38 research outputs found
Cognitive Software Defined Networking and Network Function Virtualization and Applications
The emergence of Software-Defined Networking (SDN) and Network Function Virtualization (NFV) has revolutionized the Internet. Using SDN, network devices can be controlled from a centralized, programmable control plane that is decoupled from their data plane, whereas with NFV, network functions (such as network address translation, firewall, and intrusion detection) can be virtualized instead of being implemented on proprietary hardware. In addition, Artificial Intelligence (AI) and Machine Learning (ML) techniques will be key to automating network operations and enhancing customer service. Many of the challenges behind SDN and NFV are currently being investigated in several projects all over the world using AI and ML techniques, such as AI- and software-based networking, autonomic networking, and policy-based network management. Contributions to this Special Issue come from the above areas of research. Following a rigorous review process, four excellent articles were accepted that address and go beyond many of the challenges mentioned above
Towards a Blockchain Assisted Patient Owned System for Electronic Health Records
Security and privacy of patients’ data is a major concern in the healthcare industry. In this paper, we propose a system that activates robust security and privacy of patients’ medical records as well as enables interoperability and data exchange between the different healthcare providers. The work proposes the shift from patient’s electronic health records being managed and controlled by the healthcare industry to a patient-centric application where patients are in control of their data. The aim of this research is to build an Electronic Healthcare Record (EHR) system that is layered on the Ethereum blockchain platform and smart contract in order to eliminate the need for third-party systems. With this system, the healthcare provider can search for patient’s data and request the patients’ consent to access it. Patients manage their data which enables an expedited data exchange across EHR systems. Each patient’s data are stored on the peer-to-peer node ledger. The proposed patient-centric EHR platform is cross-platform compliant, as it can be accessed via personal computers and mobile devices and facilitates interoperability across healthcare providers as patients’ medical records are gathered from different healthcare providers and stored in a unified format. The proposed framework is tested on a private Ethereum network using Ganache. The results show the effectiveness of the system with respect to security, privacy, performance and interoperability
Cross-Atlantic Experiments on EU-US Test-beds
Today, there are a number of real testbeds worldwide among which Fed4Fire testbeds are prominent in the EU, while POWDER and COSMOS are prominent in the US. This paper aims to validate inter-testbed experiments between the EU and the US by connecting a number of Fed4Fire and US testbeds as part of an NGIAtlantic project. The goal is to compare the hop count, the topology formed, the maximum bandwidth permitted, and the loss and jitter that occurred between different testbeds. Additionally, Software Defined Networking (SDN) experiments between EU and US testbeds are conducted, and an edge-computing use case is developed and tested
Dynamic Prioritization and Adaptive Scheduling using Deep Deterministic Policy Gradient for Deploying Microservice-based VNFs
The Network Function Virtualization (NFV)-Resource Allocation (RA) problem is
NP-Hard. Traditional deployment methods revealed the existence of a starvation
problem, which the researchers failed to recognize. Basically, starvation here,
means the longer waiting times and eventual rejection of low-priority services
due to a 'time out'. The contribution of this work is threefold: a) explain the
existence of the starvation problem in the existing methods and their
drawbacks, b) introduce 'Adaptive Scheduling' (AdSch) which is an 'intelligent
scheduling' scheme using a three-factor approach (priority, threshold waiting
time, and reliability), which proves to be more reasonable than traditional
methods solely based on priority, and c) a 'Dynamic Prioritization' (DyPr),
allocation method is also proposed for unseen services and the importance of
macro- and micro-level priority. We presented a zero-touch solution using Deep
Deterministic Policy Gradient (DDPG) for adaptive scheduling and an
online-Ridge Regression (RR) model for dynamic prioritization. The DDPG
successfully identified the 'Beneficial and Starving' services, efficiently
deploying twice as many low-priority services as others, reducing the
starvation problem. Our online-RR model learns the pattern in less than 100
transitions, and the prediction model has an accuracy rate of more than 80%
An Overview on Application of Machine Learning Techniques in Optical Networks
Today's telecommunication networks have become sources of enormous amounts of
widely heterogeneous data. This information can be retrieved from network
