BaseWorX platform for building object-oriented management applications

In network management applications, load balancing has typically been achieved by manually configuring the application in-situ or using configuration information generated using offline tools. As networks increase in scale and heterogeneity, management applications are being designed to take advantage of scalable computer hardware and often have multiple instances, each of which bears a portion of the management load of the network elements or functions being managed. The communication between management applications and the network elements they are managing is often stateful, posing a challenge for load balancing because connections must be managed if responsibility for managing some network elements change from one instance to another during load balancing. In this paper, we describe an algorithm that enables load balancing of telecommunication management applications for an arbitrary number of VNFs (Virtual Network Functions). The algorithm, triggered by NFV (Network Function Virtualisaton) orchestration, is horizontally scalable and there is no single point of failure once two or more management application instances are running.

Today few organizations have a thorough understanding of what is on their networks at any given time. This study tries to develop Computer and Network Asset Manager (CNAM) that is a network management application which helps large enterprises, and Small medium enterprises (SMEs) service providers, manage their data centers and IT infrastructure efficiently and cost effectively. CANM collects information on all hardware components of the network instruments that are on the network. In this project, a real-time network monitoring method is suggested for dynamic information to reduce the complexity and cost based on network management. In the proposed method, a Simple Network Management Protocol (SNMP) is first used to collect data for start interaction between equipment in the specific data center, to gather these data SNMP would need to request it from Management Information Base (MIB) that served as a database for stored information. Furthermore, to ensure data authenticity in the management station that fetches real-time data periodically from equipment, network engineer would have to manually key-in static information into CNAM when it was first launched. The SNMP would then pick up on this stored static information and automatically began collecting real-time data; this feature will be developed with the help of Model View Controller (MVC) pattern that is a software pattern for implementing user interfaces. It allows users to have interaction with the CNAM as well as input the related data. The object oriented approach shall be used to implement SNMP and MIB which represents system management information. Web Based Enterprise Management (WBEM) is required in order to transfer protocol like SNMP, as well as to enter the actual data manually for further authenticity with the real time data. This study would hopefully encourage companies to try an interactive solution for problem in managing their equipment in the data center.

Network management has suffered from increases in business, system, and operational complexity. This has been exacerbated by the heterogeneity in management data as well as the high quality requirements of multimedia services. Autonomic networking manages this growing complexity by adding intelligence inside network nodes and network management applications. While most autonomic applications simply use a control loop to monitor and configure entities, our work is aimed at building a self-governing network that is able to fulfill the requirements of current and future services. This means that management applications need a detailed and dynamic view of the contextual status of the network nodes as a whole in order to adapt their behaviour to changing context. In this paper, we propose an algorithm to semi-automatically generate filter rules based on existing information in a network management information model. These filter rules are used to determine the set of contextual data that needs to be exchanged with other nodes. The algorithm exploits the reasoning capabilities of ontologies and relies on the introduction of additional semantic relationships to achieve a fine-grained context exchange model. Large scale evaluations were conducted to characterise the performance of this ontological approach.

This article describes a key derivation method that greatly simplifies key distribution issues for network management (NM) applications and may in some cases relieve NM stations from the need to store any keys at all. This method is easily generalized for network protocols and applications where several masters need to talk to a large number of slaves. It shows a way to cut down on the amount of keys stored with little impact on security. This is especially important when human users are involved.

In this paper a new method is proposed that solves for the steady state of pressure-dependent models (PDMs) with flow control valves. Rather than model flow devices individually, the method solves the more general problem in which a water distribution system (WDS) has some link flows constrained to lie between upper and lower, or possibly equal, set limits. No heuristics are used to determine device states. The method is shown to be fast, and its effectiveness is demonstrated on PDM WDSs with up to about 20,000 links and 18,000 nodes and 60 link flow constraints, some of which prescribe a fixed flow. The method has applications in network management, network design, and flow control to deal with water distribution where there is insufficient supply.

