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

AI-Driven Resource Allocation in Optical Wireless Communication Systems

Visible light communication (VLC) is a promising solution to satisfy the extreme demands of emerging applications. VLC offers bandwidth that is orders of magnitude higher than what is offered by the radio spectrum, hence making best use of the resources is not a trivial matter. There is a growing interest to make next generation communication networks intelligent using AI based tools to automate the resource management and adapt to variations in the network automatically as opposed to conventional handcrafted schemes based on mathematical models assuming prior knowledge of the network. In this article, a reinforcement learning (RL) scheme is developed to intelligently allocate resources of an optical wireless communication (OWC) system in a HetNet environment. The main goal is to maximise the total reward of the system which is the sum rate of all users. The results of the RL scheme are compared with that of an optimization scheme that is based on Mixed Integer Linear Programming (MILP) model.Comment: 6 pages, 2 Figures, Conferenc

Terabit indoor laser-based wireless communications : LiFi 2.0 for 6G

This paper provides a summary of available technologies required for implementing indoor laser-based wireless networks capable of achieving aggregate data-rates of terabits per second as widely accepted as a sixth generation (6G) key performance indicator. The main focus of this paper is on the technologies supporting the near infrared region of the optical spectrum. The main challenges in the design of the transmitter and receiver systems and communication/networking schemes are identified and new insights are provided. This paper also covers the previous and recent standards as well as industrial applications for optical wireless communications (OWC) and LiFi

Energy-Efficient VM Placement in PON-based Data Center Architectures with Cascaded AWGRs

Data centers based on Passive Optical Networks (PONs) can offer scalability, low cost and high energy-efficiency. Application in data centers can use Virtual Machines (VMs) to provide efficient utilization of the physical resources. This paper investigates the impact of VM placement on the energy-efficiency in a PON-based data center architecture that utilizes cascaded Arrayed Waveguide Grating Routers (AWGRs). In this paper, we develop a Mixed Integer Linear Programming (MILP) optimization model to optimize the VM placement in the proposed PON-based data center architecture. This optimization aims to minimize the power consumption of the networking and computing by placing the VMs and their demands in the optimum number of resources (i.e., servers and networking devices) in the data center. To date, we consider three objective functions in our optimization framework: 1) an objective function that serves the VM requests randomly, 2) an objective function that only minimizes the processing power consumption, and 3) an objective function that jointly minimizes processing and networking power consumption. The results showed that the total power consumption can be reduced by up to 50% when performing the joint minimization of processing and networking power consumption compared to the random VM allocation approach. In addition, a reduction in the networking power consumption by up to 74% can be achieved when performing joint minimization of processing and networking power consumption compared to considering the minimization of the processing power consumption only.</p

Optimized Passive Optical Networks with Cascaded-AWGRs for Data Centers

The use of Passive Optical Networks (PONs) in modern and future data centers can provide energy efficiency, high capacity, low cost, scalability, and elasticity. This paper introduces a passive optical network design with 2-tier cascaded Arrayed Waveguide Grating Routers (AWGRs) to connect groups of racks (i.e. cells) within a data center. This design employs a Software-Defined Networking (SDN) controller to manage the routing and assignment of the networking resource while introducing multiple paths between any two cells to improve routing, load balancing and resilience. We provide benchmarking results for the power consumption to compare the energy efficiency of this design to state-of-the-art data centers. The results indicate that the cascaded AWGRs architecture can achieve up to 43% saving in the networking power consumption compared to Fat-Tree data center architecture

Energy Efficient Placement of ML-Based Services in IoT Networks

The Internet of Things (IoT) is gaining momentum in its quest to bridge the gap between the physical and the digital world. The main goal of the IoT is the creation of smart environments and self-aware things that help to facilitate a variety of services such as smart transport, climate monitoring, e-health, etc. Huge volumes of data are expected to be collected by the connected sensors/things, which in traditional cases are processed centrally by large data centers in the core network that will inevitably lead to excessive transportation power consumption as well as added latency overheads. Instead, fog computing has been proposed by researchers from industry and academia to extend the capability of the cloud right to the point where the data is collected at the sensing layer. This way, primitive tasks that can be hosted in IoT sensors do not need to be sent all the way to the cloud for processing. In this paper we propose energy efficient embedding of machine learning (ML) models over a cloud-fog network using a Mixed Integer Linear Programming (MILP) optimization model. We exploit virtualization in our framework to provide service abstraction of Deep Neural Networks (DNN) layers that can be composed into a set of VMs interconnected by virtual links. We constrain the number of VMs that can be processed at the IoT layer and study the impact on the performance of the cloud fog approach

Terabit Indoor Laser-Based Wireless Communications: Lifi 2.0 For 6G

International audienceThis article introduces the general concepts of light fidelity (LiFi) 2.0 for sixth generation (6G) of wireless networks that will be based on indoor laser-based wireless networks capable of achieving aggregate data-rates of terabits per second as widely accepted as a 6G key performance indicator. The main focus of this article is on the technologies supporting the near infrared region of the optical spectrum. The main challenges in the design of the transmitter and receiver systems and communication/networking schemes are identified and new insights are provided. This article also covers the previous and recent standards as well as industrial applications for optical wireless communications (OWC) and LiFi

White paper on 6G networking

This white paper is one of the twelve new themed 6G White Papers led by the 6G Flagship program. It involved the participation of more than 50 experts and enthusiasts of future 6G technologies. In this white paper, we intend to shed light on advanced features relevant to networking that would shape the evolution beyond 5G, ultimately leading to the 6G mobile system