traffic traces, network alarms, signal quality indicators, users' behavioral
data, etc. Advanced mathematical tools are required to extract meaningful
information from these data and take decisions pertaining to the proper
functioning of the networks from the network-generated data. Among these
mathematical tools, Machine Learning (ML) is regarded as one of the most
promising methodological approaches to perform network-data analysis and enable
automated network self-configuration and fault management. The adoption of ML
techniques in the field of optical communication networks is motivated by the
unprecedented growth of network complexity faced by optical networks in the
last few years. Such complexity increase is due to the introduction of a huge
number of adjustable and interdependent system parameters (e.g., routing
configurations, modulation format, symbol rate, coding schemes, etc.) that are
enabled by the usage of coherent transmission/reception technologies, advanced
digital signal processing and compensation of nonlinear effects in optical
fiber propagation. In this paper we provide an overview of the application of
ML to optical communications and networking. We classify and survey relevant
literature dealing with the topic, and we also provide an introductory tutorial
on ML for researchers and practitioners interested in this field. Although a
good number of research papers have recently appeared, the application of ML to
optical networks is still in its infancy: to stimulate further work in this
area, we conclude the paper proposing new possible research directions
Automatic configuration of OpenFlow in wireless mobile ad hoc networks
A Mobile wireless Ad hoc NETwork (MANET) is a decentralized wireless network in which mobile wireless nodes either directly communicate with each other or communicate via other wireless nodes in the network. In addition, OpenFlow has disruptive potential in designing a flexible programmable network which can foster innovation, reduce complexity and deliver right economics. In recent years, there are significant interests from research communities to deploy OpenFlow in MANETs. This paper proposes a configuration method with which OpenFlow can be deployed automatically in a MANET without any manual configuration. The proposed configuration method is tested in an emulated MANET created on the Fed4FIRE testbed using Mininet-WiFi (an emulator for wireless software-defined wireless networks). Experimentation includes automatic configuration of OpenFlow in linear, sparse, and dense mobile ad hoc networks. Results show the effectiveness of the method in configuring OpenFlow in wireless mobile ad hoc networks
Demonstrating Configuration of Software Defined Networking in Real Wireless Testbeds
Currently, several wireless testbeds are available to test networking solutions including Fed4Fire testbeds such as w-ilab. t and CityLab in the EU, and POWDER and COSMOS in the US. In this demonstration, we use the w-ilab.t testbed to set up a wireless ad-hoc Software-Defined Network (SDN). OpenFlow is used as an SDN protocol and is deployed using a grid wireless ad-hoc topology in w-ilab.t. In this paper, we demonstrate: (1) the configuration of a wireless ad-hoc network based on w-ilab.t and (2) the automatic deployment of OpenFlow in an ad-hoc wireless network where some wireless nodes are not directly connected to the controller. The configuration of OpenFlow is shown using the Fed4Fire GUI (Graphical User Interface). The automatic configuration time of OpenFlow is calculated during the demonstration. Further, data traffic is transmitted from each wireless device to another device to show that an OpenFlow network is set up correctly on the w-ilab.t testbed
Future Wireless Networking Experiments Escaping Simulations
In computer networking, simulations are widely used to test and analyse new protocols and ideas. Currently, there are a number of open real testbeds available to test the new protocols. In the EU, for example, there are Fed4Fire testbeds, while in the US, there are POWDER and COSMOS testbeds. Several other countries, including Japan, Brazil, India, and China, have also developed next-generation testbeds. Compared to simulations, these testbeds offer a more realistic way to test protocols and prototypes. In this paper, we examine some available wireless testbeds from the EU and the US, which are part of an open-call EU project under the NGIAtlantic H2020 initiative to conduct Software-Defined Networking (SDN) experiments on intelligent Internet of Things (IoT) networks. Furthermore, the paper presents benchmarking results and failure recovery results from each of the considered testbeds using a variety of wireless network topologies. The paper compares the testbeds based on throughput, latency, jitter, resources available, and failure recovery time, by sending different types of traffic. The results demonstrate the feasibility of performing wireless experiments on different testbeds in the US and the EU. Further, issues faced during experimentation on EU and US testbeds are also reported