In-band network telemetry (INT) enriches network management at scale through the embedding of complete device-internal states into each packet along its forwarding path, yet such embedding of INT information also incurs significant band-width overhead in the data plane. We propose DeltaINT, a general INT framework that achieves extremely low bandwidth overhead and supports various packet-level and flow-level applications in network management. DeltaINT builds on the insight that state changes are often negligible at most time, so it embeds a state into a packet only when the state change is deemed significant. We theoretically derive the time/space complexities and the bounds of bandwidth mitigation for DeltaINT. We implement DeltaINT in both software and P4. Our evaluation shows that DeltaINT reduces up to 93% of INT bandwidth, and its deployment in a Barefoot Tofino switch incurs limited hardware resource usage.

Recent advances in object technology have given rise to a set of powerful tools that are referred to as application builders. Application builders attempt to raise the level of abstractions in application development process from programming to composition of predefined components. To achieve this, application builders provide a set of components and facilities to compose these components. However, the composition facilities of the current application builders are primitive and the set of predefined components they provide can only be extended by programmers. Application builders that allow developers to build applications using domain specific components, semantics and primitives are needed. This article presents an Object-Centered Application Development System which provides a set of flexible high-level modeling and composition constructs and supports domain specific application development.

Accurate network traffic forecasting is essential for the proactive management of modern communication systems, particularly for emerging satellite constellations. However, forecasting network traffic is a non-trivial task, as the data exhibit complex statistical properties such as self-similarity, spatial–temporal long-range dependence, and non-stationarity, which challenge even the recent surge of deep learning models like iTransformer and DLinear. To address these limitations, this paper proposes MDETST, a novel Transformer-based architecture designed to more effectively capture and disentangle these complex, multi-scale spatio-temporal dependencies. Recognizing the scarcity of public satellite data, we conduct a comprehensive evaluation on the large-scale CESNET-TIMESERIES24 dataset, a challenging real-world benchmark that shares the critical statistical properties of both satellite and terrestrial systems. Extensive experiments demonstrate that our proposed MDETST model achieves state-of-the-art performance, consistently and significantly outperforming strong baselines, especially in demanding long-term forecasting scenarios. This work provides a robust and effective solution for complex traffic forecasting, showing strong potential for application in both satellite and terrestrial network management.

With the phenomenal success of diffusion models and ChatGPT, deep generation models (DGMs) have been experiencing explosive growth from 2022. Not limited to content generation, DGMs are also widely adopted in Internet of Things, Metaverse, and digital twin, due to their outstanding ability to represent complex patterns and generate plausible samples. In this article, we explore the applications of DGMs in a crucial task, i.e., improving the efficiency of wireless network management. Specifically, we firstly overview the generative AI, as well as three representative DGMs. Then, a DGM-empowered framework for wireless network management is proposed, in which we elaborate the issues of the conventional network management approaches, why DGMs can address them efficiently, and the step-by-step workflow for applying DGMs in managing wireless networks. Moreover, we conduct a case study on network economics, using the state-of-the-art DGM model, i.e., diffusion model, to generate effective contracts for incentivizing the mobile AI-Generated Content (AIGC) services. Last but not least, we discuss important open directions for the further research.

This paper develops a generalized admittance matrix approach in Fortescue coordinate system to solve unbalanced/unsymmetrical distribution networks including different number of phases. This generalized Fortescue π equivalent is defined in this paper for solving the heterogeneous phase, and thus Fortescue, network model. The performance of the approach is demonstrated in different model networks with number of nodes ranging between 168 and 14200. It is found that the current iteration method exploiting the decoupling in admittance matrix in Fortescue coordinate is substantially faster than the typical unbalanced three-phase solution in phase domain. The method has a significant potential for application in real time active power network management.

