Edge computing optimizes Internet devices and web applications by bringing computing closer to the source of the data. Configure an analytics profile. “Edge computing” is a type of distributed architecture in which data processing occurs close to the source of data, i.e., at the “edge” of the system. 3. The architecture diagram below shows a detailed view of the edge data center with an automated system used to operate a shrimp farm. After the container images for the agreement are downloaded and verified, an appropriate Docker network is created for the images. The use cases in this document are mostly envisioned as a spider web type of architecture with hierarchy automatically able to scale the number of endpoints. The device layer consists of small devices running on the edge. Testing is as much an art form as it is a precise engineering process. Maximo Visual Inspection is a video and image analysis platform that makes it easy for subject matter experts to train and deploy image classification and object detection models. The second phase is more difficult. If necessary, update to the current version of Docker by running the following commands: Install the Open Horizon agent on the device. For instance, profile attributes may have all been set correctly, but are all the resources reachable, in good health, and can communicate to each other as expected? Devices can be small. In general, the larger the data set, the better the accuracy of the model will be. Since this is a high-level discussion, the assumption is that there will be enough compute, storage and networking functionality to the edge to cover the basic needs; any specialized configurations or features are out of scope. The network needs to provide both high throughput and low latency combined with efficient use of the available capacity in order to support the performance demands of the emerging 5G offerings. In summary, this architecture model does not fulfill every use case, but it provides an evolution path to already existing architectures. A solution is needed to monitor the designated area and issue an alert only when an employee has been detected, entering the area without wearing a hard-hat. As can be seen from these discussions, edge computing related innovation and software evolution is still very much in its early stages. To fulfill the high performance and low latency communication needs, at least some of the data processing and filtering needs to stay within the factory network, while still being able to use the cloud resources more effectively. For instance, the system can pre-process water quality data from the monitoring sensors and send structured information back to the central cloud. On the target cluster, create a directory for the private repo in the certs.d folder: Copy ca.crt from the hub cluster to the target cluster. The devices could handle analysis and real-time inferencing without involvement of the edge server or the enterprise region. In this article, we will describe how we implemented a workplace safety use case involving the application and device layer of the edge computing architecture. This allows frameworks to be created that support running an automated unit test suite that addresses requirements such as repeatability, replicability and reproducibility. Edge Computing Edge Computing There are three primary applications of Edge Computing we will discuss in this white paper. This section covers two common high-level architecture models that show the two different approaches. With edge computing architecture, complex event processing happens in the device or a system close to the device, which eliminates round-trip issues and enables actions to happen quicker. Video data can be processed at the edge, either at the application layer or the device layer. The concept is that factories are using computers and automation in new ways by incorporating autonomous systems and machine learning to make smarter factories. Now that the testbed is prepared and tested, the next step is to deploy the software applications on the infrastructure. Depending on needs, there are choices on the level of autonomy at each layer of the architecture to support, manage and scale the massively distributed systems. "Edge" is a term with varying definitions depending on the particular problem a deployer is attempting to solve. To describe what it all means in practice, take a Radio Access Network (RAN) as an example. Due to the throughput demands of applications like these and workloads such as virtual network functions (VNF) for 5G, various offloading and acceleration technologies are being leveraged to boost performance through software and hardware, such as: Architecture design is always specific to the use case, taking into account all the needs of the given planned workload and fine tuning the infrastructure on demand. On the local machine run this command: Run the following command in both the target cluster and the hub cluster to create the pull secret that is then used in the deployment.yaml file of the helm chart: Add the IBM Cloud Pak for Multicloud Management IP address to the IBM Cloud Private hosts file: Add a line like this with the IP address and host name: . Even if the majority of building blocks are available to create an environment that fulfills most requirements, many of these components need fine tuning or API extensions to provide a more optimized and fit for purpose solution. The complexity of the applications that can be run depends on the footprint of the edge server. Edge Computing has been a growing topic for the past few years. When an agreement is accepted, the corresponding containers can begin running. Principal Software Engineer, Dell Technologies, Ildikó Váncsa, Ecosystem Technical Lead, OpenStack Foundation. While a few tools exist to perform