Guest Editorial: Special Section on Data Analytics and Machine Learning for Network and Service Management–Part I
- Nur Zincir-Heywood,
- Giuliano Casale,
- David Carrera,
- Lydia Y. Chen,
- Amogh Dhamdhere,
- Takeru Inoue,
- Hanan Lutfiyya,
- Taghrid Samak
Network and Service analytics can harness the immense stream of operational data from clouds, to services, to social and communication networks. In the era of big data and connected devices of all varieties, analytics and machine learning have found ways ...
Deep Reinforcement Adversarial Learning Against Botnet Evasion Attacks
As cybersecurity detectors increasingly rely on machine learning mechanisms, attacks to these defenses escalate as well. Supervised classifiers are prone to adversarial evasion, and existing countermeasures suffer from many limitations. Most solutions ...
Exploring Network-Wide Flow Data With Flowyager
Many network operations, ranging from attack investigation and mitigation to traffic management, require answering network-wide flow queries in seconds. Although flow records are collected at each router, using available traffic capture utilities, ...
ViCrypt to the Rescue: Real-Time, Machine-Learning-Driven Video-QoE Monitoring for Encrypted Streaming Traffic
Video streaming is the killer application of the Internet today. In this article, we address the problem of real-time, passive Quality-of-Experience (QoE) monitoring of HTTP Adaptive Video Streaming (HAS), from the Internet-Service-Provider (ISP) ...
Collaborative Flow Control in the DARPA Spectrum Collaboration Challenge
- Ruben Mennes,
- Jakob Struye,
- Carlos Donato,
- Miguel Camelo,
- Irfan Jabandžić,
- Spilios Giannoulis,
- Ingrid Moerman,
- Steven Latré
Wireless network technologies are becoming more and more popular. Because of this, important parts of the wireless spectrum become overloaded. Static spectrum allocation, which has been the norm for decades, is not suitable anymore. To maintain the high ...
You Only Run Once: Spark Auto-Tuning From a Single Run
Tuning configurations of Spark jobs is not a trivial task. State-of-the-art auto-tuning systems are based on iteratively running workloads with different configurations. During the optimization process, the relevant features are explored to find good ...
Diminishing Returns and Deep Learning for Adaptive CPU Resource Allocation of Containers
Containers provide a lightweight runtime environment for microservices applications while enabling better server utilization. Automatic optimal allocation of CPU pins to the containers serving specific workloads can help to minimize the completion time of ...
HitAnomaly: Hierarchical Transformers for Anomaly Detection in System Log
Enterprise systems often produce a large volume of logs to record runtime status and events. Anomaly detection from system logs is crucial for service management and system maintenance. Most existing log-based anomaly detection methods use log event <...
Few-Shot Learning and Self-Training for eNodeB Log Analysis for Service-Level Assurance in LTE Networks
With the increasing network topology complexity and continuous evolution of the new wireless technology, it is challenging to address the network service outage with traditional methods. In the long-term evolution (LTE) networks, a large number of base ...
Estimating Pole Capacity From Radio Network Performance Statistics by Supervised Learning
Network dimensioning is a critical task for cellular operators to avoid degraded user experience and unnecessary upgrades of network resources with changing mobile traffic patterns. For this purpose, smart network planning tools require accurate cell and ...
Mobility Management With Transferable Reinforcement Learning Trajectory Prediction
- Zhongliang Zhao,
- Mostafa Karimzadeh,
- Lucas Pacheco,
- Hugo Santos,
- Denis Rosário,
- Torsten Braun,
- Eduardo Cerqueira
Future mobile networks will enable the massive deployment of mobile multimedia applications anytime and anywhere. In this context, mobility management schemes, such as handover and proactive multimedia service migration, will be essential to improve ...
Machine Learning-Based Radio Coverage Prediction in Urban Environments
- Sanaz Mohammadjafari,
- Sophie Roginsky,
- Emir Kavurmacioglu,
- Mucahit Cevik,
- Jonathan Ethier,
- Ayse Basar Bener
AIM: Having a reliable prediction model of radio signal strength is an essential tool for planning and designing a radio network. Given a geographic region, and associated power estimates linked to the transmitter placements, our objective is to develop ...
Machine Learning-Based Recommender Systems to Achieve Self-Coordination Between SON Functions
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 ...
Critical Impact of Social Networks Infodemic on Defeating Coronavirus COVID-19 Pandemic: Twitter-Based Study and Research Directions
News creation and consumption has been changing since the advent of social media. An estimated 2.95 billion people in 2019 used social media worldwide. The widespread of the Coronavirus COVID-19 resulted with a tsunami of social media. Most platforms were ...
Spotting Political Social Bots in Twitter: A Use Case of the 2019 Spanish General Election
- Javier Pastor-Galindo,
- Mattia Zago,
- Pantaleone Nespoli,
- Sergio López Bernal,
- Alberto Huertas Celdrán,
- Manuel Gil Pérez,
- José A. Ruipérez-Valiente,
- Gregorio Martínez Pérez,
- Félix Gómez Mármol
While social media has been proved as an exceptionally useful tool to interact with other people and massively and quickly spread helpful information, its great potential has been ill-intentionally leveraged as well to distort political elections and ...
