Automated Network Management Research Articles

On the implementation of Cognitive Autonomous Networks

Cognitive Autonomous Networks (CAN) is a promising approach for advancing network management automation using machine learning based functions called Cognitive Functions (CFs). Thereby, the CFs interact with the environment to learn and decide suitable network configurations to optimize their objectives. To minimize conflicts among the actions of multiple CFs, the CFs send their proposed configurations to a Controller, which in turn computes the final value that optimizes the combined interest of all the CFs. However, simulating real‐life CAN deployments is challenging since: (a) the CFs have to reactively and interactively learn on the underlying system, and (b) the Controller must compute the optimal configurations in a dynamic environment with interdependent functions. In this letter, we present a scheme for implementing CAN in a simulation environment highlighting the critical design aspects for generating expected outcomes. Our results validate the proposed implementation design as the desired realistic behavior is obtained from CAN.

A Vision of Self-Evolving Network Management for Future Intelligent Vertical HetNet

Future integrated terrestrial-aerial-satellite networks will have to exhibit some unprecedented characteristics for the provision of both communications and computation services, and security for a tremendous number of devices with very broad and demanding requirements across multiple networks, operators, and ecosystems. Although 3GPP introduced the concept of self-organizing networks (SONs) in 4G and 5G documents to automate network management, even this progressive concept will face several challenges as it may not be sufficiently agile in coping with the immense levels of complexity, heterogeneity, and mobility in the envisioned beyond-5G integrated networks. In the presented vision, we discuss how future integrated networks can be intelligently and autonomously managed to efficiently utilize resources, reduce operational costs, and achieve the targeted Quality of Experience (QoE). We introduce the novel concept of the “self-evolving networks (SENs)” framework, which utilizes artificial intelligence, enabled by machine learning (ML) algorithms, to make future integrated networks fully automated and intelligently evolve with respect to the provision, adaptation, optimization, and management aspects of networking, communications, computation, and infrastructure nodes' mobility. To envisage the concept of SEN in future integrated networks, we use the Intelligent Vertical Heterogeneous Network (I-VHetNet) architecture as our reference. The article discusses five prominent scenarios where SEN plays the main role in providing automated network management. Numerical results provide an insight into how the SEN framework improves the performance of future integrated networks. The article presents the leading enablers and examines the challenges associated with the application of the SEN concept in future integrated networks.

Smart local energy systems (SLES): A framework for exploring transition, context, and impacts

Energy systems globally are becoming increasingly decentralised; experiencing new types of loads; incorporating digital or “smart” technologies; and seeing the demand side engage in new ways. These changes impact on the management and regulation of future energy systems and question how they will support a socially equitable, acceptable, net-zero transition. This paper couples a meta-narrative literature review with expert interviews to explore how socio-technical regimes associated with centralised systems of provision (i.e. the prevailing paradigm in many countries around the world) differ to those of smart local energy systems (SLES). Findings show how SLES regimes incorporate niche technologies, business models and governance structures to enable new forms of localised operation and optimisation (e.g. automated network management across energy vectors), smarter decision making and planning, by new actors (e.g. local authorities, other local stakeholders), and engaging users in new ways. Through this they are expected to deliver on a wide range of outcomes, both within the SLES boundary and to the wider system. However, there may be trade-offs between outcomes due to pressures for change originating from competing actors (e.g. landscape vs. incumbents in the regime); understanding the mapping between different outcomes, SLES elements and their interconnections will be key to unlocking wider benefits.

АДАПТИВНЫЙ МЕТОД И АЛГОРИТМ ОПЕРАТИВНОГО ОЦЕНИВАНИЯ ПАРАМЕТРОВ ТРАФИКА В ВЫСОКОСКОРОСТНЫХ КОРПОРАТИВНЫХ МУЛЬТИСЕРВИСНЫХ СЕТЯХ СВЯЗИ