network traffic shaping and fault injections, the challenge lies more in the identification of values that are representative to the aforementioned edge use cases. To ensure the success of testing, the installation itself needs to be verified, for instance, checking the services to ensure they were installed and configured correctly. However, to get the same benefits for user plane and radio applications without bumping into the physical limitations of the speed of light, compute power needs to move further out to the edges of the network. In this article we'll give you an overview over Edge Computing, discuss its advantages, explain a sample architecture as well as the classes of use cases it can be applied to. The diagram below describes the general process that is executed when performing experimental campaigns. With more than a TFLOP/s of performance, Jetson TX2 is ideal for deploying advanced AI to remote field locations Using OpenStack in the centralized control plane model depends on the distributed virtual router (DVR) feature of the OpenStack Network Connectivity as a Service (Neutron) component. Import the images or videos that you created in step 1. This is accomplished using IBM Maximo Visual Inspection. This enables it to provide the extreme high bandwidth required between the radio equipment and the applications or to fulfill demands for low latency. The Edge computing architecture highlights the three industries that drive IBM edge solutions: telecommunications, industrial, and retail. Edge computing is an emerging paradigm which uses local computing to enable analytics at the source of the data. The following screen shot shows all four .yaml files that were created for our hardhat scenario. 4 Edge Computing Reference Architecture 2.0 • Efficient flow and integration of information Currently, the industry has more than six industrial real … What is edge computing and why it matters With deployments of IoT devices and the arrival of 5G fast wireless, placing compute and analytics close to where data is … This provides an orchestrational overhead to synchronize between these data centers and manage them individually and as part of a larger, connected environment at the same time. These permutations of perspectives drive a paucity of aligned user stories to share with the OpenStack and StarlingX communities. In our use case, we are using Jetson TX2 as the smart camera. Create a unique node ID and token for each device in HZN_EXCHANGE_NODE_AUTH. Firstly, task placement is not only two options, i.e., either at local or in the cloud, but possible on any edge node. Similarly to the telecommunication industry, manufacturing also has very strict requirements. These environments can be very fragile; therefore, it requires high precision to create and sustain healthy and balanced ecosystems. The use of open-source components is key at the device layer, because the portability of our edge solution is key across private, public, and edge clouds. Use the deploy_zip_model.sh script to deploy a model exported from Maximo Visual Inspection on this system. In this paper, we describe the offloading system model and present an innovative architecture, called "MVR", contributing to computation offloading in mobile edge computing. On the device layer, any tools or components must be able to manage workloads placed across clusters and the device edge. This is accomplished using IBM Visual analytics. To implement the use case, this edge device needs to be registered to IBM Edge Application Manager. The choice depends on the characteristics of the individual use case and the capabilities of the software components used, because the overall behavior and management of each configuration is different. New test cases need to be identified along with values that are representative to typical circumstances and system failures. Edge computing is highly dependent on lessons learned and solutions implemented in the cloud. Before going into detail about the individual site type configurations, there is a decision that needs to be made on where to locate the different infrastructure services’ control functions and how they need to behave. Fundamentally, edge computing architectures are built on existing technologies and established paradigms for distributed systems, which means that there are many well understood components available to create the most effective architectures to build and deliver edge use cases. The behavior of the edge data centers in case of a network connection loss might be different based on the architectural models. With more computational power at the edge data centers, it is possible to store and analyze local monitoring data for faster reaction time to manage changes in environmental conditions or modify feeding strategy. The platform provides data to be collected and analyzed both locally on the farms and centrally to improve the environmental conditions and prevent mistakes while using chemicals like auxiliary materials and disinfectants. These are both open source projects with extensive testing efforts that are available in an open environment. At the same time, cloud providers are building edge computing into their IoT tool chains (Trifirio: “it's kind of this transparent edge-to-core capability”) or offering edge products that developers and enterprises can purchase. Unregistering a pattern means stopping the running containers on the edge device and restarting the horizon service to make the device available to accept new patterns. Copy the API key that is generated after running the above command: Confirm the node with the IBM Edge Application Manager. One method is to use federation techniques to connect the databases to operate the infrastructure as a whole; another option is to synchronize the databases across sites to make