Network Management Framework Research Articles

Implementation of network management in child protection policy: Analysis of mobilization strategy in Makassar City

This study aims to examine network management in the implementation of child protection policies in Makassar City, with a focus on mobilization strategies implemented by the Office of Women's Empowerment and Child Protection (DP3A). The research method used is a qualitative approach with data collection techniques through interviews, observations, and document studies. Informants in this study include representatives from DP3A, Makassar Police, Institute for Public Policy Studies (LSKP), and other related institutions. The results show that the mobilization strategy implemented by DP3A Makassar City, which includes motivating and rewarding networks, utilizing digital platforms, and cross-sector collaboration, has succeeded in increasing the effectiveness of child protection policy implementation. This success is in line with network management theory. DP3A Makassar City has managed to gather and utilize various resources, strengthen cross-sector collaboration, and accelerate responses to child abuse cases. The use of digital platforms and continuous evaluation are also key factors in the success of child protection efforts in Makassar City. This research provides recommendations for strengthening inter-agency coordination and collaboration through the establishment of regular coordination forums, developing efficient information and data exchange mechanisms, and establishing an integrated referral system. Future research is expected to develop a network management model or framework that can be adapted and applied in the context of child protection at the local, regional and national levels in Indonesia.

(OFC 2024) An Intent-Based Control and Management Framework for Optical Transport Networks supporting 6G Services Empowered by Large Language Models

(OFC 2024) An Intent-Based Control and Management Framework for Optical Transport Networks supporting 6G Services Empowered by Large Language Models

A dynamic resilience management framework for deep-tier supply networks

The unprecedented supply chain disruptions caused by COVID-19 has had severe operational and financial consequences to firms across industries and continents. While tactical reactionary strategies can help, firms are in need of proactive management approaches to design more resilient supply chain networks in the first place. Firms are looking for an effective framework to design and monitor supply networks, mitigate disruption consequences, and manage resilience under different scenarios. We propose a framework to manage the resilience of deep-tier automotive supply networks by integrating a simulation-based resilience assessment scheme for effectiveness with an efficient optimization-based framework to find optimal strategies for handling regular disruption events. The framework promotes network analysis techniques combined with discrete-event simulation informed by secondary data sources and global supply risk databases for improving resilience management. We validate the effectiveness of the proposed framework using a real-world global automotive original equipment manufacturer case study. Our results demonstrate that the proposed dynamic framework relying on deep-tier visibility can optimize resilience strategies through all key performance indicators. The results show an average of 35% and 40% reductions in back-ordered cost and shipment delays, respectively, with a marginal growth in holding cost when the proposed framework is implemented with deep-tier visibility.

A Computational Framework for Enhancing Industrial Operations and Electric Network Management: A Case Study

A Computational Framework for Enhancing Industrial Operations and Electric Network Management: A Case Study

Supporting MANOaaS and Heterogenous MANOaaS Deployment Within the Zero-Touch Network and Service Management Framework

Supporting MANOaaS and Heterogenous MANOaaS Deployment Within the Zero-Touch Network and Service Management Framework

Regional bioethanol supply chain optimization with the integration of GIS-MCDM method and quantile-based scenario analysis

This study presents a novel decision-support framework for the bioethanol supply chain network planning and management under uncertainties. Under the holistic framework, the most suitable sites for biorefineries are first screened out by adopting a GIS-based multi-criteria decision-making approach. Then, a mixed-integer linear programming model combined with quantile-based scenario analysis is developed to determine the strategic planning (i.e. locations and size of biorefineries) and tactical management (i.e. biomass purchasing, feedstock transportation, bioethanol production, and product delivery) under uncertainties. The model can effectively search for reliable solutions under uncertainties and achieve tradeoff solutions with the consideration of decision makers’ risk tolerance. The proposed framework is demonstrated through a case study in China. It is suggested to build seven biorefineries with a capacity of 100 million liters in Zhumadian city. Utilizing 41% of local agricultural residues could satisfy the bioethanol requirement in the transportation sector under the E20 policy. However, the estimated production cost of bioethanol in Zhumadian is very high, about 1.11 $/L, which makes it lose cost advantage in the fuel market. Thus, currently, effective subsidies, mandatory energy substitution policies, along other environmental regulatory measures are desired to promote the bioethanol industry development.