Ad hoc networks are intrinsically dynamic with respect to mobility, traffic patterns, node density, number of nodes, physical topology, and others. This scenario imposes several challenges to the ad hoc routing protocols, and there is no single solution for all scenarios. This paper proposes the application of Policy-Based Network Management (PBNM) for dynamic reconfiguration of ad hoc networks. PBNM uses the concept of policies formed by events, conditions and actions. The occurrence of an event triggers conditions evaluation, and if evaluated to true, a set of actions should be performed. The paper contribution comprises the proposal of an ad hoc policy information model based on DEN-ng policy model for the implementation of a policy manager prototype to be integrated in the NS-2 simulator allowing dynamic reconfiguration of routing protocol parameters in simulation time; and the proposal of policies for dynamically adjusting the ad hoc routing protocol behavior.KeywordsNode DensityNetwork DeviceDynamic ReconfigurationNetwork AvailabilityPolicy RepositoryThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

A variety of Web-based applications, mobile apps, and other over the top data services with affordable 3G/4G enabled smart devices are major factors for enormous increase in heterogeneous data traffic at enterprise and mobile networks. This creates challenges regarding traffic management and requires traffic-aware intelligent network management to deliver sustained quality of experience for subscribers. Deep packet inspection and analysis (DPIA) provides base platform for development of traffic-aware intelligent network management and security systems. However, computationally complex DPIA-related packet processing for high speed data traffic makes these systems expensive. Furthermore, conventionally these traffic-aware network management and security systems are deployed in enterprise networks with independent and dedicated DPIA-related processing resources and require multiple copies of passively provisioned high speed data from network, while performing similar DPIA operations over the same data again and again. This duplicate deployment of expensive software and hardware resources for DPIA processing eventually results in higher capital expenditures as well as operational expenditures for network operators. We have proposed a novel service-oriented framework for heterogeneous deep packet inspection and analysis (SoDPI) that simultaneously provides diversified DPIA services to multiple client applications for network management and security operations in high-speed networks. Proposed framework provides flexible and comprehensive API-based service interface for client applications to register required DPIA services. SoDPI framework implementation is based on commodity hardware and deploys shared set of DPIA-related packet processing components, requiring only single copy of passive data provisioned from network. Experimental evaluations show that novel SoDPI framework requires considerably reduced amount of software and hardware resources to fulfill heterogeneous DPIA packet processing requirements for multiple client applications in comparison with conventional network management and security applications with dedicated DPIA components. This results in lower cost impacts for network operators with more network manageability.

Chapter 11 - New Directions in Policy-Based Management

Phasor Measurement Units (PMUs) are the most modern measurement devices for electric power networks. Their performance, in terms of synchrophasor, frequency and rate of change of frequency (ROCOF) measurement accuracy are crucial for the overall performance of the network management and control applications that are expected to rely on PMU-based distributed measurement systems. In this regard, PMU performance should be assessed under different conditions. IEEE standard C37.118.1, which is particularly focused on PMUs for power transmission systems, reports tests and requirements for PMU compliance. However, some important operating conditions are neglected. In particular, the presence of unbalance in the three-phase system appears relevant for an advisable application of PMUs to distribution networks. In this paper, the impact of unbalance on two important classes of PMU algorithms is discussed. Analytical formulas are derived to estimate the effect of unbalance on synchrophasor, frequency and ROCOF estimations, and they are verified by means of numerical simulations.

Recent years have witnessed significant growth in the adoption of portable wireless communication and computing devices (e.g., laptops, PDAs, smart phones) and large-scale deployment of wireless networks (e.g., cellular, WLANs). We envision that future usage of mobile devices and services will be highly personalized. Users will incorporate these new technologies into their daily lives, and the way they use new devices and services will reflect their personality and lifestyle. Therefore it is imperative to study and characterize the fundamental structure of wireless user behavior in order to model, manage, leverage and design efficient mobile networks and services. In this study, using our systematic TRACE approach, we analyze wireless users' behavioral patterns by extensively mining wireless network logs from two major university campuses. We represent the data using location-preference vectors, and utilize unsupervised learning (clustering) to classify trends in user behavior using novel similarity metrics. Matrix decomposition techniques are used to identify (and differentiate between) major patterns. We discover multi-modal user behavior and hundreds of distinct groups with unique behavioral patterns in both campuses, and their sizes follow a power-law distribution. Our methods and findings might provide new directions in network management and behavior-aware network protocols and applications, to name a few.