Simulating fog and edge computing scenarios: an overview and research challenges

The fourth industrial revolution heralds a paradigm shift in how people, processes, things, data and networks communicate and connect with each other. Conventional computing infrastructures are struggling to satisfy dramatic growth in demand from a deluge of connected heterogeneous endpoints located at the edge of networks while, at the same time, meeting quality of service levels. The complexity of computing at the edge makes it increasingly difficult for infrastructure providers to plan for and provision resources to meet this demand. While simulation frameworks are used extensively in the modelling of cloud computing environments in order to test and validate technical solutions, they are at a nascent stage of development and adoption for fog and edge computing. This paper provides an overview of challenges posed by fog and edge computing in relation to simulation

P4SINC – An Execution Policy Framework for IoT Services in the Edge

Internet of Things (IoT) services are increasingly deployed at the edge to access and control Things. The execution of such services needs to be monitored to provide information for security, service contract, and system operation management. Although different techniques have been proposed for deploying and executing IoT services in IoT gateways and edge servers, there is a lack of generic policy frameworks for instrumentation and assurance of various types of execution policies for IoT services. In this paper, we present P4SINC as an execution policy framework that covers various functionalities for IoT services deployed in software-defined machines in IoT infrastructures. P4SINC supports the instrumentation and enforcement of IoT services during their deployment and execution, thus being leveraged for other purposes such as security and service contract management. We illustrate our prototype with realistic examples

Seamless connectivity:investigating implementation challenges of multibroker MQTT platform for smart environmental monitoring

Abstract. This thesis explores the performance and efficiency of MQTT-based infrastructure Internet of Things (IoT) sensor networks for smart environment. The study focuses on the impact of network latency and broker switching in distributed multi-broker MQTT platforms. The research involves three case studies: a cloud-based multi-broker deployment, a Local Area Network (LAN)-based multi-broker deployment, and a multi-layer LAN network-based multi-broker deployment. The research is guided by three objectives: quantifying and analyzing the latency of multi-broker MQTT platforms; investigating the benefits of distributed brokers for edge users; and assessing the impact of switching latency at applications. This thesis ultimately seeks to answer three key questions related to network and switching latency, the merits of distributed brokers, and the influence of switching latency on the reliability of end-user applications

Edge computing infrastructure for 5G networks: a placement optimization solution

This thesis focuses on how to optimize the placement of the Edge Computing infrastructure for upcoming 5G networks. To this aim, the core contributions of this research are twofold: 1) a novel heuristic called Hybrid Simulated Annealing to tackle the NP-hard nature of the problem and, 2) a framework called EdgeON providing a practical tool for real-life deployment optimization. In more detail, Edge Computing has grown into a key solution to 5G latency, reliability and scalability requirements. By bringing computing, storage and networking resources to the edge of the network, delay-sensitive applications, location-aware systems and upcoming real-time services leverage the benefits of a reduced physical and logical path between the end-user and the data or service host. Nevertheless, the edge node placement problem raises critical concerns regarding deployment and operational expenditures (i.e., mainly due to the number of nodes to be deployed), current backhaul network capabilities and non-technical placement limitations. Common approaches to the placement of edge nodes are based on: Mobile Edge Computing (MEC), where the processing capabilities are deployed at the Radio Access Network nodes and Facility Location Problem variations, where a simplistic cost function is used to determine where to optimally place the infrastructure. However, these methods typically lack the flexibility to be used for edge node placement under the strict technical requirements identified for 5G networks. They fail to place resources at the network edge for 5G ultra-dense networking environments in a network-aware manner. This doctoral thesis focuses on rigorously defining the Edge Node Placement Problem (ENPP) for 5G use cases and proposes a novel framework called EdgeON aiming at reducing the overall expenses when deploying and operating an Edge Computing network, taking into account the usage and characteristics of the in-place backhaul network and the strict requirements of a 5G-EC ecosystem. The developed framework implements several placement and optimization strategies thoroughly assessing its suitability to solve the network-aware ENPP. The core of the framework is an in-house developed heuristic called Hybrid Simulated Annealing (HSA), seeking to address the high complexity of the ENPP while avoiding the non-convergent behavior of other traditional heuristics (i.e., when applied to similar problems). The findings of this work validate our approach to solve the network-aware ENPP, the effectiveness of the heuristic proposed and the overall applicability of EdgeON. Thorough performance evaluations were conducted on the core placement solutions implemented revealing the superiority of HSA when compared to widely used heuristics and common edge placement approaches (i.e., a MEC-based strategy). Furthermore, the practicality of EdgeON was tested through two main case studies placing services and virtual network functions over the previously optimally placed edge nodes. Overall, our proposal is an easy-to-use, effective and fully extensible tool that can be used by operators seeking to optimize the placement of computing, storage and networking infrastructure at the users’ vicinity. Therefore, our main contributions not only set strong foundations towards a cost-effective deployment and operation of an Edge Computing network, but directly impact the feasibility of upcoming 5G services/use cases and the extensive existing research regarding the placement of services and even network service chains at the edge