achieve desired overall network gains while ensuring stable and robust network operation. The designing of appropriate SON-coordination mechanisms is quite challenging as it requires comprehensive modelling of all the complementing and conflicting interactions among them. This article discusses the application of Machine Learning based online Recommender Systems to model the dynamics between SON functions. To evaluate its applicability, in this work, the focus is to jointly implement two intertwined SON functions - Inter Cell Interference Coordination (ICIC) and Coverage and Capacity Optimization (CCO), to implicitly handle their conflicts and achieve the desired trade-off between coverage and capacity by optimizing a joint objective. The proposed cooperative learning and distributed configuration enforcement based ICIC-CCO coordinated SON solution has been evaluated on a system-level LTE network simulator with varied traffic distributions. It has been observed that the outage situations in the network are significantly reduced while still achieving high Signal-to-Interference Ratios (SIRs), even with reduced transmit power settings on several occasions.

Current networks require ad-hoc operating procedures by expert administrators to handle changes. These configuration management operations are costly and error prone. Active networks involve particularly fast dynamics of change that cannot depend on operators and must be automated. This paper describes an architecture called NESTOR that seeks to replace labor-intensive configuration management with one that is automated and software-intensive. Network element configuration state is represented in a unified object-relationship model. Management is automated via policy rules that control change propagation across model objects. Configuration constraints assure the consistency of model transactions. Model objects are stored in a distributed repository supporting atomicity and recovery of configuration change transactions. Element adapters are responsible for populating the repository with configuration objects, and for pushing committed changes to the underlying network elements. NESTOR has been implemented in two complementary versions and is now being applied to automate several configuration management scenarios of increasing complexity, with encouraging results.

Element Management System (EMS) is a system to manage network element (NE) for Ethernet Passive Optical Network (EPON). This allows the user to manage all the features of each network element (NE) individually. Network element (NE) exposes one or more management interfaces that the EMS uses to communicate with and to manage them. EMS has been design to fulfill FCAPS criteria which consist of fault management, configuration management, accounting management, performance management and security management. EPON is an emerging access network technology that provides a low-cost method of deploying optical access lines between a carrier’s central office and a customer site. EPON build on the International Telecommunications Union (ITU) standard G.983 for asynchronous transfer mode PON (APON) and seek to bring to life the dream of a full-services access network (FSAN) that delivers converged data, video, and voice over a single optical access system.

Operation support system (OSS) operates on 24times7 basis managing fault, configuration, accounting, performance and security operations from various network elements in a VoIP network. In traditional telephony systems there was a single point of connection from the OSS to the switch. Today VoIP networks are multi-vendor based distributed systems handling voice, data and multimedia content with multiple network elements and element management systems. Service providers expect a carrier grade quality system with single interface from OSS and a transparent flow through configuration management and provisioning system in VoIP networks. The challenge to network vendors and OSS service providers is to provide and end-to-end intelligent flow-through provisioning system across the various network elements and management systems. This paper describes practical tested architecture principles, design and emerging trends of flow-through provisioning system that is scalable and secure to serve millions of customers in a VoIP network

In this chapter, a brief overview of the role and applications of machine learning (ML) algorithms in future wireless cellular networks is presented, more specifically, in the context of self-organizing networks (SONs). SON is a promising and innovative concept, in which future networks are expected to analyze and use historical data in order to improve and adapt themselves to the network conditions. For this to be possible, however, algorithms that are capable of extracting patterns from data and learn from previous actions are necessary. This chapter highlights the utilization and possible applications of ML algorithms in future cellular networks. A brief introduction of ML and SON is presented, followed by an analysis of current state of the art solutions involving ML in SON. Lastly, guidelines on the utilization of intelligent algorithms in SON and future research trends in the area are highlighted and conclusions are drawn.

This article presents a comprehensive analysis of automated security configuration management for enterprise networking products, addressing the growing challenges in maintaining secure network configurations across complex modern infrastructures. It examines the evolution of network configuration management from traditional manual approaches to automated solutions, highlighting the critical role of automation in maintaining security posture and compliance. Through analysis of recent research and industry reports, the article explores the challenges posed by increasing network complexity, particularly in hybrid environments combining on-premises and cloud-based services. It investigates the components of automated configuration analysis systems, including scanning engines, security rule engines, and remediation recommendation systems. The article demonstrates the benefits of automated configuration management, including improved security consistency, reduced human error, enhanced proactive risk management, and streamlined compliance processes. It also provides detailed implementation considerations for organizations adopting automated configuration management solutions, emphasizing the importance of establishing clear security baselines, robust change management procedures, and regular updates to security rules and best practices.

