Ultra-reliable Low-latency, Energy-efficient and Computing-centric Software Data Plane for Network Softwarization

Network softwarization plays a significantly important role in the development and deployment of the latest communication system for 5G and beyond. A more flexible and intelligent network architecture can be enabled to provide support for agile network management, rapid launch of innovative network services with much reduction in Capital Expense (CAPEX) and Operating Expense (OPEX). Despite these benefits, 5G system also raises unprecedented challenges as emerging machine-to-machine and human-to-machine communication use cases require Ultra-Reliable Low Latency Communication (URLLC). According to empirical measurements performed by the author of this dissertation on a practical testbed, State of the Art (STOA) technologies and systems are not able to achieve the one millisecond end-to-end latency requirement of the 5G standard on Commercial Off-The-Shelf (COTS) servers. This dissertation performs a comprehensive introduction to three innovative approaches that can be used to improve different aspects of the current software-driven network data plane. All three approaches are carefully designed, professionally implemented and rigorously evaluated. According to the measurement results, these novel approaches put forward the research in the design and implementation of ultra-reliable low-latency, energy-efficient and computing-first software data plane for 5G communication system and beyond

CyberGuarder: a virtualization security assurance architecture for green cloud computing

Cloud Computing, Green Computing, Virtualization, Virtual Security Appliance, Security Isolation

Lightweight IoT middleware for rapid application development

Sensors connected to the cloud services equipped with data analytics has created a plethora of new type of applications ranging from personal to an industrial level forming to what is known today as Internet of Things (IoT). IoT-based system follows a pattern of data collection, data analytics, automation, and system improvement recommendations. However, most applications would have its own unique requirements in terms of the type of the smart devices, communication technologies as well as its application provisioning service. In order to enable an IoT-based system, various services are commercially available that provide services such as backend-as-a-service (BaaS) and software-as-a-service (SaaS) hosted in the cloud. This, in turn, raises the issues of security and privacy. However there is no plug-and-play IoT middleware framework that could be deployed out of the box for on-premise server. This paper aims at providing a lightweight IoT middleware that can be used to enable IoT applications owned by the individuals or organizations that effectively securing the data on-premise or in remote server. Specifically, the middleware with a standardized application programming interface (API) that could adapt to the application requirements through high level abstraction and interacts with the application service provider is proposed. Each API endpoint would be secured using Access Control List (ACL) and easily integratable with any other modules to ensure the scalability of the system as well as easing system deployment. In addition, this middleware could be deployed in a distributed manner and coordinate among themselves to fulfil the application requirements. A middleware is presented in this paper with GET and POST requests that are lightweight in size with a footprint of less than 1 KB and a round trip time of less than 1 second to facilitate rapid application development by individuals or organizations for securing IoT resources

Bioenergy for climate change mitigation: Scale and sustainability

Many global climate change mitigation pathways presented in IPCC assessment reports rely heavily on the deployment of bioenergy, often used in conjunction with carbon capture and storage. We review the literature on bioenergy use for climate change mitigation, including studies that use top-down integrated assessment models or bottom-up modelling, and studies that do not rely on modelling. We summarize the state of knowledge concerning potential co-benefits and adverse side effects of bioenergy systems and discuss limitations of modelling studies used to analyse consequences of bioenergy expansion. The implications of bioenergy supply on mitigation and other sustainability criteria are context dependent and influenced by feedstock, management regime, climatic region, scale of deployment and how bioenergy alters energy systems and land use. Depending on previous land use, widespread deployment of monoculture plantations may contribute to mitigation but can cause negative impacts across a range of other sustainability criteria. Strategic integration of new biomass supply systems into existing agriculture and forest landscapes may result in less mitigation but can contribute positively to other sustainability objectives. There is considerable variation in evaluations of how sustainability challenges evolve as the scale of bioenergy deployment increases, due to limitations of existing models, and uncertainty over the future context with respect to the many variables that influence alternative uses of biomass and land. Integrative policies, coordinated institutions and improved governance mechanisms to enhance co-benefits and minimize adverse side effects can reduce the risks of large-scale deployment of bioenergy. Further, conservation and efficiency measures for energy, land and biomass can support greater flexibility in achieving climate change mitigation and adaptation

Bioenergy for climate change mitigation : scale and sustainability

Funding Information: The authors are grateful for comments from three reviewers and the editor. The views expressed in this article are those of the authors alone.Peer reviewedPublisher PD

