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Software Defined Networking (SDN) is a widely deployed technology enabling the agile and flexible management of networks and services. This paradigm represents an appropriate candidate to address the dynamic and secure management of large…
Currently, Machine Learning (ML) is becoming ubiquitous in everyday life. Deep Learning (DL) is already present in many applications ranging from computer vision for medicine to autonomous driving of modern cars as well as other sectors in…
Software Defined Networking (SDN) has gained huge popularity in replacing traditional network by offering flexible and dynamic network management. It has drawn significant attention of the researchers from both academia and industries.…
Software-Defined Networking (SDN) separates the network control plane and data plane, which provides a network-wide view with centralized control (in the control plane) and programmable network configuration for data plane injected by SDN…
In the future, it is anticipated that software-defined networking (SDN) will become the preferred platform for deploying diverse networks. Compared to traditional networks, SDN separates the control and data planes for efficient domain-wide…
Mobile Ad Hoc Networks (MANETs) and Internet of Things (IoT) networks operate in decentralized and dynamic environments, making them ideal for scenarios lacking traditional infrastructure. However, these networks face challenges such as…
Nowadays, Software Defined Network (SDN) architectures and applications are revolutionizing the way wired networks are built and operate. However, little is known about the potential of this disruptive technology in wireless mobile…
Machine learning applications are increasingly deployed not only to serve predictions using static models, but also as tightly-integrated components of feedback loops involving dynamic, real-time decision making. These applications pose a…
Although distributed machine learning has opened up many new and exciting research frontiers, fragmentation of models and data across different machines, nodes, and sites still results in considerable communication overhead, impeding…
Deep Neural Networks (DNNs) are universal function approximators providing state-of- the-art solutions on wide range of applications. Common perceptual tasks such as speech recognition, image classification, and object tracking are now…
Software Defined Networking (SDN) offers a flexible and scalable architecture that abstracts decision making away from individual devices and provides a programmable network platform. However, implementing a centralized SDN architecture…
Availability of labelled data is the major obstacle to the deployment of deep learning algorithms for computer vision tasks in new domains. The fact that many frameworks adopted to solve different tasks share the same architecture suggests…
Software-defined networking offers numerous benefits against the legacy networking systems through simplifying the process of network management and reducing the cost of network configuration. Currently, the management of failures in the…
A key functionality of emerging connected autonomous systems such as smart transportation systems, smart cities, and the industrial Internet-of-Things, is the ability to process and learn from data collected at different physical locations.…
Software defined networking (SDN) has emerged as a promising paradigm for making the control of communication networks flexible. SDN separates the data packet forwarding plane, i.e., the data plane, from the control plane and employs a…
Cooperative decentralized learning relies on direct information exchange between communicating agents, each with access to locally available datasets. The goal is to agree on model parameters that are optimal over all data. However, sharing…
The emerging machine learning paradigm of decentralized federated learning (DFL) has the promise of greatly boosting the deployment of artificial intelligence (AI) by directly learning across distributed agents without centralized…
This thesis is concerned with distributed control and coordination of networks consisting of multiple, potentially mobile, agents. This is motivated mainly by the emergence of large scale networks characterized by the lack of centralized…
To support the needs of ever-growing cloud-based services, the number of servers and network devices in data centers is increasing exponentially, which in turn results in high complexities and difficulties in network optimization. To…
We describe two applications of machine learning in the context of IP/Optical networks. The first one allows agile management of resources at a core IP/Optical network by using machine learning for short-term and long-term prediction of…