Dynamic control of NFV forwarding graphs with end-to-end deadline constraints

There is a strong industrial drive to use cloud computing technologies and concepts for providing timing sensitive services in the networking domain since it would provide the means to share the physical resources among multiple users and thus increase the elasticity and reduce the costs. In this work, we develop a mathematical model for user-stateless virtual network functions forming a forwarding graph. The model captures uncertainties of the performance of these virtual resources as well as the time-overhead needed to instantiate them. The model is used to derive a service controller for horizontal scaling of the virtual resources as well as an admission controller that guarantees that packets exiting the forwarding graph meet their end-to-end deadline. The Automatic Service and Admission Controller (AutoSAC) developed in this work uses feedback and feedforward making it robust against uncertainties of the underlying infrastructure. Also, it has a fast reaction time to changes in the input

Feedback for increased robustness of forwarding graphs in the cloud

Cloud computing technology provides the means to share physical resources among multiple users and data center tenants by exposing them as virtual resources. There is a strong industrial drive to use similar technology and concepts to provide timing sensitive services. One such domain is a chain of connected virtual network functions. This allows the capacity of each function to be scaled up and down by adding or removing virtual resources. In this work, we develop a model of such service chain and pose the dynamic allocation of resources as an optimization problem. We design and present a set of strategies to allow virtual network nodes to be controlled in an optimal fashion subject to latency and buffer constraints. Furthermore, we derive a feedback-law for dynamically adjusting the amount of resources given to each functions in order to ensure that the system remains in the desired state even if there are modeling errors or for a stochastic input

A Study on Efficient Service Function Chain Placement in Network Function Virtualization Environment

電気通信大学202

Advanced SDN-Based QoS and Security Solutions for Heterogeneous Networks

This thesis tries to study how SDN can be employed in order to support Quality of Service and how the support of this functionality is fundamental for today networks. Considering, not only the present networks, but also the next generation ones, the importance of the SDN paradigm become manifest as the use of satellite networks, which can be useful considering their broadcasting capabilities. For these reasons, this research focuses its attention on satellite - terrestrial networks and in particular on the use of SDN inside this environment. An important fact to be taken into account is that the growing of the information technologies has pave the way for new possible threats. This research study tries to cover also this problem considering how SDN can be employed for the detection of past and future malware inside networks

Probabilistic QoS-aware Placement of VNF chains at the Edge

Deploying IoT-enabled Virtual Network Function (VNF) chains to Cloud-Edge infrastructures requires determining a placement for each VNF that satisfies all set deployment requirements as well as a software-defined routing of traffic flows between consecutive functions that meets all set communication requirements. In this article, we present a declarative solution, EdgeUsher, to the problem of how to best place VNF chains to Cloud-Edge infrastructures. EdgeUsher can determine all eligible placements for a set of VNF chains to a Cloud-Edge infrastructure so to satisfy all of their hardware, IoT, security, bandwidth, and latency requirements. It exploits probability distributions to model the dynamic variations in the available Cloud-Edge infrastructure, and to assess output eligible placements against those variations

Computing on the Edge of the Network

Um Systeme der fünften Generation zellularer Kommunikationsnetze (5G) zu ermöglichen, sind Energie effiziente Architekturen erforderlich, die eine zuverlässige Serviceplattform für die Bereitstellung von 5G-Diensten und darüber hinaus bieten können. Device Enhanced Edge Computing ist eine Ableitung des Multi-Access Edge Computing (MEC), das Rechen- und Speicherressourcen direkt auf den Endgeräten bereitstellt. Die Bedeutung dieses Konzepts wird durch die steigenden Anforderungen von rechenintensiven Anwendungen mit extrem niedriger Latenzzeit belegt, die den MEC-Server allein und den drahtlosen Kanal überfordern. Diese Dissertation stellt ein Berechnungs-Auslagerungsframework mit Berücksichtigung von Energie, Mobilität und Anreizen in einem gerätegestützten MEC-System mit mehreren Benutzern und mehreren Aufgaben vor, das die gegenseitige Abhängigkeit der Aufgaben sowie die Latenzanforderungen der Anwendungen berücksichtigt.To enable fifth generation cellular communication network (5G) systems, energy efficient architectures are required that can provide a reliable service platform for the delivery of 5G services and beyond. Device Enhanced Edge Computing is a derivative of Multi-Access Edge Computing (MEC), which provides computing and storage resources directly on the end devices. The importance of this concept is evidenced by the increasing demands of ultra-low latency computationally intensive applications that overwhelm the MEC server alone and the wireless channel. This dissertation presents a computational offloading framework considering energy, mobility and incentives in a multi-user, multi-task device-based MEC system that takes into account task interdependence and application latency requirements

