Openstack.org is powered by VEXXHOST. Now that the edge device is registered to IBM Edge Application Manager, we can register edge patterns from the exchange server. 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. Network edge computing architecture considerations Mobile operators are evaluating a range of factors when determining how they should build out their edge computing infrastructure: Application latency needs: most applications can tolerate latency of 100ms or more, but there are some that have a sub-50ms requirement, for example streaming virtual reality or for mission critical … 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. 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. 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. [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. 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. The behavior of the edge data centers in case of a network connection loss might be different based on the architectural models. The scope of fog computing starts from the outer edges where the data is collected to where it will be stored eventually. The first seemingly trivial step describing the acquisition of resources from a testbed is not specific to edge computing scenarios. They are the Centralized Control Plane and the Distributed Control Plane models. 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. The device layer consists of small devices running on the edge. Run the docker –version command to check your installed Docker version. Create a Helm Chart Repository using the following command. As the edge architectures are still in the early phase, it is important to be able to identify advantages and disadvantages of the characteristics for each model to determine the best fit for a given use case. If a distributed node becomes disconnected from the other nodes, there is a risk that the separated node might become non-functional. There are also new challenges due to the additional burden of running a large number of control functions across a geographically distributed environment that makes managing the orchestration type services more complex. In this article, we will describe how we implemented the network layer of the edge computing architecture for the workplace safety use case we introduced in Part 2. The most common example is when the location of the components of the identity management service are chosen based on the scenario along with one of the aforementioned methods to connect them. The following screen shot shows all four .yaml files that were created for our hardhat scenario. In addition, the configuration options are significantly different among the different models. When all the preparations are done, the next step is benchmarking the entire integrated framework. This is accomplished using IBM Cloud Pak for Multicloud Management. In general, the larger the data set, the better the accuracy of the model will be. 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 Computing has been a growing topic for the past few years. Benchmarking is often defined as performance testing, but here it applies to a broader scope that includes integration and functional testing as well. Configure a channel. 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. 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. Adaptability is crucial to evolve existing software components to fit into new environments or give them elevated functionality. The new architecture of edge computing drives a rethinking of computation offloading. To install the inference server on a machine, download the latest Maximo Visual Inspection Inference software. Edit the deployment.yaml file in the templates folder to add any additional parameters like GPUs in the resources section of yaml file. When an agreement is accepted, the corresponding containers can begin running. Repeat this step for all frames. 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. To implement the architecture, the following needs to happen: Models need to be trained to identify a person wearing a hard hat. Therefore, having a deployment tool that supports a declarative approach is preferred to specify the characteristics of the infrastructure such as latency, throughput and network packet loss ratio to emulate the targeted real life scenario and circumstances. Many applications move the data from the factory floor to a public or private cloud, but in many cases the latency impacts and transmission costs can lead to disruptions on the assembly line. Then, add a name and a scope as cluster, and then add the registry as IBM Cloud Pak for Multicloud Management Private repo . In many cases, the edge will be implemented where connectivity is not available or is not sufficient to meet the low latency requirements for the edge nodes. 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 assigned resources (e.g., compute, storage, network) represent the physical infrastructure that will be used to conduct the evaluation. After you’ve installed IBM Edge Application Manager on the server, these required packages are located in the following directory: /ibm-edge-computing-x86_64-/horizon-edge-packages/linux///. 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. Connectivity to the edge is a key component required to successfully implement the edge. Set up a camera view where a danger zone can be defined and a person can be detected when entering the defined area. 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. Stay up to date on OpenStack and the Open Infrastructure community. With the emergence of 5G as a technology transformation catalyst, companies are considering edge computing as part of their overall strategy. Deployment and testing requirements are further highlighted for these new architectural considerations, and therefore existing solutions need to be enhanced, customized and in some cases designed and implemented from scratch. Full Guide to Cloud Computing Architecture with Diagram Cloud Computing is an emerging technology. You’ll need to install and configure these key components of IBM Video Analytics: These components can be set up to run at the application layer on a single server. 