Bioenergy for climate change mitigation: Scale and sustainability

Many global climate change mitigation pathways presented in IPCC assessment reports rely heavily on the deployment of bioenergy, often used in conjunction with carbon capture and storage. We review the literature on bioenergy use for climate change mitigation, including studies that use top-down integrated assessment models or bottom-up modelling, and studies that do not rely on modelling. We summarize the state of knowledge concerning potential co-benefits and adverse side effects of bioenergy systems and discuss limitations of modelling studies used to analyse consequences of bioenergy expansion. The implications of bioenergy supply on mitigation and other sustainability criteria are context dependent and influenced by feedstock, management regime, climatic region, scale of deployment and how bioenergy alters energy systems and land use. Depending on previous land use, widespread deployment of monoculture plantations may contribute to mitigation but can cause negative impacts across a range of other sustainability criteria. Strategic integration of new biomass supply systems into existing agriculture and forest landscapes may result in less mitigation but can contribute positively to other sustainability objectives. There is considerable variation in evaluations of how sustainability challenges evolve as the scale of bioenergy deployment increases, due to limitations of existing models, and uncertainty over the future context with respect to the many variables that influence alternative uses of biomass and land. Integrative policies, coordinated institutions and improved governance mechanisms to enhance co-benefits and minimize adverse side effects can reduce the risks of large-scale deployment of bioenergy. Further, conservation and efficiency measures for energy, land and biomass can support greater flexibility in achieving climate change mitigation and adaptation

Control over the Cloud : Offloading, Elastic Computing, and Predictive Control

The thesis studies the use of cloud native software and platforms to implement critical closed loop control. It considers technologies that provide low latency and reliable wireless communication, in terms of edge clouds and massive MIMO, but also approaches industrial IoT and the services of a distributed cloud, as an extension of commercial-of-the-shelf software and systems.First, the thesis defines the cloud control challenge, as control over the cloud and controller offloading. This is followed by a demonstration of closed loop control, using MPC, running on a testbed representing the distributed cloud.The testbed is implemented using an IoT device, clouds, next generation wireless technology, and a distributed execution platform. Platform details are provided and feasibility of the approach is shown. Evaluation includes relocating an on-line MPC to various locations in the distributed cloud. Offloaded control is examined next, through further evaluation of cloud native software and frameworks. This is followed by three controller designs, tailored for use with the cloud. The first controller solves MPC problems in parallel, to implement a variable horizon controller. The second is a hierarchical design, in which rate switching is used to implement constrained control, with a local and a remote mode. The third design focuses on reliability. Here, the MPC problem is extended to include recovery paths that represent a fallback mode. This is used by a control client if it experiences connectivity issues.An implementation is detailed and examined.In the final part of the thesis, the focus is on latency and congestion. A cloud control client can experience long and variable delays, from network and computations, and used services can become overloaded. These problems are approached by using predicted control inputs, dynamically adjusting the control frequency, and using horizontal scaling of the cloud service. Several examples are shown through simulation and on real clouds, including admitting control clients into a cluster that becomes temporarily overloaded

Towards Mission-Critical Control at the Edge and Over 5G

With the emergence of industrial IoT and cloud computing, and the advent of 5G and edge clouds, there are ambitious expectations on elasticity, economies of scale, and fast time to market for demanding use cases in the next generation of ICT networks. Responsiveness and reliability of wireless communication links and services in the cloud are set to improve significantly as the concept of edge clouds is becoming more prevalent. To enable industrial uptake we must provide cloud capacity in the networks but also a sufficient level of simplicity and self-sustainability in the software platforms. In this paper, we present a research test-bed built to study mission-critical control over the distributed edge cloud. We evaluate system properties using a conventional control application in the form of a Model Predictive Controller. Our cloud platform provides the means to continuously operate our mission-critical application while seamlessly relocating computations across geographically dispersed compute nodes. Through our use of 5G wireless radio, we allow for mobility and reliably provide compute resources with low latency, at the edge. The primary contribution of this paper is a state-of-the art, fully operational test-bed showing the potential for merged IoT, 5G, and cloud. We also provide an evaluation of the system while operating a mission-critical application and provide an outlook on a novel research direction.Comment: June 18th: Upload the final version as submitted to IEEE Services [EDGE] 2018 on May 16th (updated abstract and some wording, results unchanged