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. That doesn’t mean that edge is dead. The Deep Learning Engine in IBM Video Analytics can run local models and remote Maximo Visual Inspection models. Create a Helm Chart Repository using the following command. 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. This architecture model is much more flexible in case of a network connection loss because all the required services to modify the workloads or perform user management operations are available locally. Interestingly, while cloud transformation started later in the telecom industry, operators have been pioneers in the evolution of cloud computing out to the edge. 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. Cars with autonomous driving capabilities need the brakes applied immediately or they run the risk of … This bringing of storage and computing nearer to the devices improves response time and lessens the To install the inference server on a machine, download the latest Maximo Visual Inspection Inference software. Then, add a name and a scope as cluster, and then add the registry as IBM Cloud Pak for Multicloud Management Private repo . This is especially true in edge architectures where resources must be available over complex networking topologies. With edge computing techniques, it is possible to build intelligent aquaculture infrastructure in order to introduce artificial intelligence and machine learning techniques that will optimize feeding strategy or reduce cost by minimizing human error and reacting faster to machine failures. 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. Configure the Deep Learning Engine in IBM Video Analytics to call the deployed model in Maximo Visual Inspection. [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. Make sure the file is transferred to IBM Cloud Pak for Multicloud Management. 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. 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 central locations are typically well equipped to handle high volumes of centralized signaling and are optimized for workloads which control the network itself. The creation of the agreements normally is received and accepted in less than a minute. The real challenge lies in efficient and thorough testing of the new concepts and evolving architecture models. The origins of edge computing lie in content delivery networks that were created in the late 1990s to serve web and video content from edge servers that were deployed close to users. 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? One common standard practice is the artifact review and badging approach. Then, click Configure and select the IBM Cloud Private that is linked to your IBM Cloud Pak for Multicloud Management. Your GitHub repo now has the helm package (.tgz file) and ththe index.yaml file. 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. This article discusses how the different layers come together using a use case that requires all three layers: application, device, and network. 1. Aquaculture is similar to agriculture, except that instead of domestic animals, it breeds and harvests fish, shellfish, algae and other organisms that live in a variety of salt or freshwater environments. Therefore, by only caching 20% of their content, service providers will have 80% of traffic being pulled from edge data centers. 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. Video data can be processed at the edge, either at the application layer or the device layer. 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. 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 approach delivers the illusion of a single connected system without requiring intrusive changes. Otherwise, no alert is issued. Stay up to date on OpenStack and the Open Infrastructure community. The Edge computing architecture highlights the three industries that drive IBM edge solutions: telecommunications, industrial, and retail. 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///. 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. The figure below shows sample screens for a HardHat Tracking analytic profile being registered and assigned and how a tripwire alert can be configured to define an area of interest. Devices can be small. In general, the larger the data set, the better the accuracy of the model will be. In your browser for IBM Cloud Pak for Multicloud Management, navigate to Manage > Helm Repositories > Add Repository > . The application layer runs on the local edge and has greater compute power than the device layer. Edge Computing is an additional tier between Cloud and the Devices. Connectivity to the edge is a key component required to successfully implement the edge. We can use a cloud architecture diagram defines the components as well as the relationships between them. If you set aside the geographically distributed nature, this approach faces very similar challenges as operating large-scale data centers. Fog computing refers to decentralizing a computing infrastructure by extending the cloud through the placement of nodes strategically between the cloud and edge devices. It is also used to define an object to detect as well as the area to designate as a danger zone. 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. is powered by VEXXHOST. Optionally, you can unregister the current running pattern such that you can deploy a different pattern. For instance, using the OpenStack Identity Management service (Keystone) to locate it into an edge deployment without the limitation of technologies as its API supports both OpenStack and Kubernetes or the combination of both. Introduction to Fog Computing Architecture Fog architecture involves using services of end devices (switches, routers, multiplexers, etc) for computational, storage and processing purposes. With the YOLO model deployed on the TX2, whenever the camera detects a person, we can start video streaming to the server. As discussed earlier, there is no single solution that would fulfill every need. The devices could handle analysis and real-time inferencing without involvement of the edge server or the enterprise region. With IBM Edge Application Manager, you can quickly, autonomously, and securely deploy and manage enterprise application workloads at the edge and at massive scale. Testing is as much an art form as it is a precise engineering process. In order to ensure stable and trustable outcomes it is recommended to look into the best practices of the scientific community to find the most robust solution. Some Linux distributions can be set up to run older Docker versions. These permutations of perspectives drive a paucity of aligned user stories to share with the OpenStack and StarlingX communities. Set up at least one type of alert, such as a tripwire or a region alert to define the danger zone area. The diagram below describes the general process that is executed when performing experimental campaigns. 