Energy Efficient Distributed Processing for IoT

The number of connected objects in the Internet of Things (IoT) is growing exponentially. IoT devices are expected to number between 26 billion to 50 billion devices by 2020 and this figure can grow even further due to the production of miniaturised portable devices that are lightweight, energy and cost efficient together with the widespread use of the Internet and the added value organisations and individuals can gain from IoT devices, if their data is processed. These connected objects are expected to be used in multitudes of applications, of which, some are, highly resource intensive such as visual processing services for surveillance based object recognition applications. The sensed data requires processing by the cloud in order to extract knowledge and make decisions accordingly. Given the pervasiveness of future IoT-based visual processing applications, massive amounts of data will be collected due to the nature of multimedia files. Transporting all that collected data to the cloud at the core of the network, is prohibitively costly, in terms of energy consumption. Hence, to tackle the aforementioned challenges, distributed processing is proposed by academia and industry to make use of a large number of devices located in the edge of the network to process some or all of the data before it gets to the cloud. Due to the heterogeneity of the devices in the edge of the network, it is crucial to develop energy efficient models that take care of resource provisioning optimally. The focus in today’s network design and development has shifted towards energy efficiency, due to the rising cost of electricity, resource scarcity and increasing emission of carbon dioxide (CO2). This thesis addresses some of the challenges associated with service placement in a distributed architecture such as the fog. First, a Passive Optical Network (PON) is used to connect IoT devices and to support the fog infrastructure. A metro network is also used to connect to the fog and aggregate traffic from the PON towards the core network. An IP/WDM backbone network is considered to model the core layer and to interconnect the cloud data centres. The entire network was modelled and optimised through Mixed Integer Linear Programming (MILP) and the total end to end power consumption was jointly minimised for processing and networking. Two aspects of service placements were examined: 1) non-splitable services, and 2) splitable services. The results obtained showed that, in the capacitated problem, service splitting introduced power consumption savings of up to 86% compared to 46% with non-splitable services. Moreover, an energy efficient special purposed data centre (SP-DC) was deployed in addition to its general purpose counterpart (GP-DC). The results showed that, for very high demands, power savings of up to 50% could be achieved compared to 30% without SP-DC. The performance of the proposed architecture was further examined by considering additional dimensions to the problem of service placements such as resiliency dimension in terms of 1+1 server protection, in the long term network design problem (un-capacitated) and the impact of inter-service synchronisation overhead on the total number service splits per task

View on 5G Architecture: Version 1.0

The current white paper focuses on the produced results after one year research mainly from 16 projects working on the abovementioned domains. During several months, representatives from these projects have worked together to identify the key findings of their projects and capture the commonalities and also the different approaches and trends. Also they have worked to determine the challenges that remain to be overcome so as to meet the 5G requirements. The goal of 5G Architecture Working Group is to use the results captured in this white paper to assist the participating projects achieve a common reference framework. The work of this working group will continue during the following year so as to capture the latest results to be produced by the projects and further elaborate this reference framework. The 5G networks will be built around people and things and will natively meet the requirements of three groups of use cases: • Massive broadband (xMBB) that delivers gigabytes of bandwidth on demand • Massive machine-type communication (mMTC) that connects billions of sensors and machines • Critical machine-type communication (uMTC) that allows immediate feedback with high reliability and enables for example remote control over robots and autonomous driving. The demand for mobile broadband will continue to increase in the next years, largely driven by the need to deliver ultra-high definition video. However, 5G networks will also be the platform enabling growth in many industries, ranging from the IT industry to the automotive, manufacturing industries entertainment, etc. 5G will enable new applications like for example autonomous driving, remote control of robots and tactile applications, but these also bring a lot of challenges to the network. Some of these are related to provide low latency in the order of few milliseconds and high reliability compared to fixed lines. But the biggest challenge for 5G networks will be that the services to cater for a diverse set of services and their requirements. To achieve this, the goal for 5G networks will be to improve the flexibility in the architecture. The white paper is organized as follows. In section 2 we discuss the key business and technical requirements that drive the evolution of 4G networks into the 5G. In section 3 we provide the key points of the overall 5G architecture where as in section 4 we elaborate on the functional architecture. Different issues related to the physical deployment in the access, metro and core networks of the 5G network are discussed in section 5 while in section 6 we present software network enablers that are expected to play a significant role in the future networks. Section 7 presents potential impacts on standardization and section 8 concludes the white paper

Fluid flow queue models for fixed-mobile network evaluation

A methodology for fast and accurate end-to-end KPI, like throughput and delay, estimation is proposed based on the service-centric traffic flow analysis and the fluid flow queuing model named CURSA-SQ. Mobile network features, like shared medium and mobility, are considered defining the models to be taken into account such as the propagation models and the fluid flow scheduling model. The developed methodology provides accurate computation of these KPIs, while performing orders of magnitude faster than discrete event simulators like ns-3. Finally, this methodology combined to its capacity for performance estimation in MPLS networks enables its application for near real-time converged fixed-mobile networks operation as it is proven in three use case scenarios

Doctor of Philosophy

dissertationThe next generation mobile network (i.e., 5G network) is expected to host emerging use cases that have a wide range of requirements; from Internet of Things (IoT) devices that prefer low-overhead and scalable network to remote machine operation or remote healthcare services that require reliable end-to-end communications. Improving scalability and reliability is among the most important challenges of designing the next generation mobile architecture. The current (4G) mobile core network heavily relies on hardware-based proprietary components. The core networks are expensive and therefore are available in limited locations in the country. This leads to a high end-to-end latency due to the long latency between base stations and the mobile core, and limitations in having innovations and an evolvable network. Moreover, at the protocol level the current mobile network architecture was designed for a limited number of smart-phones streaming a large amount of high quality traffic but not a massive number of low-capability devices sending small and sporadic traffic. This results in high-overhead control and data planes in the mobile core network that are not suitable for a massive number of future Internet-of-Things (IoT) devices. In terms of reliability, network operators already deployed multiple monitoring sys- tems to detect service disruptions and fix problems when they occur. However, detecting all service disruptions is challenging. First, there is a complex relationship between the network status and user-perceived service experience. Second, service disruptions could happen because of reasons that are beyond the network itself. With technology advancements in Software-defined Network (SDN) and Network Func- tion Virtualization (NFV), the next generation mobile network is expected to be NFV-based and deployed on NFV platforms. However, in contrast to telecom-grade hardware with built-in redundancy, commodity off-the-shell (COTS) hardware in NFV platforms often can't be comparable in term of reliability. Availability of Telecom-grade mobile core network hardwares is typically 99.999% (i.e., "five-9s" availability) while most NFV platforms only guarantee "three-9s" availability - orders of magnitude less reliable. Therefore, an NFV-based mobile core network needs extra mechanisms to guarantee its availability. This Ph.D. dissertation focuses on using SDN/NFV, data analytics and distributed system techniques to enhance scalability and reliability of the next generation mobile core network. The dissertation makes the following contributions. First, it presents SMORE, a practical offloading architecture that reduces end-to-end latency and enables new functionalities in mobile networks. It then presents SIMECA, a light-weight and scalable mobile core network designed for a massive number of future IoT devices. Second, it presents ABSENCE, a passive service monitoring system using customer usage and data analytics to detect silent failures in an operational mobile network. Lastly, it presents ECHO, a distributed mobile core network architecture to improve availability of NFV-based mobile core network in public clouds