Using OSM for real-time redeployment of VNFs based on network status

Στην παρούσα διπλωματική εργασία θα εξετάσουμε την Εικονικοποίηση δικτυακών λειτουργιών (Network Functions Virtualisation - NFV) ως την κατάλληλη αρχιτεκτονική για την υλοποίηση ενός δικτύου κατάλληλου για το Διαδίκτυο των Πραγμάτων (Internet of Things - IoT), το οποίο πρέπει να είναι ευέλικτο και επεκτάσιμο. Πιο συγκεκριμένα, θα επικεντρωθούμε στην αποτελεσματική αξιοποίηση του Open Source MANO (OSM) στην υλοποίηση μιας εφαρμογής που παρακολουθεί την κατάσταση του δικτύου των Εικονικοποιημένων δικτυακών λειτουργιών (Virtual Network Functions – VNFs) και σε περίπτωση κακής κατάστασης του δικτύου (π.χ. συμφόρηση του δικτύου) αναλαμβάνει τη μετακίνηση των επηρεαζόμενων VNFs σε κάποιον άλλο Διαχειριστή Εικονικής Υποδομής (Virtual Infrastructure Manager – VIM), για να αποτραπεί η πτώση στην απόδοση των ενεργών υπηρεσιών.In this thesis we will be examining the Network Functions Virtualisation (NFV) framework as a suitable framework for implementing a network appropriate for Internet of Things (IoT), which needs to be flexible and scalable. More precisely, we will be focusing on how Open Source MANO (OSM) can be efficiently utilized in a solution that monitors the network status of Virtual Network Functions (VNFs) and in case of bad network status (e.g. network congestion) triggers the redeployment of affected VNFs to some other Virtual Infrastructure Manager (VIM) to prevent the underperformance of running services

Online Learning-Assisted VNF Service Chain Scaling with Network Uncertainties

Network function virtualization has emerged as a promising technology to enable rapid network service composition/innovation, energy conservation and cost minimization for network operators. To optimally operate a virtualized network service, it is of key importance to optimally deploy a VNF (virtualized network function) service chain within the provisioning infrastructure (e.g., servers and the network within a cloud datacenter), and dynamically scale it in response to flow traffic changes. Most of the existing work on VNF scaling assume access to precise network bandwidth information for placement decisions, while in reality, network bandwidth typically fluctuates following an unknown pattern and an effective way to adapt to it is to do trials. In this paper, we address dynamic VNF service chain deployment and scaling by a novel combination of an online provisioning algorithm and a multi-armed bandit optimization framework, which exploits online learning of the available bandwidths to enable optimal deployment of a scaled service chain. Specifically, we adopt the online algorithm to minimize the cost for provisioning VNF instances on the go, and a bandit-based online learning algorithm to place the VNF instances which minimizes the congestion in a datacenter network. We demonstrate effectiveness of our algorithms using solid theoretical analysis and trace-driven evaluation.link_to_subscribed_fulltex

Architectures and Algorithms for Cloud-Based Multimedia Conferencing

Multimedia conferencing is the real-time exchange of multimedia content between multiple parties. It is the basis of several applications, such as distance learning, online meetings, and massively multiplayer online games. Cloud-based provisioning of multimedia conferencing has several benefits, like resource efficiency, elasticity, and scalability. However, it remains very challenging. A challenge, for instance, is the lack of holistic architectures which cover both the infrastructure and the platform layers of cloud-based multimedia conferencing applications. Another challenge is the lack of appropriate algorithms for resource allocation in the conferencing cloud to accommodate the fluctuating number of participants, while meeting the required quality of services (QoS). Yet another example is the lack of suitable algorithms for scaling the multimedia conferencing applications in the cloud while meeting both QoS requirements and cost efficiency objective. Unfortunately, the solutions proposed so far do not address these challenges. This thesis focuses on the architectural and algorithmic challenges of cloud-based multimedia conferencing. It proposes architectural components and interfaces for multimedia conferencing application provisioning, covering both the Platform-as-a-Service (PaaS) and the Infrastructure-as-a-Service (IaaS) layers. The proposed interfaces simplify multimedia conference service provisioning for a wide range of application providers. On the algorithmic side, it proposes resource allocation mechanisms that support scalability in terms of the number of participants while meeting the QoS. These mechanisms allocate the actual resources (e.g., CPU, RAM, and storage) in an optimal manner. Besides these mechanisms, it proposes the scalability approaches for cloud-based multimedia conferencing applications. To ensure cost efficiency, these proposed solutions enable fine-grained scalability of the applications with respect to the number of participants while considering the QoS requirements. All algorithmic problems in this thesis are formulated using the Integer Linear Programming (ILP) and heuristics have been designed and validated to solve them