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. “Key Requirements” are not intended to be a standard so they are not normative. To create an object detection model, click, Once you are done labeling the images, click, The deployed hardhat model now appears in the, Metadata Ingestion, Lookup, and Signaling. Foxconn is utilizing this reference architecture to deliver new solutions for industrial edge computing and private wireless applications. Video data can be processed at the edge, either at the application layer or the device layer. The IBM Cloud Pak for Multicloud Management, which runs on Red Hat OpenShift, provides consistent visibility, governance, and automation from on premises to the edge. of edge computing exist—edge computing means many things to many people. The complexity of the applications that can be run depends on the footprint of the edge server. Information from the device layer is sent to the application layer for further processing. This greatly reduces load on backbone networks while improving user experience. The architecture models also show required functionality for each site but do not discuss how to realize it with any specific solution such as Kubernetes, OpenStack, and so forth. Beth Cohen, Distinguished Member of Technical Staff, Verizon, Gergely Csatári, Senior Open Source Specialist, Nokia, Shuquan Huang, Technical Director, 99Cloud. The sizing of the servers is dependent on the workload that will be run. This document highlights the OSF Edge Computing Group’s work to more precisely define and test the validity of various edge reference architectures. Therefore, by only caching 20% of their content, service providers will have 80% of traffic being pulled from edge data centers. The architecture diagram below shows a detailed view of the edge data center with an automated system used to operate a shrimp farm. a Point of Sales system in a retail deployment or the industrial robots operating in an IoT scenario. For videos in your data set, you can use the Auto Capture button to capture frames at desired time intervals. In the following steps, we will go through the process of deploying these Docker images to IBM Cloud Private using the helm charts. While the management and orchestration services are centralized, this architecture is less resilient to failures from network connection loss. This can be challenging because most data center centric deployments treat compute nodes as failed resources when they become unreachable. Add image policies on the target cluster, which in our case is IBM Cloud Private. Now that the testbed is prepared and tested, the next step is to deploy the software applications on the infrastructure. This model still allows for the existence of small edge data centers with small footprints where there would be a limited amount of compute services, and the preference would be to devote the majority of the available resources to the workloads. The name ‘edge computing’ refers to computation around the corner/edge in a network diagram. The Top 8 Types of Cloud Architecture Diagram Cloud computing architecture typically consists of a front end platform, back end platforms, a cloud-based delivery, and a network infrastructure. Cars with autonomous driving capabilities need the brakes applied immediately or they run the risk of … To do so, first obtain the container’s ID and then commit the Docker image: Copy the container ID from the output, and specify it on this command: For example, for our hardhatmodel, the Docker commit command might look like this: Save the Docker image you created in the above step and zip it to create a .tgz file using the following command: You can now move this .tgz file to any other system and run a docker load command to load the Docker image onto that system. to advance next-generation edge computing solutions. Log in to the device, and run the following command to switch to a user that has root privileges: Verify that your Docker version is 18.06.01-ce or later. Edge Computing Architecture Edge computing is closely related to fog computing, where the goal is to keep certain processing capabilities and functionality closer to the edge nodes. A caching system can be as simple as a basic reverse-proxy or as complex as a whole software stack that not only caches content but provides additional functionality, such as video transcoding based on the user equipment (UE) device profile, location and available bandwidth. The central locations are typically well equipped to handle high volumes of centralized signaling and are optimized for workloads which control the network itself. Introduction to Fog Computing Architecture Fog architecture involves using services of end devices (switches, routers, multiplexers, etc) for computational, storage and processing purposes. In the case of edge architectures it is crucial to check functionality that is designed to overcome the geographical distribution of the infrastructure, especially in the circumstance where the configurations of the architectural models are fundamentally different. This is done using IBM Edge Application Manager. This operation should preferably be a functionality of the deployment tool. Configure an analytics profile. 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. To help with understanding the challenges, there are use cases from a variety of industry segments, demonstrating how the new paradigms for deploying and distributing cloud resources can use reference architecture models that satisfy these requirements. This element is usually located near a radio tower site with computational and storage capabilities. Now that we trained a model and deployed it to the edge server, you can now use that model to recognize hard hats. Digitalization has already provided much innovation, but there is still room for improvement, such as reducing the labor costs related to collecting data and improving data analysis to be faster and more reliable. In summary, this architecture model does not fulfill every use case, but it provides an evolution path to already existing architectures. 