RESUMEN
Most recent edge and fog computing architectures aim at pushing cloud-native traits at the edge of the network, reducing latency, power consumption, and network overhead, allowing operations to be performed close to data sources. To manage these architectures in an autonomous way, systems that materialize in specific computing nodes must deploy self-* capabilities minimizing human intervention across the continuum of computing equipment. Nowadays, a systematic classification of such capabilities is missing, as well as an analysis on how those can be implemented. For a system owner in a continuum deployment, there is not a main reference publication to consult to determine what capabilities do exist and which are the sources to rely on. In this article, a literature review is conducted to analyze the self-* capabilities needed to achieve a self-* equipped nature in truly autonomous systems. The article aims to shed light on a potential uniting taxonomy in this heterogeneous field. In addition, the results provided include conclusions on why those aspects are too heterogeneously tackled, depend hugely on specific cases, and shed light on why there is not a clear reference architecture to guide on the matter of which traits to equip the nodes with.
RESUMEN
Cloud-native computing principles such as virtualization and orchestration are key to transferring to the promising paradigm of edge computing. Challenges of containerization, operative models and scarce availability of established tools make a thorough review indispensable. Therefore, the authors have described the practical methods and tools found in the literature as well as in current community-led development projects, and have thoroughly exposed the future directions of the field. Container virtualization and its orchestration through Kubernetes have dominated the cloud computing domain, while major efforts have been recently recorded focused on the adaptation of these technologies to the edge. Such initiatives have addressed either the reduction of container engines and the development of specific tailored operating systems or the development of smaller K8s distributions and edge-focused adaptations (such as KubeEdge). Finally, new workload virtualization approaches, such as WebAssembly modules together with the joint orchestration of these heterogeneous workloads, seem to be the topics to pay attention to in the short to medium term.
RESUMEN
During the past few decades, the combination of flourishing maritime commerce and urban population increases has made port-cities face several challenges. Smart Port-Cities of the future will take advantage of the newest IoT technologies to tackle those challenges in a joint fashion from both the city and port side. A specific matter of interest in this work is how to obtain reliable, measurable indicators to establish port-city policies for mutual benefit. This paper proposes an IoT-based software framework, accompanied with a methodology for defining, calculating, and predicting composite indicators that represent real-world phenomena in the context of a Smart Port-City. This paper envisions, develops, and deploys the framework on a real use-case as a practice experiment. The experiment consists of deploying a composite index for monitoring traffic congestion at the port-city interface in Thessaloniki (Greece). Results were aligned with the expectations, validated through nine scenarios, concluding with delivery of a useful tool for interested actors at Smart Port-Cities to work over and build policies upon.