1,720,983 research outputs found
Intent-based Decentralized Orchestration for Green Energy-aware Provisioning of Fog-native Workflows
No full textIntent-based Decentralized Orchestration for Green Energy-aware Provisioning of Fog-native Workflows
No full text
A Geometric Approach to Real-time Quality of Experience Prediction in Volatile Edge Networks
Full text linkSelf-organizing fog support services for responsive edge computing
Full text linkRecent years have seen fog and edge computing emerge as new paradigms to provide more responsive software services. While both these concepts have numerous advantages in terms of efficiency and user experience by moving computational tasks closer to where they are needed, effective service scheduling requires a different approach in the geographically widespread fog than it does in the cloud. Additionally, fog and edge networks are volatile, and of such a scale that gathering all the required data for a centralized scheduler results in prohibitively high memory use and network traffic. Since the fog is a geographically distributed computational substrate, a suitable solution is to use a decentralized service scheduler, deployed on all nodes, which can monitor and deploy services in its neighbourhood without having to know the entire service topology. This article presents a fully decentralized service scheduler, labeled "SoSwirly", for fog and edge networks containing hundreds of thousands of devices. It scales service instances as required by the edge, based on available resources and flexibly defined distance metrics. A mathematical model of fog networks is presented, along with a theoretical analysis and an empirical evaluation which indicate that under the right conditions, SoSwirly is highly scalable. It is also explained how to achieve these conditions by carefully selecting configuration parameters. Concretely, only 15 MiB of memory is required on each node, and network traffic in the evaluations is less than 4 Kbps on edge nodes, while 4-6% more service instances are created than by a centralized algorithm
A Geometric Approach to Real-time Quality of Experience Prediction in Volatile Edge Networks
No full textExtending Kubernetes clusters to low-resource edge devices using virtual Kubelets
Full text linkIn recent years, containers have gained popularity as a lightweight virtualization technology. This rise in popularity has gone hand in hand with the adoption of microservice architectures, mostly thanks to the scalable, ethereal, and isolated nature of containers. More recently, edge devices have become powerful enough to be able to run containerized microservices, while remaining flexible enough in terms of size and power to be deployed almost anywhere. This has triggered research into several container placement strategies involving edge networks, leading to concepts such as osmotic computing. While these container placement strategies are optimal in terms of workload placement, current container orchestrators are often not suitable for running on edge devices due to their high resource requirements. In this article, FLEDGE is presented as a Kubernetes-compatible container orchestrator based on Virtual Kubelets, aimed primarily at container orchestration on low-resource edge devices. Several aspects of low-resource container orchestration are examined, such as the choice of container runtime and how to realize container networking. A number of evaluations are performed to determine how FLEDGE compares to Kubernetes and K3S in terms of resource requirements, showing that it needs around 60MiB memory and 78MiB storage to run on a Raspberry Pi 3, including all dependencies, which is significantly less than both studied alternatives
A Geometric Approach to Real-time Quality of Experience Prediction in Volatile Edge Networks
No full textA Novel Edge-to-Cloud-as-a-Service (E2CaaS) Model for Building Software Services in Smart Cities
Full text linkThe main goal of a smart city is to enhance the quality of life of its inhabitants by providing services using Information and Communications Technology (ICT) components in a city. ICT components include not only Internet of Things (IoT) data sources spread across the city, but also traditional non-IoT data sources. Managing all ICT components in a smart city can be challenging and results in many complexities. Consequently, there is a need for ICT management architectures. Traditional solutions are often based on a centralized ICT architecture using Cloud technologies. Recently, the number of ICT components, services, and their corresponding complexities are growing, leading to large-scale ICT architectures. Centralized Cloud solutions cannot cope with the ever-expanding demands of this kind of architectures. The limitations of the centralized approaches necessitate the design of a new ICT architecture, using distributed technologies, for every layer and element of the city. Many solutions for management from Edge-to-Cloud (E2C) through distributed technologies are forthcoming, including Decentralized-to-Centralized ICT (DC2C-ICT) and Distributed-to-Centralized ICT (D2C-ICT) architectures. The DC2C-ICT architecture and its components work on their own tasks and are solely communicating with a centralized platform. On the other hand, components of the D2C-ICT architecture can work together to provide the services for the citizens across different layers from E2C. Therefore, the D2CICT architecture is less dependent on the central Cloud-based entity, but harder to design and manage. In this paper, an “Edge-to-Cloud-as-a-Service (E2CaaS) ” model is proposed together with a model on how to build efficient software services in smart cities through different layers of E2C. The most important tasks for building these services are the management of“Data/Database,” “Resources,” and “Network Communication and Cybersecurity issues”
- …