The article deals with an adaptive method developed and an algorithm that implements it for the rapid evaluation of traffic characteristics and parameters in high-speed corporate multiservice communication networks. This algorithm operates in real-time environment. High-speed corporate multiservice networks are characterized by high dynamics of changing their state, including changes in the characteristics of transmitted traffic. Under these conditions, the automated network management system should provide the required quality of communication services and services provided to users. Thus, the relevance of this study is determined by the need to implement network management processes in a mode close to real time, with a given quality in the conditions of dynamic priority of unknown changes in network characteristics. The basis of the proposed method is the concept of conditional non-linear Pareto - optimal filtering by V.S. Pugachev, which consists in the fact that traffic parameters are estimated in two stages. Firstly, the forecast of parameter values is estimated, and then their correction is made with the following observations of parameters. Traffic parameter forecasts are made in a small sliding window, and refinement of current traffic parameter estimates is implemented by pseudogradient procedures, the parameters of which are regulated by the method of fuzzy Takagi-Sugeno logical output. In this case, increments in pseudo-gradient procedures are calculated relative to the average value of previous estimates calculated in a small moving window as well. This method reduced the average relative error of estimating the intensity parameters of non-stationary network traffic by 1.4-1.9 times, and also reduced the size of the sliding window in which it is processed from 1.7 to 4.2 times.The proposed method and algorithm belong to the class of methods and algorithms which require a preliminary training. The average relative error in estimating traffic parameters does not exceed 7%, which is a sufficient value for the implementation of operational network management tasks.

Deep Learning at the Mobile Edge: Opportunities for 5G Networks

Mobile edge computing (MEC) within 5G networks brings the power of cloud computing, storage, and analysis closer to the end user. The increased speeds and reduced delay enable novel applications such as connected vehicles, large-scale IoT, video streaming, and industry robotics. Machine Learning (ML) is leveraged within mobile edge computing to predict changes in demand based on cultural events, natural disasters, or daily commute patterns, and it prepares the network by automatically scaling up network resources as needed. Together, mobile edge computing and ML enable seamless automation of network management to reduce operational costs and enhance user experience. In this paper, we discuss the state of the art for ML within mobile edge computing and the advances needed in automating adaptive resource allocation, mobility modeling, security, and energy efficiency for 5G networks.

Adaptive ML-Based Frame Length Optimisation in Enterprise SD-WLANs

Software-Defined Networking (SDN) is gaining a lot of traction in wireless systems with several practical implementations and numerous proposals being made. Despite instigating a shift from monolithic network architectures towards more modulated operations, automated network management requires the ability to extract, utilise and improve knowledge over time. Beyond simply scrutinizing data, Machine Learning (ML) is evolving from a simple tool applied in networking to an active component in what is known as Knowledge-Defined Networking (KDN). This work discusses the inclusion of ML techniques in the specific case of Software-Defined Wireless Local Area Networks (SD-WLANs), paying particular attention to the frame length optimization problem. With this in mind, we propose an adaptive ML-based approach for frame size selection on a per-user basis by taking into account both specific channel conditions and global performance indicators. By relying on standard frame aggregation mechanisms, the model can be seamlessly embedded into any Enterprise SD-WLAN by obtaining the data needed from the control plane, and then returning the output back to this in order to efficiently adapt the frame size to the needs of each user. Our approach has been gauged by analysing a multitude of scenarios, with the results showing an average improvement of 18.36% in goodput over standard aggregation mechanisms.

A Self-Tuning Algorithm for Optimal QoE-Driven Traffic Steering in LTE

Due to the wide diversity of services in mobile networks, cellular operators have changed their focus from Quality of Service (QoS) to Quality of Experience (QoE). To manage this change, Self-Organizing Networks (SON) techniques have been developed to automate network management, with traffic steering as a key use case. Traditionally, traffic steering aims to balance traffic volume or load among adjacent cells. Although more advanced schemes have been devised to balance QoE among cells, these do not guarantee that the overall system QoE is improved. In this work, a novel self-tuning algorithm for parameters in a classical mobility load balancing scheme is proposed to steer traffic among adjacent cells in a Long-Term Evolution (LTE) network driven by QoE criteria. Unlike previous approaches, based on heuristic rules, the proposed algorithm takes a gradient ascent approach to ensure that parameter changes always improve the overall system QoE. For this purpose, the impact of parameter changes on system QoE is estimated with an analytical network performance model that can be adjusted with statistics taken from the real network. The proposed algorithm is tested in a system-level simulator implementing a realistic LTE scenario. Results show that the method outperforms classical load and QoE mobility load balancing schemes.