Resource Management in Multi-Access Edge Computing (MEC)

This PhD thesis investigates the effective ways of managing the resources of a Multi-Access Edge Computing Platform (MEC) in 5th Generation Mobile Communication (5G) networks. The main characteristics of MEC include distributed nature, proximity to users, and high availability. Based on these key features, solutions have been proposed for effective resource management. In this research, two aspects of resource management in MEC have been addressed. They are the computational resource and the caching resource which corresponds to the services provided by the MEC. MEC is a new 5G enabling technology proposed to reduce latency by bringing cloud computing capability closer to end-user Internet of Things (IoT) and mobile devices. MEC would support latency-critical user applications such as driverless cars and e-health. These applications will depend on resources and services provided by the MEC. However, MEC has limited computational and storage resources compared to the cloud. Therefore, it is important to ensure a reliable MEC network communication during resource provisioning by eradicating the chances of deadlock. Deadlock may occur due to a huge number of devices contending for a limited amount of resources if adequate measures are not put in place. It is crucial to eradicate deadlock while scheduling and provisioning resources on MEC to achieve a highly reliable and readily available system to support latency-critical applications. In this research, a deadlock avoidance resource provisioning algorithm has been proposed for industrial IoT devices using MEC platforms to ensure higher reliability of network interactions. The proposed scheme incorporates Banker’s resource-request algorithm using Software Defined Networking (SDN) to reduce communication overhead. Simulation and experimental results have shown that system deadlock can be prevented by applying the proposed algorithm which ultimately leads to a more reliable network interaction between mobile stations and MEC platforms. Additionally, this research explores the use of MEC as a caching platform as it is proclaimed as a key technology for reducing service processing delays in 5G networks. Caching on MEC decreases service latency and improve data content access by allowing direct content delivery through the edge without fetching data from the remote server. Caching on MEC is also deemed as an effective approach that guarantees more reachability due to proximity to endusers. In this regard, a novel hybrid content caching algorithm has been proposed for MEC platforms to increase their caching efficiency. The proposed algorithm is a unification of a modified Belady’s algorithm and a distributed cooperative caching algorithm to improve data access while reducing latency. A polynomial fit algorithm with Lagrange interpolation is employed to predict future request references for Belady’s algorithm. Experimental results show that the proposed algorithm obtains 4% more cache hits due to its selective caching approach when compared with case study algorithms. Results also show that the use of a cooperative algorithm can improve the total cache hits up to 80%. Furthermore, this thesis has also explored another predictive caching scheme to further improve caching efficiency. The motivation was to investigate another predictive caching approach as an improvement to the formal. A Predictive Collaborative Replacement (PCR) caching framework has been proposed as a result which consists of three schemes. Each of the schemes addresses a particular problem. The proactive predictive scheme has been proposed to address the problem of continuous change in cache popularity trends. The collaborative scheme addresses the problem of cache redundancy in the collaborative space. Finally, the replacement scheme is a solution to evict cold cache blocks and increase hit ratio. Simulation experiment has shown that the replacement scheme achieves 3% more cache hits than existing replacement algorithms such as Least Recently Used, Multi Queue and Frequency-based replacement. PCR algorithm has been tested using a real dataset (MovieLens20M dataset) and compared with an existing contemporary predictive algorithm. Results show that PCR performs better with a 25% increase in hit ratio and a 10% CPU utilization overhead

Service-centric networking

This chapter introduces a new paradigm for service centric networking. Building upon recent proposals in the area of information centric networking, a similar treatment of services – where networked software functions, rather than content, are dynamically deployed, replicated and invoked – is discussed. Service-centric networking provides the mechanisms required to deploy replicated service instances across highly distributed networked cloud infrastructures and to route client requests to the closest instance while providing more efficient network infrastructure usage, improved QoS and new business opportunities for application and service providers. </jats:p