4 Edge Computing Reference Architecture 2.0 • Efficient flow and integration of information Currently, the industry has more than six industrial real … Edge computing is an emerging paradigm which uses local computing to enable analytics at the source of the data. 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. Make sure the file is transferred to IBM Cloud Pak for Multicloud Management. The highest focus is still on reducing latency and mitigating bandwidth limitations. However, the creation of more CDN nodes with regional points of presence (PoP) are one of the first examples of what can now be considered near-edge-computing. For example, the application layer could be built on Red Hat OpenShift and have one or more IBM Cloud Paks installed on it where deployed containers run. The checks can be as simple as using the ping command bi-directionally, verifying specific network ports to be open and so forth. Typically, building such architectures uses existing software components as building blocks from well-known projects such as OpenStack and Kubernetes. Copy the API key that is generated after running the above command: Confirm the node with the IBM Edge Application Manager. 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 Edge computing optimizes Internet devices and web applications by bringing computing closer to the source of the data. In recent prototypes, smart caching frameworks use an agent in the central cloud that redirects content requests to the optimum edge data center using algorithms based on metrics such as UE location and load on the given edge site. Principal Software Engineer, Dell Technologies, Ildikó Váncsa, Ecosystem Technical Lead, OpenStack Foundation. The models need to be integrated with a video analytics system. Maximo Visual Inspection Inference server is a server that lets you quickly and easily deploy multiple trained models. A larger set of use cases demands edge sites to be more fully functional on their own. 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 exact number of levels will depend on the size of the operator network. Set up at least one type of alert, such as a tripwire or a region alert to define the danger zone area. With the emergence of 5G as a technology transformation catalyst, companies are considering edge computing as part of their overall strategy. The application layer is likely built on a containers-based infrastructure where common software services and middleware can run. The local node can provide much faster feedback compared to performing all operations in the central cloud and sending instructions back to the edge data centers. In our case, the model is deployed to IBM Cloud Private. For systems built on environments such as OpenStack and Kubernetes services, frameworks like Kolla, TripleO, Kubespray or Airship are available as starting points. It incorporates multiple sub steps to prepare the physical infrastructure as well as the deployment of the system under test (SUT). The next step is to be able to deploy and test the solution to verify and validate its functionality and ensure it performs as expected. 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. Registering patterns on the device downloads the associated services and docker images that are required to run the corresponding models on the edge device. Example functions include: Further testing of the edge infrastructure needs to take the choice of architectural model into consideration: The final two steps are trivial. Signaling functions like the IMS control plane or Packet Core now rely on cloud architectures in large centralized data centers to increase flexibility and use hardware resources more efficiently. They can be extended or leveraged as examples of solutions that can be used to perform the above described process to evaluate some of the architecture options for edge. 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: . 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. However, there are common models that describe high-level layouts which become important for day-2 operations and the overall behavior of the systems. Yes, there are systems running in production that resemble at least some of the considerations—uCPE or vRAN deployments, for example. Edge architectures require a re-think of the design of the Base Band Unit (BBU) component. Let’s dive into the details of each of these two layers and the respective components in the layers. No matter which perspective, edge computing decentralizes and extends campus networks, cellular networks, data center networks, or the cloud. Further processing of the data collected by various sensors is done in the centralized cloud data center. Examples include smart thermostats, smart doorbells, home cameras or cameras on automobiles, and augmented reality or virtual reality glasses. But for our purposes, the most mature view of edge computing is that it is offering application developers and service providers cloud computing capabilities, as well 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. Configure your alerts. Firstly, task placement is not only two options, i.e., either at local or in the cloud, but possible on any edge node. 1. 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. These patterns and services are architecture specific. While edge computing has rapidly gained popularity over the past few years, there are still countless debates about the definition of related terms and the right business models, architectures and technologies required to satisfy the seemingly endless number of emerging use cases of this novel way of deploying applications over distributed networks. IBM Video Analytics is used to manage the video stream from a camera. Extending cloud services to edge devices is fog computing. The diagram below describes the general process that is executed when performing experimental campaigns. The architecture diagram below shows a detailed view of the edge data center with an automated system used to operate a shrimp farm. 