Search

Data sovereignty upholds that data is subject to the rules of the data provider and the applicable laws of the country where the data is collected. To achieve data sovereignty, various approaches have been proposed which rely on cloud computing resources to provide users with the necessary functionality to exchange sovereign data. However, when users are located far away from the cloud, using cloud resources can cause delays and potential network bandwidth bottlenecks. To address such issues, we propose an architecture that relies on edge computing resources. In this architecture, the components that handle the transfer of sovereign data are placed on-premise, allowing users to exchange data without using the cloud. To evaluate our approach, we conduct various experiments in a real-world setting using a system that follows the proposed architecture and a baseline that relies on the cloud. The results show that the proposed approach provides significant benefits, such as a ∼20% latency reduction and increased network bandwidth.

Fog computing is a novel computing paradigm which enables the execution of applications on compute nodes which reside both in the cloud and at the edge of the network. Various performance benefits, such as low communication latency and high network bandwidth, have turned this paradigm into a well-accepted extension of cloud computing. So far, many fog computing systems have been proposed, consisting of distributed compute nodes which are often organized hierarchically in layers. Such systems commonly rely on the assumption that the nodes of adjacent layers reside close to each other, thereby achieving low latency computations. However, this assumption may not hold in fog computing systems that span over large geographical areas, due to the wide distribution of the nodes. In addition, most proposed fog computing systems route the data on a path which starts at the data source, and goes through various edge and cloud nodes. Each node on this path may accept the data if there are available resources to process this data locally. Otherwise, the data is forwarded to the next node on path. Notably, when the data is forwarded (rather than accepted), the communication latency increases by the delay to reach the next node. This thesis aims at tackling these problems by proposing distributed algorithms whereby the compute nodes measure the network proximity to each other, and self-organize accordingly. These algorithms are implemented on geographically distributed compute nodes, considering image processing and smart city use cases, and are thoroughly evaluated showing significant latency- and bandwidth-related performance benefits. Furthermore, we analyze the communication latency of sending data to distributed edge and cloud compute nodes, and we propose two novel routing approaches: i) A context-aware routing mechanism which maintains a history of previous transmissions, and uses this history to find nearby nodes with available resources. ii) edgeRouting, which leverages the high bandwidth between nodes of cloud providers in order to select network paths with low communication latency. Both of these mechanisms are evaluated under real-world settings, and are shown to be able to lower the communication latency of fog computing systems significantly, compared to alternative methods.

2020 IEEE International Conference on Edge Computing (EDGE)

In recent years, the Internet of Things (IoT) has gained a lot of attention due to connecting various sensor devices with the cloud, in order to enable smart applications such as: smart traffic management, smart houses, and smart grids, among others. Due to the growing popularity of the IoT, the number of Internet-connected devices has increased significantly. As a result, these devices generate a huge amount of network traffic which may lead to bottlenecks, and eventually increase the communication latency with the cloud. To cope with such issues, a new computing paradigm has emerged, namely: fog computing. Fog computing enables computing that spans from the cloud to the edge of the network in order to distribute the computations of the IoT data, and to reduce the communication latency. However, fog computing is still in its infancy, and there are still related open problems. In this paper, we focus on the node discovery problem, i.e., how to add new compute nodes to a fog computing system. Moreover, we discuss how addressing this problem can have a positive impact on various aspects of fog computing, such as fault tolerance, resource heterogeneity, proximity awareness, and scalability. Finally, based on the experimental results that we produce by simulating various distributed compute nodes, we show how addressing the node discovery problem can improve the fault tolerance of a fog computing system., OASIcs, Vol. 80, 2nd Workshop on Fog Computing and the IoT (Fog-IoT 2020), pages 5:1-5:10

It has been more than fifteen years since the term Internet of Things (IoT) was introduced to the public. However, despite the efforts of research groups and innovative corporations, still today it is not possible to say that IoT is upon us. This is mainly due to the fact that a unified IoT architecture has not been yet clearly defined and there is no common agreement in defining protocols and standards for all IoT parts. The framework that current IoT platforms use consists mostly in technologies that partially fulfill some of the IoT requirements. While developers employ existing technologies to build the IoT, research groups are working on adapting protocols to the IoT in order to optimize communications. In this paper, we present and compare existing IoT application layer protocols as well as protocols that are utilized to connect the “things” but also end-user applications to the Internet. We highlight IETF’s CoAP, IBM’s MQTT, HTML 5’s Websocket among others, and we argue their suitability for the IoT by considering reliability, security, and energy consumption aspects. Finally, we provide our conclusions for the IoT application layer communications based on the study that we have conducted.