Accounting performed by network and service providers covers the tasks of determining, collecting, and evaluating information on the service usage of their customers. This information forms the basis of the subsequent charging process. For performing these tasks in case of heterogeneous mobile networks a generic configuration management, specifically tailored at the provisioning of various Internet services is needed. This work defines a role model covering all participating entities of a distributed service provisioning environment for mobile networks together with key requirements. In addition, it outlines different accounting scenarios and proposes an architecture for a generic, technology- and service-independent configuration management of accounting in a distributed, mobile, and multi-provider environment. In turn, the approach specifies required configuration and state information in network components. Finally, a use case with an inter-domain scenario provides an example for the proposed accounting configuration management process.

Edge intelligence in radio access network (RAN) is an emerging concept wherein machine learning (ML) driven self-organizing network (SON) functions reside in edge nodes or base stations (BSs). This allows to fulfill ultra-low latency, scalability, ultra-high reliability and signaling needs and requirements for beyond-5G applications. However, the resource-intensive hyper-parameter search of edge-distributed ML workloads is a major challenge that undermines the gains of ML driven automated networks. In current edge-ML, resource-constraints force the ML driven SON functions to incline towards inference-only workloads at the edge, whereas their training is done in the central infrastructure in non-collaborative fashion. This requires a redundant transfer of big data for training from the edge to the central location and a hyper-parameter search with high space time complexity, eventually resulting in high signaling costs and delays. This paper aims to address these challenges by presenting the Coordinated HypEr-paramETer seArcH (CHEETAH) frame-work for edge intelligence. It is a resource-efficient, low time-complexity distributed framework that eliminates the transfer of large training data from the edge to the central location and reduces the time required to find optimal (or near-optimal) hyper-parameters. Using a traffic forecasting use-case based on real network data, we demonstrate that as compared to state-of-the-art, the CHEETAH framework offers an attractive pragmatic solution in terms of predicting the best hyper-parameter configuration while keeping the associated costs low.

The article discusses the basic principles of configuration management of electrical networks, aimed at ensuring the reliability, efficiency and cost-effectiveness of their operation. The relevance of an adaptive approach to network management in the conditions of increasing integration of distributed energy sources (DES) and variable electricity consumption schedules is substantiated. The main aspects of forming an optimal network structure to minimize power losses, improve the quality of power supply and ensure the system's resistance to external and internal influences are outlined. Attention is paid to key tasks related to the implementation of these principles, in particular, determining optimal load distribution points, disaster recovery algorithms, and automation of control processes. Modern approaches to network modeling and their implementation in the practice of energy companies are analyzed. Particular attention is paid to the need for coordination between network elements and active participation of consumers in optimization processes. The main effect of using a decentralized sectioning system is considered, i.e. automatic selective disconnection and localization of the damaged area, and automatic restoration of power supply to undamaged consumers.

Existing mobile networking systems lack the level of intelligence, scalability, and autonomous adaptability required to optimally enable next-generation networks like 5G and beyond, which are expected to be Self Organizing Networks (SONs). It is anticipated that machine learning (ML) will be instrumental in designing future “x”G SON networks with their demanding Quality of Experience (QoE) requirements. This paper evaluates a methodology that uses supervised machine learning to predict the QoE level of the end user experiences and uses this information to detect anomalous behavior of dysfunctional network nodes (eNodeBs/base stations) in self-organizing mobile networks. An end-to-end network scenario is created using the network simulator ns-3, where end users interact with a remote host that is accessed over the Internet to run the most commonly used applications like file downloads and uploads and the resulting output is used as a dataset to implement ML algorithms for QoE prediction and eNodeB (eNB) anomaly detection. Three ML algorithms were implemented and compared to study their effectiveness and the scalability of the methodology. In the test network, an accuracy score greater than 99% is achieved using the ML algorithms. As suggested by the ns-3 simulation the use of ML for QoE prediction will help network operators understand end-user needs and identify network elements that are failing and need attention and recovery.