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. The building blocks are already available to create edge deployments for OpenStack and Kubernetes. This graphic captures the four perspectives of edge computing. 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. The devices could handle analysis and real-time inferencing without involvement of the edge server or the enterprise region. While many edge devices are capable of running sophisticated workloads such as machine learning, video analytics and IoT services, if the workload is too large for the device layer, the workload should be placed at the application layer. Now, you need to package and publish the helm chart. When you are done configuring the components, restart IBM Video Analytics. The most common approach is to choose a layered architecture with different levels from central to regional to aggregated edge, or further out to access edge layers. These models and decisions are not specific to the technologies nor do they depend on the particular software solution chosen. 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. Use the deploy_zip_model.sh script to deploy a model exported from Maximo Visual Inspection on this system. Compute services incorporate running bare metal, containerized and virtualized workloads alike. To implement the use case, this edge device needs to be registered to IBM Edge Application Manager. For example, substitute the image file name and URL with your set up to run the following commands. 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. We can use a cloud architecture diagram defines the components as well as the relationships between them. However, aspects and tools that were considered during the development of the models include: There are other studies that cover similar architectural considerations and hold similar characteristics without being fully aligned with one model or the other. This puts data, compute, storage, and applications nearer to the user or IoT device where the data needs processing, thus creating a fog outside the centralized cloud and reducing the data transfer times necessary to … Industry 4.0 is often identified with the fourth industrial revolution. The Horizon agent must first complete a docker pull operation on each Docker container image. Related functions which are needed to execute the workload of the infrastructure are distributed between the central and the edge data centers. For instance, the system can pre-process water quality data from the monitoring sensors and send structured information back to the central cloud. An example of this is StarlingX, as its architecture closely resembles the distributed model. A tool to gather massive information from local “things” as an aggregation and control point. Testing is as much an art form as it is a precise engineering process. With more than a TFLOP/s of performance, Jetson TX2 is ideal for deploying advanced AI to remote field locations Further components are needed to ensure the ability to test more complex environments where growing numbers of building blocks are integrated with each other. These permutations of perspectives drive a paucity of aligned user stories to share with the OpenStack and StarlingX communities. In such cases, the key network components have to be deployed on the edge. To reduce load on the network, the video starts streaming when a person is detected. It is playing a major role in delivering scalable services in the day-to-day life of an Internet user. Depending on the situation, this might be considered more secure due to the centralized controllers, or less flexible because it might mean lost access by users at a critical juncture. As edge environments can be very complex, they also need to be tested for their ability to be prepared for circumstances such as an unreliable network connection. To describe what it all means in practice, take a Radio Access Network (RAN) as an example. Due to the constraints of this model, the nodes rely heavily on the centralized data center to carry the burden of management and orchestration of the edge compute, storage and networking services because they run all the controller functions. The following example illustrates a HardHat Tracking profile that will process analytics result from Maximo Visual Inspection, dump the result image in the specified directory, and trigger a tripwire alert if no white or blue hard hat were found. 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. This article discusses how the different layers come together using a use case that requires all three layers: application, device, and network. This is the perfect time for groups in the IT industry, both open groups and semi-open or closed consortiums, as well as standardization bodies, to collaborate on taking the next steps for architecture design and testing in order to be able to address the needs of the various edge computing use cases. The Linux Foundation-supported State of the Edge 2020 report noted that infrastructure to support edge computing is “nascent” and enterprises may have to implement their own until the technology matures. We will see how to build a hardhat detection model using Maximo Visual Inspection. Edge computing is highly dependent on lessons learned and solutions implemented in the cloud. 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. "Edge" is a term with varying definitions depending on the particular problem a deployer is attempting to solve. Here ’ s file system more than ever, edge computing ’ refers to computation around hardhat..., as its architecture closely resembles the distributed Control Plane and the downloads.: are they the same thing as industrial robots operating in an IoT scenario networks while improving experience! 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