Efficient Mobility Management in Mobile Edge Computing Networks: Joint Handover and Service Migration

Mobile edge computing (MEC) has been envisioned as an essential technology for latency-critical applications by providing computing services in close proximity to mobile users. Bringing MEC to come into practice, how to support user mobility remains challenging. In addition to seeking a thorny trade-off between service latency and migration cost, both interactions in space and time exist in mobility management, which requires collaboration among users and perfect prior knowledge, including user mobility and network information. In this paper, we propose an efficient mobility management framework for MEC networks, in which mobility management is operated centered around users’ performance and cost, while radio access and computing service provision are loosely coupled. With a loosely coupled design, the proposed framework exhibits more flexibility and incurs higher complexity. Focusing on multi-user and multi-cell MEC networks, this joint control problem is formulated to maximize the long-term total utility accounting for the service delay and migration cost. Considering the exponential complexity, a distributed mobility management approach is developed, which combines game theory and user-oriented deep reinforcement learning to deal with the interactions in space and time. Simulation results show the efficiency and scalability of the proposed approach.

A novel duplex deep reinforcement learning based RRM framework for next-generation V2X communication networks

Resource management in the next-generation vehicle-to-everything (V2X) communication networks is a demanding research problem. It is difficult to achieve the best results if the resources are not managed efficiently. To address this challenge and facilitate the dynamic movement of network demands, efficient management of resources is crucial. In this context, we propose a duplex deep reinforcement learning (DDRL)-based resource management framework for next-generation V2X communication networks. Our framework considers multiple network state parameters and utilizes these resources during the learning phase to address resource management problems effectively. The key objective was to select the forthcoming resource control state through the duplex deep reinforcement learning phase, thereby optimizing resource utilization efficiency. To achieve this, we jointly implement time division duplexing (TDD) and frequency division duplexing (FDD), which allow for adaptive management of TDD and FDD to meet high mobility requirements and efficiently utilize limited radio resources. This study introduces a dynamic duplex deep Q-learning-assisted sequential decision-making algorithm to manage band resources for the next-generation V2X network. By employing this algorithm, we enable the efficient allocation of resources, considering factors such as latency, system throughput, network utilization, busy channel rate (BCR), and packet received rate. To validate the performance of our proposed framework, we conducted simulation experiments using benchmarking techniques. The results demonstrate that our framework outperforms benchmark approaches across various performance parameters. It exhibits superior efficiency in terms of latency, system throughput, network utilization, BCR, and packet received rate.

E2E network slice management framework for 5G multi-tenant networks

E2E network slice management framework for 5G multi-tenant networks

A novel fractional uplink power control framework for self-organizing networks

Internet of things and network densification bring significant challenges to uplink management. Only depending on optimization algorithm enhancements is not enough for uplink transmission. To control intercell interference, Fractional Uplink Power Control (FUPC) should be optimized from network-wide perspective, which has to find a better traffic distribution model. Conventionally, traffic distribution is geographic-based, and ineffective due to tricky locating efforts. This paper proposes a novel uplink power management framework for Self-Organizing Networks (SON), which firstly builds up pathloss-based traffic distribution model and then makes the decision of FUPC based on the model. PathLoss-based Traffic Distribution (PLTD) aggregates traffic based on the propagation condition of traffic that is defined as the pathloss between the position generating the traffic and surrounding cells. Simulations show that the improvement in optimization efficiency of FUPC with PLTD can be up to 40% compared to conventional GeoGraphic-based Traffic Distribution (GGTD).

Addressing ZSM Security Issues with Blockchain Technology

Undoubtedly, we are witnessing a new era of computer networks that aspire to support modern demanding applications by providing the highest Quality of Experience (QoE) to the end user. Next Generations Networks (NGNs) ensure that characteristics such as ultra-low latency, high availability and wide service coverage can be met across the network regardless of the network infrastructure ownership. To accomplish that, beyond the necessary improvements in the radio propagation field, changes have been made in the core network functions which are now characterized as programmable, and software defined. Software Defined Networks (SDNs) and Network Function Virtualization (NFV) are the keystones of the NGNs flexibility. The high expectations of NGNs’ performance and the continuous changes in the network conditions lead to the development of new network management frameworks that add elasticity and dynamicity and minimize human intervention. ETSI (the European Standards Organization) presents the Zero-touch Service Management (ZSM) framework that uses hyped technologies such as Artificial Intelligence (AI) and Machine Learning (ML) to achieve full end-to-end automation of the network services’ management across one or many different domains. Focusing on multi-domain network service management, there are several security issues identified by the standardization team which mostly derive from the lack of trust among network providers. In the present research, we explore the suitability of blockchain technology adoption for facing these security issues. Blockchain technology inherently addresses security in trustless environments such as the infrastructures defined by the ZSM team. Our contribution is three-fold: (a) we define the architecture of a multi-domain network infrastructure that adopts the ZSM approach and integrates blockchain functionality, (b) we explore the adoption of different blockchain and distributed ledger technologies (DLT) approaches to address ZSM security needs and (c) we provide guidelines to prospective solution designers/implementers on the detailed requirements that this solution has to meet to maximize the offered value.