SDN/NFV-Empowered Future IoV With Enhanced Communication, Computing, and Caching

Internet-of-Vehicles (IoV) connects vehicles, sensors, pedestrians, mobile devices, and the Internet with advanced communication and networking technologies, which can enhance road safety, improve road traffic management, and support immerse user experience. However, the increasing number of vehicles and other IoV devices, high vehicle mobility, and diverse service requirements render the operation and management of IoV intractable. Software-defined networking (SDN) and network function virtualization (NFV) technologies offer potential solutions to achieve flexible and automated network management, global network optimization, and efficient network resource orchestration with cost-effectiveness and are envisioned as a key enabler to future IoV. In this article, we provide an overview of SDN/NFV-enabled IoV, in which SDN/NFV technologies are leveraged to enhance the performance of IoV and enable diverse IoV scenarios and applications. In particular, the IoV and SDN/NFV technologies are first introduced. Then, the state-of-the-art research works are surveyed comprehensively, which is categorized into topics according to the role that the SDN/NFV technologies play in IoV, i.e., enhancing the performance of data communication, computing, and caching, respectively. Some open research issues are discussed for future directions.

Monitoring and Data Analytics for Optical Networking: Benefits, Architectures, and Use Cases

Operators' network management continuously measures network health by collecting data from the deployed network devices; data is used mainly for performance reporting and diagnosing network problems after failures, as well as by human capacity planners to predict future traffic growth. Typically, these network management tools are generally reactive and require significant human effort and skills to operate effectively. As optical networks evolve to fulfil highly flexible connectivity and dynamicity requirements, and supporting ultra-low latency services, they must also provide reliable connectivity and increased network resource efficiency. Therefore, reactive human-based network measurement and management will be a limiting factor in the size and scale of these new networks. Future optical networks must support fully automated management, providing dynamic resource re-optimization to rapidly adapt network resources based on predicted conditions and events; identify service degradation conditions that will eventually impact connectivity and highlight critical devices and links for further inspection; and augment rapid protection schemes if a failure is predicted or detected, and facilitate resource optimization after restoration events. Applying automation techniques to network management requires both the collection of data from a variety of sources at various time frequencies, but it must also support the capability to extract knowledge and derive insight for performance monitoring, troubleshooting, and maintain network service continuity. Innovative analytics algorithms must be developed to derive meaningful input to the entities that orchestrate and control network resources; these control elements must also be capable of proactively programming the underlying optical infrastructure. In this article, we review the emerging requirements for optical network management automation, the capabilities of current optical systems, and the development and standardization status of data models and protocols to facilitate automated network monitoring. Finally, we propose an architecture to provide Monitoring and Data Analytics (MDA) capabilities, we present illustrative control loops for advanced network monitoring use cases, and the findings that validate the usefulness of MDA to provide automated optical network management.

CATH: an effective method for detecting denial-of-service attacks in software defined networks

Software defined networks (SDNs) are innovative networkframeworks that have recently received wide attention. Their programmingflexibility facilitates automatic network management and control, thusmitigating existing issues in the traditional network architecture. However,SDNs face several security risks, in particular denial-of-service (DoS)attacks, the most common and serious network attacks. To address such athreat, an SDN-DoS attack detection method is proposed based on fusingmultiple flow features for describing the network catastrophe between thenormal and the attack state. Several statistic attributes of SDN flowinformation are first chosen as detection features; subsequently, the cuspmodel is used to establish a catastrophe equilibrium surface for SDN states.After being trained, the cusp catastrophe model can be utilized to inferwhether an SDN is under DoS attack. The experimental results demonstratethat the method can effectively and timely perceive SDN-DoS attacks, notonly in simple networks but also in larger enterprise networks.

Improving Cell Outage Management Through Data Analysis

Self-organizing networks (SONs) are an important feature for network management automation in the new generation of mobile communications. While SONs have been considered to be part of the recent 3GPP standards such as LTE, it is expected that future 5G mobile networks will present new challenges for SON solutions. One of the most important use cases in self-healing is cell outage compensation (COC). This article proposes an important improvement in COC functionality based on analyzing large real data sets from live networks in order to adapt compensation to the real neighboring context. First, we propose two methods (offline and online) to classify outages depending on the degraded metrics in neighboring cells, and we present results for a set of cell outages occurring in a live LTE network. Second, we propose a method for estimating lost traffic due to cell outages in order to quantify the load that cannot be intrinsically absorbed by neighboring cells. Finally, a novel COC methodology is proposed by taking into account the results obtained in the two previous studies.

