Leveraging NFV heterogeneity at the network edge

With network function virtualisation (NFV) and network programmability, network functions (NFs) such as firewalls, traffic load balancers, content filters, and intrusion detection systems (IDS) are virtualized and either instantiated on user space hosts using virtual machines (VMs), lightweight containers, or in the network data plane using programmable switching technology such as P4 or offloaded onto Smart network interface cards (NICs) – often chained together to create a service function chain (SFC), based on defined service level agreement (SLA). The need to leverage heterogeneous programmable platforms to support the in-network acceleration of functions keeps growing as emerging use cases come with peculiar requirements. This thesis identifies various heterogeneous frameworks for deploying virtual network functions that network operators can leverage in service provider networks. A novel taxonomy that provides network operators and the wider research community valuable insights is proposed. The thesis presents the performance gains obtained from using heterogeneous frameworks for deploying virtual network functions using real testbeds. In addition, this thesis investigates the optimal placement of vNFs over the distributed edge network while considering the heterogeneity of packet processing elements. In particular, the work questions the status quo of how vNFs are currently being deployed, i.e., the lack of frameworks to support the seamless deployment of vNFs that are implemented on diverse packet processing platforms – leveraging the capability of the programmable network data plane. In response, the thesis presents a novel integer linear programming (ILP) model for the hybrid placement of diverse network functions that leverages the heterogeneity of the network data plane and the abundant processing capability of user space hosts, with the objective function of minimizing end-to-end latency for vNF placement. A novel hybrid placement heuristic algorithm, HYPHA, is also proposed to find a quick, efficient solution to the hybrid vNF placement problem. Using optimal stopping theory (OST) principles, an optimal placement scheduling model is presented to handle dynamic edge placement scenarios. The results in this work demonstrate that employing a hybrid deployment scheme that leverages the processing capability of the network data plane yields minimal user-tovNF latency and overall end-to-end latency while fulfilling the placement of a diverse set of user requests from emerging use cases to speed up service delivery by network operators. The results also show that network operators can leverage the high-speed, low-latency feature of data plane packet processing elements for hosting delay-sensitive applications and improving service delivery for subscribed users. It is shown that the proposed hybrid heuristic algorithm can obtain near-optimal vNF mapping while incurring fewer latency threshold violations set by network operators. Furthermore, in addition to emerging edge use cases, the placement solution presented in this thesis can be adapted to place network functions efficiently in core network infrastructure while leveraging the heterogeneity of servers. The dynamic placement scheduler also minimises the number of latency violations and vNF migrations between heterogeneous hosts based on SLAs set by network operators

Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration

Future AI applications require performance, reliability and privacy that the existing, cloud-dependant system architectures cannot provide. In this article, we study orchestration in the device-edge-cloud continuum, and focus on AI for edge, that is, the AI methods used in resource orchestration. We claim that to support the constantly growing requirements of intelligent applications in the device-edge-cloud computing continuum, resource orchestration needs to embrace edge AI and emphasize local autonomy and intelligence. To justify the claim, we provide a general definition for continuum orchestration, and look at how current and emerging orchestration paradigms are suitable for the computing continuum. We describe certain major emerging research themes that may affect future orchestration, and provide an early vision of an orchestration paradigm that embraces those research themes. Finally, we survey current key edge AI methods and look at how they may contribute into fulfilling the vision of future continuum orchestration.Comment: 50 pages, 8 figures (Revised content in all sections, added figures and new section

Machine learning methods for service placement : a systematic review

With the growth of real-time and latency-sensitive applications in the Internet of Everything (IoE), service placement cannot rely on cloud computing alone. In response to this need, several computing paradigms, such as Mobile Edge Computing (MEC), Ultra-dense Edge Computing (UDEC), and Fog Computing (FC), have emerged. These paradigms aim to bring computing resources closer to the end user, reducing delay and wasted backhaul bandwidth. One of the major challenges of these new paradigms is the limitation of edge resources and the dependencies between different service parts. Some solutions, such as microservice architecture, allow different parts of an application to be processed simultaneously. However, due to the ever-increasing number of devices and incoming tasks, the problem of service placement cannot be solved today by relying on rule-based deterministic solutions. In such a dynamic and complex environment, many factors can influence the solution. Optimization and Machine Learning (ML) are two well-known tools that have been used most for service placement. Both methods typically use a cost function. Optimization is usually a way to define the difference between the predicted and actual value, while ML aims to minimize the cost function. In simpler terms, ML aims to minimize the gap between prediction and reality based on historical data. Instead of relying on explicit rules, ML uses prediction based on historical data. Due to the NP-hard nature of the service placement problem, classical optimization methods are not sufficient. Instead, metaheuristic and heuristic methods are widely used. In addition, the ever-changing big data in IoE environments requires the use of specific ML methods. In this systematic review, we present a taxonomy of ML methods for the service placement problem. Our findings show that 96% of applications use a distributed microservice architecture. Also, 51% of the studies are based on on-demand resource estimation methods and 81% are multi-objective. This article also outlines open questions and future research trends. Our literature review shows that one of the most important trends in ML is reinforcement learning, with a 56% share of research

Data Politics

Data has become a social and political issue because of its capacity to reconfigure relationships between states, subjects, and citizens. This book explores how data has acquired such an important capacity and examines how critical interventions in its uses in both theory and practice are possible. Data and politics are now inseparable: data is not only shaping our social relations, preferences and life chances but our very democracies. Expert international contributors consider political questions about data and the ways it provokes subjects to govern themselves by making rights claims. Concerned with the things (infrastructures of servers, devices, and cables) and language (code, programming, and algorithms) that make up cyberspace, this book demonstrates that without understanding these conditions of possibility it is impossible to intervene in or to shape data politics. Aimed at academics and postgraduate students interested in political aspects of data, this volume will also be of interest to experts in the fields of internet studies, international studies, Big Data, digital social sciences and humanities

LUX-ZEPLIN (LZ) Technical Design Report

In this Technical Design Report (TDR) we describe the LZ detector to be built at the Sanford Underground Research Facility (SURF). The LZ dark matter experiment is designed to achieve sensitivity to a WIMP-nucleon spin-independent cross section of three times ten to the negative forty-eighth square centimeters

LIPIcs, Volume 274, ESA 2023, Complete Volume

LIPIcs, Volume 274, ESA 2023, Complete Volum