Data Fusion Oriented Graph Convolution Network Model for Rumor Detection
With the rapid development of Mobile Internet, social media platforms explosively grow and greatly facilitate people to obtain and exchange information. Since any users can post arbitrary information on social media, some people with ulterior motives try ...
Multi-Domain Network Slicing With Latency Equalization
With network slicing, physical networks are partitioned into multiple virtual networks tailored to serve different types of service with their specific requirements. In order to optimize the utilization of network resources for delay-critical applications,...
Network Slice Reconfiguration by Exploiting Deep Reinforcement Learning With Large Action Space
It is widely acknowledged that network slicing can tackle the diverse usage scenarios and connectivity services that the 5G-and-beyond system needs to support. To guarantee performance isolation while maximizing network resource utilization under dynamic ...
<italic>SliceNetVSwitch</italic>: Definition, Design and Implementation of 5G Multi-Tenant Network Slicing in Software Data Paths
Network slicing is a primary Fifth-Generation (5G) mobile networking technology to create virtualised and softwarised logical networks for various vertical businesses with diverging Quality of Service (QoS) requirements. Meanwhile, there is a clear gap in ...
Ensuring Reliability and Low Cost When Using a Parallel VNF Processing Approach to Embed Delay-Constrained Slices
- Nattakorn Promwongsa,
- Mohammad Abu-Lebdeh,
- Somayeh Kianpisheh,
- Fatna Belqasmi,
- Roch H. Glitho,
- Halima Elbiaze,
- Noel Crespi,
- Omar Alfandi
Slices were introduced in 5G to enable the co-existence of applications with different requirements on a single infrastructure. Slices may be delay-constrained for mission-critical applications such as Tactile Internet applications. When delay-constrained ...
Study of Virtual Network Function Placement in 5G Cloud Radio Access Network
5G and beyond need to meet stringent requirements of latency, reliability, and support for heterogeneous devices. However, the existing wireless network architecture is limited to fulfill these constraints. Cloud radio access network, along with network ...
Deploying Virtual Network Functions With Non-Uniform Models in Tree-Structured Networks
Network Function Virtualization (NFV) has promoted the implementation of network functions from expensive hardwares to software middleboxes. These software middleboxes, also called Virtual Network Functions (VNFs), are executed on switch-connected ...
Deadline-Aware SFC Orchestration Under Demand Uncertainty
In network function virtualization, a service function chain (SFC) specifies a sequence of virtual network functions that user traffic has to traverse to realize a network service. The problem of SFC orchestration has been extensively studied in the ...
A Privacy-Preserving Reinforcement Learning Algorithm for Multi-Domain Virtual Network Embedding
The problem of optimally deploying a virtual network onto a substrate physical network is referred to as Virtual Network Embedding (VNE). In general, this embedding is requested by a customer to an Internet Service Provider (ISP), which performs the VNE ...
A Dynamic and Collaborative Multi-Layer Virtual Network Embedding Algorithm in SDN Based on Reinforcement Learning
Most of existing virtual network embedding (VNE) algorithms only consider how to construct virtual networks more efficiently on a physical infrastructure, without considering the possibility that the constructed virtual networks may be further virtualized ...
A Medium-Term Disruption Tolerant SDN for Wireless TCP/IP Networks
A novel framework, Medium-Term Disruption Tolerant Software Defined Network (MDT-SDN), is proposed to handle medium-term disruptions of the order 10 seconds to 6 minutes. Such medium-term disruptions are crucial for the next generation wireless networks ...
Online Sparse BLSTM Models for Resource Usage Prediction in Cloud Datacentres
Real time resource usage prediction is an important part of resource provisioning in a cloud data centre. As cloud workloads vary dynamically, effective resource provisioning requires prediction of future resource usage trends. The problem is highly ...
Intelligent and Flexible Bandwidth Scheduling for Data Transfers in Dedicated High-Performance Networks
High-demanding transfers of extremely large amounts of data have been increasingly supported by the bandwidth reservation services in dedicated high-performance networks (HPNs). To use the bandwidth reservation service, a user needs to initialize a ...
Predictive Cyber Foraging for Visual Cloud Computing in Large-Scale IoT Systems
Cyber foraging has been shown to be especially effective for augmenting low-power Internet-of-Thing (IoT) devices by offloading video processing tasks to nearby edge/cloud computing servers. Factors such as dynamic network conditions, concurrent user ...
Aloe: Fault-Tolerant Network Management and Orchestration Framework for IoT Applications
Internet of Things (IoT) platforms use a large number of low-cost resource constrained devices and generates millions of short-flows. In-network processing is gaining popularity day by day to handle IoT applications and services. However, traditional ...