Coordination of SON Functions in Multi-Vendor Femtocell Networks

With the increase of network complexity, there is a high need for network management automation. This is achieved through SON principles that enable self-configuration, self-optimization, and self-healing. However, even though SON functions are meant to be autonomous, a high level of coordination among them is required. To this end, efficient conflict detection and resolution techniques are needed, especially in multi-vendor deployments. This article presents a design together with a sample implementation of a coordination scheme between three key SON functions in femtocell networks: cell ID assignment, coverage adjustment, and idle mode control. This ensures stability and continuity of the network operation even in a situation when the functions have contradicting objectives. The solution is based on the Broadband Forum TR-069 protocol and is applicable to multi-vendor networks. Simulation evaluation has shown that SON coordination reduces mean cell ID conflicts by over 30 percent and, resulting from that, call drop probability by over 40 percent.

Combination of multiple diagnosis systems in Self-Healing networks

The Self-Organizing Networks (SON) paradigm proposes a set of functions to automate network management in mobile communication networks. Within SON, the purpose of Self-Healing is to detect cells with service degradation, diagnose the fault cause that affects them, rapidly compensate the problem with the support of neighboring cells and repair the network by performing some recovery actions.The diagnosis phase can be designed as a classifier. In this context, hybrid ensembles of classifiers enhance the diagnosis performance of expert systems of different kinds by combining their outputs. In this paper, a novel scheme of hybrid ensemble of classifiers is proposed as a two-step procedure: a modeling stage of the baseline classifiers and an application stage, when the combination of partial diagnoses is actually performed. The use of statistical models of the baseline classifiers allows an immediate ensemble diagnosis without running and querying them individually, thus resulting in a very low computational cost in the execution stage.Results show that the performance of the proposed method compared to its standalone components is significantly better in terms of diagnosis error rate, using both simulated data and cases from a live LTE network. Furthermore, this method relies on concepts which are not linked to a particular mobile communication technology, allowing it to be applied either on well established cellular networks, like UMTS, or on recent and forthcoming technologies, like LTE-A and 5G.

Multilevel pattern mining architecture for automatic network monitoring in heterogeneous wireless communication networks

The rapid development of network technology and its evolution toward heterogeneous networks has increased the demand to support automatic monitoring and the management of heterogeneous wireless communication networks. This paper presents a multilevel pattern mining architecture to support automatic network management by discovering interesting patterns from telecom network monitoring data. This architecture leverages and combines existing frequent itemset discovery over data streams, association rule deduction, frequent sequential pattern mining, and frequent temporal pattern mining techniques while also making use of distributed processing platforms to achieve high-volume throughput.

Software-Defined Networking for Scalable Cloud-based Services to Improve System Performance of Hadoop-based Big Data Applications

The rapid growth of Cloud Computing has brought with it major new challenges in the automated manageability, dynamic network reconfiguration, provisioning, scalability and flexibility of virtual networks. OpenFlow-enabled Software-Defined Networking (SDN) alleviates these key challenges through the abstraction of lower level functionality that removes the complexities of the underlying hardware by separating the data and control planes. SDN has an efficient, dynamic, automated network management, higher availability and application provisioning through programmable interfaces which are very critical for flexible and scalable cloud-based services. In this study, the author explores broadly useful open technologies and methodologies for applying an OpenFlow-enabled SDN to scalable cloud-based services and a variety of diverse applications. The approach in this paper introduces new research challenges in the design and implementation of advanced techniques for bringing an SDN-enabled components and big data applications into a cloud environment in a dynamic setting. Some of these challenges become pressing concerns to cloud providers when managing virtual networks and data centers, while others complicate the development and deployment of cloud-hosted applications from the perspective of developers and end users. However, the growing demand for manageable, scalable and flexible clouds necessitates that effective solutions to these challenges be found. Hence, through real-world research validation use cases, this paper aims at exploring useful mechanisms for the role and potential of an OpenFlow-enabled SDN and its direct benefit for scalable cloud-based services. Finally, it demonstrates the impact of an OpenFlow-enabled SDN that fully embraces the opportunities and challenges of cloud infrastructures to improve the system performance of Hadoop-based big data applications by utilizing the network control capabilities of an OpenFlow to solve network congestion.

Context-Aware Self-Optimization: Evolution Based on the Use Case of Load Balancing in Small-Cell Networks

Self-organizing network (SON) mechanisms in cellular systems automate network management procedures to improve performance while reducing operational expenditure (OPEX) and capital expenditure (CAPEX). Within the area of SON research and development, self-optimization is an especially hot research topic, and key use cases have been defined by Third-Generation Partnership Project (3GPP) standards. This article proposes to apply the context-awareness concept to self-optimizing small-cell networks. Context information provides valuable additional data to enhance self-optimization mechanisms. In this context, a framework for context-aware (CA) self-optimization in smallcell environments is described, and a particular self-optimization use case, load balancing (LB), is used to evaluate the benefits of the proposed approach. The results show that the proposed CA-LB algorithms increase the users? satisfaction, while the optimization time is significantly reduced.