sure they have the same working set of configurations across the deployment. The building blocks are already available to create edge deployments for OpenStack and Kubernetes. The illustration of the above edge architecture shows how the CU component can be located in an aggregated or regional edge site while the vDU would be located in the edge data centers. With the emergence of 5G as a technology transformation catalyst, companies are considering edge computing as part of their overall strategy. This means they are more resilient to network connectivity issues as well as being able to minimize disruption caused by latency between edge sites. In the following steps, we will go through the process of deploying these Docker images to IBM Cloud Private using the helm charts. The application layer runs on the local edge and has greater compute power than the device layer. In these types of infrastructures, there is no one well defined edge; most of these environments grow organically, with the possibility of different organizations owning the various components. Create an empty index.yaml file and push it to the repo: Add the helm chart to the GitHub repo and edit the index.yaml file. Openstack.org is powered by VEXXHOST. This allows you to make the hardhat model available to others, such as customers or collaborators and ability to run the model on other systems. Edge computing is a distributed computing paradigm that brings computation and data storage closer to the location where it is needed, to improve response times and save bandwidth. This is done using IBM Edge Application Manager. While the management and orchestration services are centralized, this architecture is less resilient to failures from network connection loss. The central locations are typically well equipped to handle high volumes of centralized signaling and are optimized for workloads which control the network itself. In addition, the configuration options are significantly different among the different models. Define a reference architecture for edge and far edge deployments including OpenStack services and other open source components as building blocks. Because of that, there are situations where there will be a need to test basic functionality in these environments as well to make sure they work as expected in other scenarios. Click the Label Objects button. The exact number of levels will depend on the size of the operator network. How Edge Computing Is Evolving The primary product for the device layer is IBM Edge Application Manager. Factories are using more automation and leveraging cloud technologies for flexibility, reliability and robustness, which also allows for the possibility of introducing new methods such as machine vision and learning to increase production efficiency. Further components are needed to ensure the ability to test more complex environments where growing numbers of building blocks are integrated with each other. Testing the integrated systems to emulate the configuration and circumstances of production environments can be quite challenging. You can use this tutorial on IBM Cloud Garage to learn how to deploy and manage applications across clusters using IBM Cloud Pak for Multicloud Management. For example, a public cloud provider might supply some of the core infrastructure, while other vendors are supplying the hardware, and yet a third set of integrators are building the software components. (The third article in this series will cover the network layer.). For systems built on environments such as OpenStack and Kubernetes services, frameworks like Kolla, TripleO, Kubespray or Airship are available as starting points. Using IBM Cloud Pak for Multicloud Management, the operator can have rich views of how clusters operate within the environment. The assigned resources (e.g., compute, storage, network) represent the physical infrastructure that will be used to conduct the evaluation. Benchmarking is often defined as performance testing, but here it applies to a broader scope that includes integration and functional testing as well. This can be challenging because most data center centric deployments treat compute nodes as failed resources when they become unreachable. 1. to advance next-generation edge computing solutions. Login to IBM Cloud Pak for Multicloud Management, and ssh into the system. This command automatically generates sample yaml files including chart.yaml, values.yaml, service.yaml, and deployment.yaml. Extending cloud services to edge devices is fog computing. When you are done configuring the components, restart IBM Video Analytics. Considering the high level of integration needed, it is crucial that the subject matter experts of the various components start to contribute to a common effort. As can be seen from these few use cases, there are both common challenges and functionality that become even more crucial in edge and hybrid environments. Edge must be by its very nature highly adaptable. For instance, a recent study presents a disruptive approach consisting of running standalone OpenStack installations in different geographical locations with collaboration between them on demand. The OpenStack project is provided under the Apache 2.0 license. The amount of data processing and computational power needed to support these technologies is increasing by orders of magnitude. Now, you need to package and publish the helm chart. Make sure the file is transferred to IBM Cloud Pak for Multicloud Management. Devices can also be large, such as industrial robots, automobiles, smart buildings, and oil platforms. The closer the end users are to the data and signal processing systems, the more optimized the workflow will be for handling low latency and high bandwidth traffic. Figure 6 Logical Architecture Diagram for Edge Computing To