2,391 research outputs found
Self-organizing fog support services for responsive edge computing
No full textRecent 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 textLive demonstration of a highly scalable fog service orchestrator
No full textIn recent years, computing workloads have shifted from the cloud to the fog and edge, as IoT devices are becoming powerful enough to run containerized services. While the fog and edge computing can increase energy efficiency, reduce network traffic and provide better end user experience, the scale and volatility of the fog and edge also present new problems for service scheduling. In the edge, there are orders of magnitude more devices than in cloud data centers, and conditions are often less stable. Additionally, unlike in data centers, the network topology of the edge often changes, requiring a real-time approach to scheduling. In this demonstration, an implementation of a highly scalable orchestrator named “Swirly” is presented. The challenge of fog service scheduling is illustrated by using this implementation to organize software services in near real-time and on-demand in a virtual representation of a real-world industry park. Performance indicators are presented to show that this solution can scale up to 300.000 edge nodes
Extending Kubernetes clusters to low-resource edge devices using virtual Kubelets
No full textIn 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
Enabling and leveraging AI in the intelligent edge : a review of current trends and future directions
No full textThe use of AI on Smart applications and in the organization of the network edge presents a rapidly advancing research field, with a great variety of challenges and opportunities. This article aims to provide a holistic review of studies from 2019 to 2021 related to the Intelligent Edge, a concept comprising both the use of AI to organize edge networks (Edge Intelligence) and Smart applications in the edge. An introduction is given to the technologies required to understand the state of the art of AI in edge networks, and a taxonomy is provided with “Enabling Technology” for Edge Intelligence, “Organization” of the edge using AI, and AI “Applications” in the edge as its main topics. Research trend data from 2015 to 2020 is presented for various subdivisions of these topics, showing both absolute and relative research interest in each subtopic. The “Organization” aspect, being the main focus of this article, has a more fine-grained subdivision, explaining all contributing factors in detail. The trends indicate an exponential increase in research interest in nearly all subtopics, but significant differences between them. For each subdivision of the taxonomy a number of selected studies from 2019 to 2021 are gathered to form a high-level illustration of the state of the art of Edge Intelligence. From these selected studies and the trend data, a number of short-term challenges and high-level visions for Edge Intelligence are formulated, providing a basis for future work
A Geometric Approach to Real-time Quality of Experience Prediction in Volatile Edge Networks
No full text
A Geometric Approach to Real-time Quality of Experience Prediction in Volatile Edge Networks
No full text
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