Correlation-Based Time-Series Analysis for Cell Degradation Detection in SON

The increasing amount of network elements in the current deployments of cellular networks is leading to an enormous complexity of the operation and maintenance. Self-organizing networks (SONs) is a good solution for operators to save operational expenditures by automating network management. One of the key challenges in this context is the automatic identification of degraded cells. In this letter, a method to detect degraded cells through the analysis of the time evolution of metrics is proposed. Results show that cell faulty patterns can be effectively detected by comparing them with a set of fictitious degraded patterns.

A recommender system architecture for predictive telecom network management

Current telecom networks generate massive amounts of monitoring data consisting of observations on network faults, configuration, accounting, performance, and security. Due to the ever increasing degree of complexity of networks, coupled with specific constraints (legal, regulatory, increasing scale of management in heterogeneous networks), the traditional reactive management approaches are increasingly stretched beyond their capabilities. A new network management paradigm is required that takes a preemptive rather than reactive approach to network management. This work presents the design and specification of E-Stream, a predictive recommendationbased solution to automated network management. The architecture of E-Stream illustrates the challenges of leveraging vast volumes of management data to identify preemptive corrective actions. Such design challenges are mitigated by the components of E-Stream, which together form a single functional system. The EStream approach starts by abstracting trace information to extract sequences of events relevant to interesting incidents in the network. After observing event sequences in incoming event streams, specific appropriate actions are selected, ranked, and recommended to preempt the predicted incidents.

Automated network management and configuration using Probabilistic Trans-Algorithmic Search

Online configuration of large-scale systems such as networks requires parameter optimization within a limited amount of time, especially when configuration is needed as a response to recover from a failure in the system. To quickly configure such systems in an online manner, we propose a Probabilistic Trans-Algorithmic Search (PTAS) framework which leverages multiple optimization search algorithms in an iterative manner. PTAS applies a search algorithm to determine how to best distribute available experiment budget among multiple optimization search algorithms. It allocates an experiment budget to each available search algorithm and observes its performance on the system-at-hand. PTAS then probabilistically reallocates the experiment budget for the next round proportional to each algorithm’s performance relative to the rest of the algorithms. This “roulette wheel” approach probabilistically favors the more successful algorithm in the next round. Following each round, the PTAS framework “transfers” the best result(s) among the individual algorithms, making our framework a trans-algorithmic one. PTAS thus aims to systematize how to “search for the best search” and hybridize a set of search algorithms to attain a better search. We use three individual search algorithms, i.e., Recursive Random Search (RRS) (Ye and Kalyanaraman, 2004), Simulated Annealing (SA) (Laarhoven and Aarts, 1987), and Genetic Algorithm (GA) (Goldberg, 1989), and compare PTAS against the performance of RRS, GA, and SA. We show the performance of PTAS on well-known benchmark objective functions including scenarios where the objective function changes in the middle of the optimization process. To illustrate applicability of our framework to automated network management, we apply PTAS on the problem of optimizing link weights of an intra-domain routing protocol on three different topologies obtained from the Rocketfuel dataset. We also apply PTAS on the problem of optimizing aggregate throughput of a wireless ad hoc network by tuning datarates of traffic sources. Our experiments show that PTAS successfully picks the best performing algorithm, RRS or GA, and allocates the time wisely. Further, our results show that PTAS’ performance is not transient and steadily improves as more time is available for search.

Reconfigurable Optical Add and Drop Multiplexers A Review

Optical multiplexing is the key function of a WDM network and reliable method for data transport networks. WDM networks configured as rings/mesh along with Optical Add-Drop Multiplexers supports added flexibility, simplicity and augment the spectral efficiency. Further enhancement achieved with Reconfigurable OADM architectures, growing briskly along with automatic network management, let the transport network to acclimatize with dynamically varying environment and flexibly respond to the transport network changes. It permits single or many wavelengths to be added and/or dropped from a transport fiber without optical-to-electrical-to-optical domain translation. Presently ROADM technology has revolutionized optical networking and an inseparable part of modern optical communication offering huge bandwidth for data transport at minimum expense. In this view the article presents comprehensive study for numerous generations of ROADM and their architecture and persistent development.