SDN workload balancing and QoE control in next generation network infrastructures

The increasing demand of bandwidth, low latency and reliability, even in mobile scenarios, has pushed the evolution of the networking technologies to satisfy new requirements of innovative services. Flexible orchestration of network resources is increasingly being investigated by the research community and by the service operator companies as a mean to easily deploy new remunerative services while reducing capital expenditures and operating expenses. In this regard, the Future Internet initiatives are expected to improve state of the art technologies by developing new orchestrating platforms based on the most prominent enabling technologies, namely, Software Defined Network (SDN) orchestrated Network Function Virtualization (NFV) infrastructure. After introducing the fundamental of the Next Generation Network, formalized as the conceptual Future Internet Platform architecture, the reference scenarios and the proposed control frameworks are given. The thesis discusses the design of two resources management framework of such architecture, targeted, respectively, (i) at the balancing of SDN Control traffic at the network core and (ii) at the user Quality of Experience (QoE) evaluation and control at the network edge. Regarding the first framework, to address the issues related with the adoption of a logically centralized but physically distributed SDN control plane, a discrete-time, distributed, non-cooperative load balancing algorithm is proposed, based on game theory and converged to a specific equilibrium known as Wardrop equilibrium. Regarding the QoE framework, a cognitive approach is presented, aimed at controlling the Quality of Experience (QoE) of the end users by closing the loop between the provided QoS and the user experience feedbacks parameters. QoE Management functionalities are aimed at approaching the desired QoE level exploiting a mathematical model and methodology to identify a set of QoE profiles and an optimal and adaptive control strategy based on a Reinforcement Learning algorithm. For both the proposed solutions, simulation and proof-of-concept implementation results are presented and discussed, to highlight the correctness and the effectiveness of the proposed solutions

A Comprehensive Survey on Resource Management in Internet of Things, Journal of Telecommunications and Information Technology, 2020, nr 4

Efficient resource management is a challenging task in distributed systems, such as the Internet of Things, fog, edge, and cloud computing. In this work, we present a broad overview of the Internet of Things ecosystem and of the challenges related to managing its resources. We also investigate the need for efficient resource management and the guidelines given/suggested by Standard Development Organizations. Additionally, this paper contains a comprehensive survey of the individual phases of resource management processes, focusing on resource modeling, resource discovery, resource estimation, and resource allocation approaches based on performance parameters or metrics, as well as on architecture types. This paper presents also the architecture of a generic resource management enabler. Furthermore, we present open issues concerning resource management, pointing out the directions of future research related to the Internet of Thing

SCALING UP TASK EXECUTION ON RESOURCE-CONSTRAINED SYSTEMS

The ubiquity of executing machine learning tasks on embedded systems with constrained resources has made efficient execution of neural networks on these systems under the CPU, memory, and energy constraints increasingly important. Different from high-end computing systems where resources are abundant and reliable, resource-constrained systems only have limited computational capability, limited memory, and limited energy supply. This dissertation focuses on how to take full advantage of the limited resources of these systems in order to improve task execution efficiency from different aspects of the execution pipeline. While the existing literature primarily aims at solving the problem by shrinking the model size according to the resource constraints, this dissertation aims to improve the execution efficiency for a given set of tasks from the following two aspects. Firstly, we propose SmartON, which is the first batteryless active event detection system that considers both the event arrival pattern as well as the harvested energy to determine when the system should wake up and what the duty cycle should be. Secondly, we propose Antler, which exploits the affinity between all pairs of tasks in a multitask inference system to construct a compact graph representation of the task set for a given overall size budget. To achieve the aforementioned algorithmic proposals, we propose the following hardware solutions. One is a controllable capacitor array that can expand the system’s energy storage on-the-fly. The other is a FRAM array that can accommodate multiple neural networks running on one system.Doctor of Philosoph

5G and beyond networks

This chapter investigates the Network Layer aspects that will characterize the merger of the cellular paradigm and the IoT architectures, in the context of the evolution towards 5G-and-beyond, including some promising emerging services as Unmanned Aerial Vehicles or Base Stations, and V2X communications