A Trust Management Framework for Vehicular Ad Hoc Networks

Vehicular Ad Hoc Networks (VANETs) enable road users and public infrastructure to share information that improves the operation of roads and driver experience. However, these are vulnerable to poorly behaved authorized users. Trust management is used to address attacks from authorized users in accordance with their trust score. By removing the dissemination of trust metrics in the validation process, communication overhead and response time are lowered. In this paper, we propose a new Tamper-Proof Device (TPD) based trust management framework for controlling trust at the sender side vehicle that regulates driver behaviour. Moreover, the dissemination of feedback is only required when there is conflicting information in the VANET. If a conflict arises, the Road-Side Unit (RSU) decides, using the weighted voting system, whether the originator is to be believed, or not. The framework is evaluated against a centralized reputation approach and the results demonstrate that it outperforms the latter.

Smart Zero-Touch Management of UAV-Based Edge Network

The next generation of wireless communications networks, namely 6G, will be aimed at realizing a fully connected world, and at providing ubiquitous connectivity to people and objects even in remote areas that are very far from the structured Internet core network. These goals include the definition and the design of intelligent communications environments mainly characterized by pervasive artificial intelligence and large-scale automation. The target of this paper is the design of a management framework for edge networks realized with Flying Ad-Hoc Networks (FANET) consisting of a set of Unmanned Aerial Vehicles (UAVs) to provide a remote geographic area with computing and networking facilities for delay-sensitive applications. To this purpose, each UAV is equipped with a Computing Element (CE) to process jobs received through vertical offloading from ground devices. In addition, horizontal offload among UAVs of the FANET is introduced for load balancing purposes, to guarantee that the FANET computation delay for each received job is minimized and is almost independent of the activity state of the area covered by the UAV receiving that job. The proposed FANET management framework is based on Deep Reinforcement Learning (DRL) to allow zero-touch adaptation to the time-variant activity state of the area covered by each UAV. Numerical results demonstrate the power of the proposed framework and the enhancements achieved with respect to the current literature.

Hierarchical Resource Management Framework and Multi-hop Task Scheduling Decision for Resource-Constrained VEC Networks

Hierarchical Resource Management Framework and Multi-hop Task Scheduling Decision for Resource-Constrained VEC Networks

An automated closed-loop framework to enforce security policies from anomaly detection

Due to the growing complexity and scale of IT systems, there is an increasing need to automate and streamline routine maintenance and security management procedures, to reduce costs and improve productivity. In the case of security incidents, the implementation and application of response actions require significant efforts from operators and developers in translating policies to code. Even if Machine Learning (ML) models are used to find anomalies, they need to be regularly trained/updated to avoid becoming outdated. In an evolving environment, a ML model with outdated training might put at risk the organization it was supposed to defend.To overcome those issues, in this paper we propose an automated closed-loop process with three stages. The first stage focuses on obtaining the Decision Trees (DT) that classify anomalies. In the second stage, DTs are translated into security Policies as Code based on languages recognized by the Policy Engine (PE). In the last stage, the translated security policies feed the Policy Engines that enforce them by converting them into specific instruction sets. We also demonstrate the feasibility of the proposed framework, by presenting an example that encompasses the three stages of the closed-loop process.The proposed framework may integrate a broad spectrum of domains and use cases, being able for instance to support the decide and the act stages of the ETSI Zero-touch Network & Service Management (ZSM) framework.

A New Real Time Energy Efficient Management of Radial Unbalance Distribution Networks Through Integration of Load Shedding and CVR

This paper elucidates a new real time energy management framework for radial active unbalance distribution networks (UDNs) by integrating load shedding and conservation voltage reduction (CVR) techniques. In contrast with the shortsighted real time optimization strategies, the proposed technique accounts offline beneficial aspects in real time optimization platform as time coupled stochastic expressions. Those are further simplified to a mixed integer non-convex programming (MINCP) using merger of Queueing theory and Lyapunov optimization process. To solve the complex MINCP portfolio, a consecutive mixed integer linear programming (c-MILP) based solution method is proposed after adopting necessary linear approximations. After demonstrating on modified IEEE 123 bus test network, it is showed that the proposed real time strategy can provide most energy efficient, secure and reliable operation to the UDNs and can accommodate offline advantageous attributes successfully along with the real time load shedding and CVR constraints. Validating the power flow solutions at OpenDSS platform, it is proved that the proposed c-MILP approach possess fast convergence and provide near optimal power flow solutions. Further investigations certify that presence of residential consumers are more beneficial for the networks as they are more sensitive to voltage.