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 test results need to be collected and evaluated, before returning the SUT infrastructure to its original state. This command automatically generates sample yaml files including chart.yaml, values.yaml, service.yaml, and deployment.yaml. A larger set of use cases demands edge sites to be more fully functional on their own. In addition, the configuration options are significantly different among the different 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 Distributed Control Plane model defines an architecture where the majority of the control services reside on the large/medium edge data centers. By Eric Gose, Julio Wong, Mathews Thomas, Sai Srinivas Gorti, Sharath Prasad, Tass Supakkul, Utpal Mangla Updated May 27, 2020 | Published May 4, 2020, The first article in this edge computing series described a high-level edge computing architecture that identified the key layers of the edge including the device layer, application layer, network layer, and the cloud edge layer. Testing the integrated systems to emulate the configuration and circumstances of production environments can be quite challenging. Principal Software Engineer, Dell Technologies, Ildikó Váncsa, Ecosystem Technical Lead, OpenStack Foundation. This use case is also a great example of where equipment is deployed and running in poor environmental conditions. 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. Our next article in this edge computing series dives deeper into the network edge and the tooling that is needed to implement it. Log on to Maximo Visual Inspection, and click on. Add the private repo and the ca.crt file on the target cluster’s file system. Edge computing optimizes Internet devices and web applications by bringing computing closer to the source of the data. Building an edge infrastructure consists of various well known components that were not implemented specifically for edge use cases originally. When you are done configuring the components, restart IBM Video Analytics. When all the preparations are done, the next step is benchmarking the entire integrated framework. This graphic captures the four perspectives of edge computing. They are the Centralized Control Plane and the Distributed Control Plane models. Some of the system functions and elements that need to be taken into consideration include: By automating and connecting these farms, the solution minimizes the isolation that exists in this industry. For more information about signaling workloads, reference Chapter 2.1 of the CNTT Reference Model under Control Plane for a list of examples. Yes, there are systems running in production that resemble at least some of the considerations—uCPE or vRAN deployments, for example. Display the property values set for the helm chart by using the helm template command: Change my-app to be whatever you used for your helm chart repository name. The following screen shot shows all four .yaml files that were created for our hardhat scenario. 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. It is playing a major role in delivering scalable services in the day-to-day life of an Internet user. The concept is that factories are using computers and automation in new ways by incorporating autonomous systems and machine learning to make smarter factories. Repeat this step for all frames. 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. The checks can be as simple as using the ping command bi-directionally, verifying specific network ports to be open and so forth. Run the docker –version command to check your installed Docker version. The system can also pre-filter data before sending it to the central cloud for further processing. 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. 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. 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. Content delivery networks (CDN) are not a new concept. 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. The name ‘edge computing’ refers to computation around the corner/edge in a network diagram. Log in to the target cluster’s IBM Cloud Private, and navigate to Manage > Resource Security > Image Policies > Add Image Policy. Make sure to include varied scenarios with different lighting conditions. This allows frameworks to be created that support running an automated unit test suite that addresses requirements such as repeatability, replicability and reproducibility. 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. Foxconn is utilizing this reference architecture to deliver new solutions for industrial edge computing and private wireless applications. 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. 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 covered two key components of the edge: the application layer and the device layer. In order to use any model in IBM Visual Analytics, the model must be configured in the Deep Learning Engine configuration files that include docker compose YAML, nginx, and JSON for each model as shown in the figure below. 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 If necessary, update to the current version of Docker by running the following commands: Install the Open Horizon agent on the device. Edge architectures require a re-think of the design of the Base Band Unit (BBU) component. To reduce load on the network, the video starts streaming when a person is detected. We will see how to build a hardhat detection model using Maximo Visual Inspection. If a person is not wearing a hard hat, IBM Video Analytics fires an alert. It incorporates multiple sub steps to prepare the physical infrastructure as well as the deployment of the system under test (SUT). : Network connection loss or degradation to the central or regional data center, Providing minimal viable functionality on small footprints, create/delete a resource (user, flavor, image, etc); scope: one or more edge sites, list instances (VM, container); scope: an edge site or ‘single pane of glass’ dashboard, create resources for cross-data-center networks. 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. 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. 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. 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 approach is to choose a layered architecture with different levels from central to regional to aggregated edge, or further out to access edge layers. In our previous white paper the OSF Edge Computing Group defined cloud edge computing as resources and functionality delivered to the end users by extending the capabilities of traditional data centers out to the edge, either by connecting each individual edge node directly back to a central cloud or several regional data centers, or in some cases connected to each other in a mesh. The models need to be containerized and deployed to the edge. Industry 4.0 is often identified with the fourth industrial revolution. 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. 