Engineering Self-Adaptive Applications on Software Defined Infrastructure

Cloud computing is a flexible platform that offers faster innovation, elastic resources, and economies of scale. However, it is challenging to ensure non-functional properties such as performance, cost and security of applications hosted in cloud. Applications should be adaptive to the fluctuating workload to meet the desired performance goals, in one hand, and on the other, operate in an economic manner to reduce the operational cost. Moreover, cloud applications are attractive target of security threats such as distributed denial of service attacks that target the availability of applications and increase the cost. Given such circumstances, it is vital to engineer applications that are able to self-adapt to such volatile conditions. In this thesis, we investigate techniques and mechanisms to engineer model-based application autonomic management systems that strive to meet performance, cost and security objectives of software systems running in cloud. In addition to using the elasticity feature of cloud, our proposed autonomic management systems employ run-time network adaptations using the emerging software defined networking and network function virtualization. We propose a novel approach to self-protecting applications where the application traffic is dynamically managed between public and private cloud depending on the condition of the traffic. Our management approach is able to adapt the bandwidth rates of application traffic to meet performance and cost objectives. Through run-time performance models as well as optimization, the management system maximizes the profit each time the application requires to adapt. Our autonomous management solutions are implemented and evaluated analytically as well as on multiple public and community clouds to demonstrate their applicability and effectiveness in real world environment

An Approach to Guide Users Towards Less Revealing Internet Browsers

When browsing the Internet, HTTP headers enable both clients and servers send extra data in their requests or responses such as the User-Agent string. This string contains information related to the sender’s device, browser, and operating system. Previous research has shown that there are numerous privacy and security risks result from exposing sensitive information in the User-Agent string. For example, it enables device and browser fingerprinting and user tracking and identification. Our large analysis of thousands of User-Agent strings shows that browsers differ tremendously in the amount of information they include in their User-Agent strings. As such, our work aims at guiding users towards using less exposing browsers. In doing so, we propose to assign an exposure score to browsers based on the information they expose and vulnerability records. Thus, our contribution in this work is as follows: first, provide a full implementation that is ready to be deployed and used by users. Second, conduct a user study to identify the effectiveness and limitations of our proposed approach. Our implementation is based on using more than 52 thousand unique browsers. Our performance and validation analysis show that our solution is accurate and efficient. The source code and data set are publicly available and the solution has been deployed

Cyber-Physical Threat Intelligence for Critical Infrastructures Security

Modern critical infrastructures comprise of many interconnected cyber and physical assets, and as such are large scale cyber-physical systems. Hence, the conventional approach of securing these infrastructures by addressing cyber security and physical security separately is no longer effective. Rather more integrated approaches that address the security of cyber and physical assets at the same time are required. This book presents integrated (i.e. cyber and physical) security approaches and technologies for the critical infrastructures that underpin our societies. Specifically, it introduces advanced techniques for threat detection, risk assessment and security information sharing, based on leading edge technologies like machine learning, security knowledge modelling, IoT security and distributed ledger infrastructures. Likewise, it presets how established security technologies like Security Information and Event Management (SIEM), pen-testing, vulnerability assessment and security data analytics can be used in the context of integrated Critical Infrastructure Protection. The novel methods and techniques of the book are exemplified in case studies involving critical infrastructures in four industrial sectors, namely finance, healthcare, energy and communications. The peculiarities of critical infrastructure protection in each one of these sectors is discussed and addressed based on sector-specific solutions. The advent of the fourth industrial revolution (Industry 4.0) is expected to increase the cyber-physical nature of critical infrastructures as well as their interconnection in the scope of sectorial and cross-sector value chains. Therefore, the demand for solutions that foster the interplay between cyber and physical security, and enable Cyber-Physical Threat Intelligence is likely to explode. In this book, we have shed light on the structure of such integrated security systems, as well as on the technologies that will underpin their operation. We hope that Security and Critical Infrastructure Protection stakeholders will find the book useful when planning their future security strategies