Easy Edges: Automating Connectivity and Scheduling at the Edge

Today, cloud computing is a well-established paradigm that enjoys a moderate level of popularity among Science Gateways and Virtual Research Environments. Historically, Science Gateways have empowered researchers with traditional onpremise High-Performance Computing (HPC), but recent years have seen many turning to the cloud to fill that space. One often cited cloud complaint is the distance that data must travel. Sensors and input devices generate data locally in the lab, but even for a basic calculation, all those bits must make the costly journey to a data centre hundreds of miles away. The proliferation of the Internet of Things, though, is quickly changing this. As microchips decrease in size, the compute power of those sensors and input devices is increasing, along with every other compute or networking device in the lab. These devices, at the edge of the network, can now be exploited for less intensive computations, such as pre-processing or analytics. This creates an ideal opportunity for Science Gateways. As well as connecting research communities to the cloud, they can bring edges into the mix and get the advantages of both. In that, lies a considerable challenge though. Edge devices come in all shapes and sizes – or, from a computing point of view, all operating systems and system architectures. Many such devices are connected by Wi-Fi, so connection issues are common and unpredictable. Once deployed, communications between edge and cloud must be secured. DevOps engineers are solving these issues with OCIcompliant containers (Docker), configuration management (Ansible®), container orchestrators (Kubernetes), and edgeenabling add-ons (k0s, k3s, KubeEdge or microk8s). But configuring a container orchestration cluster in the cloud takes expert knowledge and effort, and navigating the landscape of edge add-ons takes even more. These are lengthy, manual processes, not suited to a Science Gateway administrator. This abstract proposes a generic solution to the problem of edge connectivity by abstracting away this complexity and automating the process. Users bring their containers and edge devices, and Kubernetes, Ansible and KubeEdge or k3s do the rest, with some help from MiCADO, a dynamic cloud orchestrator. Figure 1 shows this process. An Ansible Playbook creates a Kubernetes cluster in the cloud, deploying an appropriate Kubernetes edge add-on in the process. Then, a user provides a description of their application (container images and configuration) and edge nodes (connection details). MiCADO supports these descriptions in TOSCA, but higher-level tools can be used to generate TOSCA from descriptive metadata. Actioning this description triggers Kubernetes and a second Ansible Playbook, which create the connection to the edge and schedule containers appropriately across edge and cloud. In MiCADO, the TOSCASubmitter is responsible for these triggers. A KubernetesAdaptor transpiles the relevant container descriptions to Kubernetes Manifests and executes them. An AnsibleAdaptor, newly created for connecting edges on the fly,automates configuration and execution of the Playbook. This approach is being used for a Science Gateway in the PITHIA-NRF Project, and for an Industry Gateway in the DIGITbrain Project. It supports a simpler route to edge computing that can be exploited by Science Gateways for efficient computing along the cloud-to-edge continuum