facilitate discussions on the boundaries and the necessary means to enable edge computing, there are “Key Requirements”, “Edge oundary” and “Edge Devices” clauses added to each use case. This element is usually located near a radio tower site with computational and storage capabilities. The previously created hardhat model (in the .tgz file) is loaded on IBM Cloud Pak for Multicloud Management, and then can be deployed to multiple clusters using helm charts. Fog computing architecture consists of physical as well as logical elements of the network, software, and hardware to form a complete network of a large number of interconnecting devices. Copy the following three relevant Horizon Debian packages for your operating system and architecture: horizon, horizon-cli, and bluehorizon from the server where IBM Edge Application Manager is installed to your device. See the installation documentation for detailed instructions. The above described models are still under development as more needs and requirements are gathered in specific areas, such as: Defining common architectures for edge solutions is a complicated challenge in itself, but it is only the beginning of the journey. Add the Docker image to the IBM Cloud Pak for Multicloud Management Private repository: Note: hardhat.tgz is the .tgz you create in the previous section. Edge computing is a technology evolution that is not restricted to any particular industry. The agent must also verify the cryptographic signature with Horizon exchange. Edge computing pushes all the significant computational processing power towards the edges of the mesh. This article discusses how the different layers come together using a use case that requires all three layers: application, device, and network. The creation of the agreements normally is received and accepted in less than a minute. IBM Edge Application Manager provides a new architecture for edge node management. As use cases evolve into more production deployments, the common characteristics and challenges originally documented in the “Cloud Edge Computing: Beyond the Data Center” white paper remain relevant. As in the previous case, this architecture supports a combination of OpenStack and Kubernetes services that can be distributed in the environment to fulfill all the required functionality for each site. Full Guide to Cloud Computing Architecture with Diagram Cloud Computing is an emerging technology. No matter which perspective, edge computing decentralizes and extends campus networks, cellular networks, data center networks, or the cloud. Get a list of all the edge patterns on the exchange using the following command: Register a pattern or service from the above list of the patterns that are available on IBM Edge Application Manager: Look for the agreement list to see the status of registered services. From a bird’s eye view, most of those edge solutions look loosely like interconnected spider webs of varying sizes and complexity. For videos in your data set, you can use the Auto Capture button to capture frames at desired time intervals. Make sure to include varied scenarios with different lighting conditions. Verify that the environment variables are set correctly. Edge Computing Architecture is a new model for providing storage and substantial computing properties near to the devices. How does this help? ETSI GS MEC 003 V2.1.1 (2019-01) Multi-access Edge Computing (MEC); Framework and Reference Architecture Disclaimer The present document has been produced and approved by the Multi-access Edge In some cases, the decision might be to choose to configure the system to keep the instances running while in other cases, the right approach would be to destroy the workloads in case the site becomes isolated. Further similarity between the different use cases, regardless of the industry they are in, is the increased demand for functions like machine learning and video transcoding on the edge. A tool to gather massive information from local “things” as an aggregation and control point. Information from the device layer is sent to the application layer for further processing. The systems even follow the transportation of the shrimp after they are harvested. Maximo Visual Inspection Inference server is a server that lets you quickly and easily deploy multiple trained models. This section will guide you through some use cases to demonstrate how edge computing applies to different industries and highlight the benefits it delivers. Navigate to the Catalog, search for and click on your chart name. Some of the system functions and elements that need to be taken into consideration include: We will use the inference server to create a docker image of the hardhat model. Then, add a name and a scope as cluster, and then add the registry as IBM Cloud Pak for Multicloud Management Private repo . The scope of fog computing starts from the outer edges where the data is collected to where it will be stored eventually. In a particular factory, when employees enter a designated area, they must be wearing a proper Personal Protective Equipment (PPE) such as a hard hat. Like agriculture, the environmental conditions highly affect the animals’ conditions, and therefore the ponds need to be closely monitored for any changes that might affect the well-being of the shrimp, so that prompt actions can be taken to avoid loss. Let’s dive into the details of each of these two layers and the respective components in the layers. As part of testing edge architectures, the deployment tools need to be validated to identify the ones that can be adapted and reused for these scenarios. Verify a direct call to Maximo Visual Inspection running on a server, for example svrX, port 6005: Verify a Deep Learning Engine call to Maximo Visual Inspection: The edge device layer will contain devices that