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"NETWORK SERVICE"
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Machine Learning-Assisted Closed-Control Loops for Beyond 5G Multi-Domain Zero-Touch Networks
End-to-End (E2E) services in beyond 5G (B5G) networks are expected to be built upon resources and services distributed in multi-domain, multi-technology environments. In such scenarios, key challenges around multi-domain management and collaboration need to be tackled. ETSI Zero-touch network and Service Management (ZSM) architectural framework provides the structure and methods for effectively delivering E2E network services. ZSM pursues cross-domain automation with minimum human intervention through two main enablers: Closed Control Loop (CCL) and Artificial Intelligence (AI). In this work, we propose a multi-domain ZSM-based architecture aiming at B5G scenarios where several per-domain CCLs leverage Machine Learning (ML) methods to collaborate in E2E service management tasks. We instantiate the architecture in the use case scenario of multi-domain automated healing of Dynamic Adaptive Streaming over HTTP (DASH) video services. We present two ML-assisted techniques, first to estimate a Service Level Agreement (SLA) violation through a Edge-based Quality of Experience (QoE) Probe, and second to identify the root cause at the core transport network. Results from the experimental evaluation in an emulation environment using real mobile network traces point to the potential benefits of applying ML techniques for QoS-to-QoE estimation at Multi-Access Edge Computing facilities and correlation to faulty transport network links. Altogether, the work contributes towards a vision of ML-based sandbox environments in the spirit of E2E service and network digital twins towards the realization of automated, multi-domain CCLs for B5G.
Journal Article
A service network perspective to evaluate service matching in early design
Purpose
Service matching is defined in this paper as the process of combining a new service with one or more existing services. A recurring problem for service designer is to match new services with existing ones. This process may be seen as the fundamental action for the development of a service network. The purpose of this paper is to evaluate the consequences that may follow from service matching.
Design/methodology/approach
Through an analogy with living organisms in natural ecosystems, the service relationship deployment (SRD) allows the investigation of the possible relationships between matched services.
Findings
This paper presents a new method, named SRD, developed to support the process of service matching in the early design phases of a new service. The description of the method is supported by some practical examples.
Originality/value
The focus of the scientific community on the problem of matching new services with existing ones, is very limited. This paper proposes a new methodology to address this issue.
Journal Article
Deep Learning-Based Multi-Domain Framework for End-to-End Services in 5G Networks
Over the past few years, network slicing has emerged as a pivotal component within the realm of 5G technology. It plays a critical role in effectively delineating network services based on a myriad of performance and operational requirements, all of which draw from a shared pool of common resources. The core objective of 5G technology is to facilitate simultaneous network slicing, thereby enabling the creation of multiple distinct end-to-end networks. This multiplicity of networks serves the paramount purpose of ensuring that the traffic within one network slice does not impede or adversely affect the traffic within another. Therefore, this paper proposes a Deep learning-based Multi Domain framework for end-to-end network slicing in traffic-aware prediction. The proposed method uses Deep Reinforcement Learning (DRL) for in-depth resource allocation analysis and improves the Quality of Service (QOS). The DRL-based Multi-domain framework provides traffic-aware prediction and enhances flexibility. The study results demonstrate that the suggested approach outperforms conventional, heuristic, and randomized methods and enhances resource use while maintaining QoS.
Journal Article
Routing and switching essentials v6. Companion guide
This course describes the architecture, components, and operations of routers and switches in a small network. You learn how to configure a router and a switch for basic functionality. This companion guide is designed as a portable desk reference to use anytime, anywhere to reinforce the material from the course and organise your time.
Book
The Continuous-Time Service Network Design Problem
Consolidation carriers transport shipments that are small relative to trailer capacity. To be cost effective, the carrier must consolidate shipments, which requires coordinating their paths in both space and time; i.e., the carrier must solve a
service network design
problem. Most service network design models rely on discretization of time—i.e., instead of determining the exact time at which a dispatch should occur, the model determines a time interval during which a dispatch should occur. While the use of time discretization is widespread in service network design models, a fundamental question related to its use has never been answered:
Is it possible to produce an optimal continuous-time solution without explicitly modeling each point in time
? We answer this question in the affirmative. We develop an iterative refinement algorithm using partially time-expanded networks that solves continuous-time service network design problems. An extensive computational study demonstrates that the algorithm not only is of theoretical interest but also performs well in practice.
Journal Article
Adaptive Machine Learning Based Distributed Denial-of-Services Attacks Detection and Mitigation System for SDN-Enabled IoT
The development of smart network infrastructure of the Internet of Things (IoT) faces the immense threat of sophisticated Distributed Denial-of-Services (DDoS) security attacks. The existing network security solutions of enterprise networks are significantly expensive and unscalable for IoT. The integration of recently developed Software Defined Networking (SDN) reduces a significant amount of computational overhead for IoT network devices and enables additional security measurements. At the prelude stage of SDN-enabled IoT network infrastructure, the sampling based security approach currently results in low accuracy and low DDoS attack detection. In this paper, we propose an Adaptive Machine Learning based SDN-enabled Distributed Denial-of-Services attacks Detection and Mitigation (AMLSDM) framework. The proposed AMLSDM framework develops an SDN-enabled security mechanism for IoT devices with the support of an adaptive machine learning classification model to achieve the successful detection and mitigation of DDoS attacks. The proposed framework utilizes machine learning algorithms in an adaptive multilayered feed-forwarding scheme to successfully detect the DDoS attacks by examining the static features of the inspected network traffic. In the proposed adaptive multilayered feed-forwarding framework, the first layer utilizes Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), k-Nearest Neighbor (kNN), and Logistic Regression (LR) classifiers to build a model for detecting DDoS attacks from the training and testing environment-specific datasets. The output of the first layer passes to an Ensemble Voting (EV) algorithm, which accumulates the performance of the first layer classifiers. In the third layer, the adaptive frameworks measures the real-time live network traffic to detect the DDoS attacks in the network traffic. The proposed framework utilizes a remote SDN controller to mitigate the detected DDoS attacks over Open Flow (OF) switches and reconfigures the network resources for legitimate network hosts. The experimental results show the better performance of the proposed framework as compared to existing state-of-the art solutions in terms of higher accuracy of DDoS detection and low false alarm rate.
Journal Article
Co-Creating Value through Agents Interaction within Service Network
Purpose: The purpose of this paper is to give further understanding on value co-creation
mechanisms in B-to-B service network by reinforcing the processes, the relationships, and the
influences of other agents where Collaborative Transportation Management (CTM) forms
might be best employed.
Design/methodology/approach: In order to model the interactions among agents in the
collaboration processes and the value co-creation processes, this research used three
collaboration cases in Indonesia. The agent-based simulation was used to capture both the
collaboration process and the value co-creation process of the three collaboration cases.
Findings: The interactions among the agents both inside and outside their collaboration
environments determined agent’s role as a value co-creator. The willingness of an agent to
accept the opinion of another agent determined the degree of their willingness to co-operate
and to change their strategies, and perceptions. Therefore, interaction among agents
influenced the size of the value obtained by them in each collaboration process.
Research limitations/implications: The findings of the simulations subject to assumptions
based on the collaboration cases. Further research is related to how to encourage agents to
co-operate and adjust their perceptions.
Practical implications: It is crucial for the practitioners to interact with another agent both
inside and outside their collaboration environment. The opinions of another agent inside the
collaboration environment also need to be considered.
Originality/value: This research is derived from its emphasis on how a value is co-created by
reinforcing both the collaborative processes and the interactions among agents as well as on how
CTM might be best employed.
Journal Article