Intelligent Joint Network Slicing and Routing via GCN-Powered Multi-Task Deep Reinforcement Learning

In 6G mobile systems, network slicing is an emerging technology to support services with distinct requirements by dividing a common infrastructure into multiple logical networks. However, as a network management method, it is difficult for network slicing to achieve a real-time resource allocation to satisfy the stringent requirement of services in 6G networks. This paper introduces a joint network slicing and routing mechanism, which combines the network management and control framework to provide a fine-grained, real-time and dynamic resource allocation. Graph Convolutional Networks (GCN)-powered Multi-Task Deep Reinforcement Learning (DRL) is proposed to solve this complicated resource allocation problem. We first extend the DRL model into multi-task manner, where multiple output branches are matched to joint scheduling resources in every network slice. GCN with differentiable pooling mechanism is integrated into DRL model to capture the topological information from graph-structured network status. We implement our model in the SDN controller and evaluate it with representative topologies. The packet-level experiments show that 1) compared to rule-based and other learning-based methods, GCN-powered multi-task DRL can improve the performance of joint network slicing and routing; 2) our method is robust to diverse network environments; 3) in contrast with other learning-based algorithms, our method achieves a better performance.

A Decentralized Blockchain-Based Trust Management Framework for Vehicular Ad Hoc Networks

Vehicular Ad hoc Networks (VANETs) are one of the pillars of the Internet of Vehicles, they provide plenty of applications ranging from safety to entertainment. Safety applications largely depend on reliable and authentic traffic-related data. However, ensuring the data reliability and authenticity is facing many challenges due mainly to the scalability of VANETs such as the high speed, the long roads, and the open nature of VANETs. This paper addresses these challenges by proposing a decentralized Blockchain-based trust management framework (BC-TMF) aiming to compute trust metrics for vehicles. These trust metrics rely on the authenticity of the messages. Each vehicle assesses the authenticity of the received messages in real-time, calculates a local trust metric for the originator of such messages, then shares it with a miner. Periodically each miner aggregates the received trust metrics into global trust metrics, then packs them in a block. To investigate the efficiency and consistency of the proposed framework, extensive simulations are conducted. The obtained results show that the proposed BC-TMF has an excellent capability in computing accurate trust metrics for vehicles. Besides, it outperforms the existing ones in terms of the accuracy of computed trust metrics, particularly for malicious vehicles.

A Software-Defined Networking Roadside Unit Cloud Resource Management Framework for Vehicle Ad Hoc Networks

Roadside unit (RSU) cloud and its vehicle-to-infrastructure (V2I) connectivity can enable various security, entertainment, and shared mobility applications for vehicles in intelligent transportation systems (ITS) through wireless communications. In this article, the deep programmability of software-defined networking (SDN) is employed to dynamically reconfigure network hosting services and their data forwarding information for effectively meeting the basic shared mobility applications’ needs in vehicle ad hoc networks (VANETs). Multipath is also enabled to forward data flow for balancing network links utilization rate and SDN is thus utilized to achieve the minimum cloud delay with the least number of hosts, which can be summarized as a mixed-integer linear programming (MILP) problem. The joint optimization (JO) algorithm is proposed and in contrast to the two single-objective algorithms which are the delay optimization (DO) algorithm and host optimization (HO) algorithm, respectively. Results show that, for the single-threading instance, the JO and DO algorithms are the same in essence. For the multithreading instance, the JO algorithm generally outperforms the two single-objective optimization algorithms, respectively, under given demands. Furthermore, results also demonstrate that the services should be deployed globally in a distributed manner rather than in the centralized manner for achieving the minimized cloud delay in designing an RSU cloud.

Adaptive Wireless Network Management with Multi-Agent Reinforcement Learning.

Wireless networks are trending towards large scale systems, containing thousands of nodes, with multiple co-existing applications. Congestion is an inevitable consequence of this scale and complexity, which leads to inefficient use of the network capacity. This paper proposes an autonomous and adaptive wireless network management framework, utilising multi-agent deep reinforcement learning, to achieve efficient use of the network. Its novel reward function incorporates application awareness and fairness to address both node and network level objectives. Our experimental results demonstrate the proposed approach’s ability to be optimised for application-specific requirements, while optimising the fairness of the network. The results reveal significant performance benefits in terms of adaptive data rate and an increase in responsiveness compared to a single-agent approach. Some significant qualitative benefits of the multi-agent approach—network size independence, node-led priorities, variable iteration length, and reduced search space—are also presented and discussed.