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. This approach reduces the need to bounce data back and forth between the cloud The Edge Servers can be in the same or different physical locations. Devices can also be large, such as industrial robots, automobiles, smart buildings, and oil platforms. This means they are more resilient to network connectivity issues as well as being able to minimize disruption caused by latency between edge sites. 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. The scope of fog computing starts from the outer edges where the data is collected to where it will be stored eventually. 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. See the installation documentation for detailed instructions. 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. How does this help? The first seemingly trivial step describing the acquisition of resources from a testbed is not specific to edge computing scenarios. Edge Computing Architecture is a new model for providing storage and substantial computing properties near to the devices. 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 … 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. After the container images for the agreement are downloaded and verified, an appropriate Docker network is created for the images. These architectural changes introduce new challenges for the lifecycle of the building blocks: Reducing backhaul and latency metrics and improving quality of service (QoS) are good reasons for pushing content caching and management out to the network edge. This section describes shrimp farms, which are controlled ecosystems where humans and automated tools oversee the entire lifecycle of the animals from the larva phase to the fully grown harvestable stage. Configure an analytics profile. We will also explore some of the differentiating requirements and ways to architect the systems so they do not require a radically new infrastructure just to comply with the requirements. Set up a new or update an existing AnalyticProfile for tracking whether a person is wearing a hard hat. The behavior of the edge data centers in case of a network connection loss might be different based on the architectural models. There are different options that can be used to overcome the operational challenges of this model. For the Centralized Control Plane model, the edge infrastructure is built as a traditional single data center environment which is geographically distributed with WAN connections between the controller and compute nodes. 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. OpenStack is one of the top 3 most active open source projects and manages 15 million compute cores     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. Bruce Jones, StarlingX Architect & Program Manager, Intel Corp. Adrien Lebre, Professor in Computer Science, IMT Atlantique / Inria / LS2N, David Paterson, Sr. 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. Further processing of the data collected by various sensors is done in the centralized cloud data center. The complexity of edge architectures often demands a granular and robust pre-deployment validation framework. 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. 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. With the emergence of 5G as a technology transformation catalyst, companies are considering edge computing as part of their overall strategy. This is accomplished using IBM Cloud Pak for Multicloud Management. 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. 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. Edge computing is highly dependent on lessons learned and solutions implemented in the cloud. These environments can be very fragile; therefore, it requires high precision to create and sustain healthy and balanced ecosystems. 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. As can be seen from these discussions, edge computing related innovation and software evolution is still very much in its early stages. After four years, while there is no question that there is continuing interest in edge computing, there is little consensus on a standard edge definition, solution or architecture. On the plus side, it provides a centralized view of the infrastructure as a whole, which has its advantages from an operational perspective. Published in: 2017 IEEE International Conference on Edge Computing (EDGE) Now that the testbed is prepared and tested, the next step is to deploy the software applications on the infrastructure. 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. A tool to gather massive information from local “things” as an aggregation and control point. 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. 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. to advance next-generation edge computing solutions. a Point of Sales system in a retail deployment or the industrial robots operating in an IoT scenario. Caching systems in edge environments need to take end user device (EUD) proximity, system load and additional metrics as factors in determining which edge data center will deliver the payloads to which endpoints. We will now look at how products such as Maximo Visual Inspection, Multi Cloud Manager, IBM Video Analytics and IBM Edge Application Manager can be used to create a full end to end solution. Let’s dive into the details of each of these two layers and the respective components in the layers. 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. Then, use the command line interface to verify that the Deep Learning Engine can call Maximo Visual Inspection successfully. This section covers two common high-level architecture models that show the two different approaches. Edge computing is an emerging paradigm which uses local computing to enable analytics at the source of the data. 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. Install the copied Horizon Debian packages by running the one of the following commands (which show our TX2 device): Point your edge device horizon agent to IBM Edge Application Manager by creating or editing /etc/default/horizon with this content (substituting the value for $ICP_URL that you used above): Edit the following values with their respective values: Restart the agent by running the following command: Verify the agent is running and properly configured by issuing these commands: Set these environment variables. Configure your alerts. The amount of data processing and computational power needed to support these technologies is increasing by orders of magnitude. 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. It is recommended to review the Distributed Compute Node (DCN) deployment configuration of TripleO which is aligned with this model. Edge Computing Edge Computing There are three primary applications of Edge Computing we will discuss in this white paper. 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, 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.
2020 edge computing architecture diagram