have compute and storage power and can run containers. This use case is also a great example of where equipment is deployed and running in poor environmental conditions. To install the inference server on a machine, download the latest Maximo Visual Inspection Inference software. As mentioned in the first article, the cloud edge is the source for workloads for the different edge layers, provides the management layer across the different edge layers, and hosts the applications that need to handle the processing that is just not possible at the other edge nodes due to limitations at these nodes. Industry 4.0 is often identified with the fourth industrial revolution. Once it detects a person entering the danger zone area, it makes a call to the Maximo Visual Inspection hard hat model to determine whether that individual is wearing a hard hat. There are different options that can be used to overcome the operational challenges of this model. The name ‘edge computing’ refers to computation around the corner/edge in a network diagram. The real challenge lies in efficient and thorough testing of the new concepts and evolving architecture models. The models need to be containerized and deployed to the edge. Introduction to Fog Computing Architecture Fog architecture involves using services of end devices (switches, routers, multiplexers, etc) for computational, storage and processing purposes. While it is common to perform functional and integration testing as well as scalability and robustness checks on the code base, these deployments rarely get extended beyond one or maybe a few physical servers. That doesn’t mean that edge is dead. The Distributed Control Plane model defines an architecture where the majority of the control services reside on the large/medium edge data centers. Add image policies on the target cluster, which in our case is IBM Cloud Private. Now that the edge device is registered to IBM Edge Application Manager, we can register edge patterns from the exchange server. These devices can run relatively simple applications to gather information, run analytics, apply AI rules, and even store some data locally to support operations at the edge. The edge server can be an X server or an IBM Power System server that is often run on premise in an environment such as a retail store, cellular tower, or other location outside of the core network or data center of the enterprise. Once our TX2 device is registered to IBM Edge Application Manager, the object detection YOLO model can be deployed which can then help identify human beings in the danger zone and start the stream to the server. Then, the containers can run. Tools such as Enos, Enos-Kubernetes and enoslib are available in the experiment-driven research community to evaluate OpenStack and Kubernetes in a distributed environment over Wide Area Network (WAN) connection. The complexity of edge architectures often demands a granular and robust pre-deployment validation framework. If you do not have a lot of data, you can use the Augment Data button to create additional images using filters such as flip, blur, rotate, and so on. It is also important to note that the test suites can be heavily dependent on the use case, so they need to be fine tuned for the architecture model being used. Create a Helm Chart Repository using the following command. Gather and analyze sensor data on the edge, Edge computing architecture and use cases, Building and deploying a 5G network service for your edge apps, first article in this edge computing series, Managing Models in the Deep Learning Engine, next article in this edge computing series, Telecommunications, Media & Entertainment, Edge computing use case: Workplace safety on a factory floor, Creating a model using Maximo Visual Inspection, Containerizing the model using the Maximo Visual Inspection Inference server, Deploying our model to the edge servers using IBM Cloud Pak for Multicloud Management, Deploying the model from IBM Cloud Pak for Multicloud Management, Use the trained model to recognize hard hats using IBM Video Analytics, Register the device to IBM Edge Application Manager, Register patterns and deploy models to your edge device, Building out the edge in the application layer and device layer (this article). 5G telecom networks promise extreme mobile bandwidth, but to deliver, they require massive new and improved capabilities from the backbone infrastructures to manage the complexities, including critical traffic prioritization. Edit the chart.yaml file to specify the custom name and version (as you can see in the screen shot below). [IoT World, North America’s largest IoT event, is going virtual August 11-13 with a three-day virtual experience putting IoT, AI, 5G and edge into action across industry verticals. OpenStack is one of the top 3 most active open source projects and manages 15 million compute cores, Edge Computing: Next Steps in Architecture, Design and Testing, Edge Computing for Intelligent Aquaculture, Cloud Edge Computing: Beyond the Data Center, Single-root input/output virtualization (SR-IOV), SmartNics/Field-programmable gate array (FPGA), Challenges of managing a large number of edge data centers: Available functionality at the edge data center vs. orchestration overhead, Preparing the architecture to handle one failure at a time: e.g. The configuration needs to allow applications to continue running even in case of network outages if the use case requires the workload to be highly available, i.e. Bruce Jones, StarlingX Architect & Program Manager, Intel Corp. Adrien Lebre, Professor in Computer Science, IMT Atlantique / Inria / LS2N, David Paterson, Sr. , or the enterprise region run applications on the edge computing vs. 5G are... 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2020 edge computing architecture diagram