Dynamic Composition and Automated Deployment of Digital Twins for Manufacturing

Digital twins represent an emerging trend and are already widely applied in the manufacturing sector to simulate the behaviour of manufacturing lines or industrial products, and to enhance or optimize their performance. The DIGITbrain project, funded by the European Commission’s H2020 Programme aims to extend the traditional digital twin concept towards the Digital Product Brain that steers the behaviour and performance of an industrial product by coalescing its physical and digital dimensions, and by memorising the occurred (physical and digital) events throughout its entire lifecycle. With such capabilities, the Digital Product Brain can steer the quick and convenient customization and repurposing of manufacturing lines/industrial products and support the realization of a smart business model based on Manufacturing as a Service (MaaS). MaaS can lead to more customised manufacturing processes and products, and can also support the refactoring of manufacturing lines in the case of crisis situations, such as a pandemic. The technical developments in DIGITbrain are based on the results of the CloudiFacturing project that implemented a cloud-based platform, combined with a digital marketplace as a business gateway, for the execution of simulation or optimisation applications and workflows. However, workflows and applications in CloudiFacturing are typically monolithic, tightly coupling algorithms with models and data sources, making them applicable to only one particular scenario. In order to improve the reusability of various assets (i.e. data, models and algorithms), DIGITbrain clearly separates these assests from each other and enables the creation of DMA (data-model-algorithm) tuples that represent a certain instance of a digital twin. Such digital twin or DMA tuple instances can then be executed as a set of interconnected microservices on the targeted cloud, or even on edge and fog computing resources. For the automated deployment and run-time management of microservices-based applications, DIGITbrain utilises the MiCADO cloud to edge orchestration framework that is responsible for deploying the instantiated DMA tuples on central cloud computing resources, or on edge and fog nodes closer to the data sources. However, as the various data, model and algorithm assets are created separately, a specific challenge has emerged to automatically and dynamically generate the deployment descriptors required by MiCADO during the publishing and authoring process. MiCADO uses an Application Description Template (ADT) based on the OASIS TOSCA (Topology and Orchestration Specification for Cloud Applications) standard specification to describe the application topology to be deployed and the various policies that govern the application’s run-time behaviour. In all previous application scenarios, the ADT was created in a single manual step by the application developer/owner. However, in DIGITbrain the ADT needs to be programmatically assembled from previously published fragments that represent the individual data, model and algorithm assets, as well as representations of the cloud, fog and edge resources that comprise the infrastructure. This presentation will give an overview of how MiCADO is applied within the DIGITbrain platform, what extensions were required to support the targeted application scenarios, and how a MICADO ADT can be dynamically assembled from metadata of individual assets

Scaling and Suppression of Anomalous Heating in Ion Traps

We measure and characterize anomalous motional heating of an atomic ion confined in the lowest quantum levels of a novel rf ion trap that features moveable electrodes. The scaling of heating with electrode proximity is measured, and when the electrodes are cooled from 300 to 150 K, the heating rate is suppressed by an order of magnitude. This provides direct evidence that anomalous motional heating of trapped ions stems from microscopic noisy potentials on the electrodes that are thermally driven. These observations are relevant to decoherence in quantum information processing schemes based on trapped ions and perhaps other charge-based quantum systems

Zero-Point cooling and low heating of trapped 111Cd+ ions

We report on ground state laser cooling of single 111Cd+ ions confined in radio-frequency (Paul) traps. Heating rates of trapped ion motion are measured for two different trapping geometries and electrode materials, where no effort was made to shield the electrodes from the atomic Cd source. The low measured heating rates suggest that trapped 111Cd+ ions may be well-suited for experiments involving quantum control of atomic motion, including applications in quantum information science.Comment: 4 pages, 6 figures, Submitted to PR

Enabling modular design of an application-level auto-scaling and orchestration framework using tosca-based application description templates

This paper presents a novel approach to writing TOSCA templates for application reusability and portability in a modular auto-scaling and orchestration framework (MiCADO). The approach defines cloud resources as well as application containers in a flexible and generic way, and allows for those definitions to be extended with specific properties related to a desired container orchestrator chosen at deployment time. The approach is demonstrated in a proof-of-concept where only a minor change was required to a previously used application template in order to achieve the successful deployment and lifecycle management of the popular web authoring tool Wordpress on a new realization of the MiCADO framework featuring a different container orchestrator

T-junction ion trap array for two-dimensional ion shuttling, storage and manipulation

We demonstrate a two-dimensional 11-zone ion trap array, where individual laser-cooled atomic ions are stored, separated, shuttled, and swapped. The trap geometry consists of two linear rf ion trap sections that are joined at a 90 degree angle to form a T-shaped structure. We shuttle a single ion around the corners of the T-junction and swap the positions of two crystallized ions using voltage sequences designed to accommodate the nontrivial electrical potential near the junction. Full two-dimensional control of multiple ions demonstrated in this system may be crucial for the realization of scalable ion trap quantum computation and the implementation of quantum networks.Comment: 3 pages, 5 figure