As edge evolves, more industries find it relevant, which only brings fresh requirements or gives existing ones different contexts, attracting new parties to solve these challenges. Now more than ever, edge computing has the promise for a very bright future indeed! Learn more. 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. Here’s an architecture diagram showing these 4 components: The application layer enables you to run applications on the edge. Bruce Jones, StarlingX Architect & Program Manager, Intel Corp. Adrien Lebre, Professor in Computer Science, IMT Atlantique / Inria / LS2N, David Paterson, Sr. This can be challenging because most data center centric deployments treat compute nodes as failed resources when they become unreachable. 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. Further components are needed to ensure the ability to test more complex environments where growing numbers of building blocks are integrated with each other. In this section, we will go through steps involved in installing the Open Horizon agent on our device and registering the device to IBM Edge Application Manager Exchange so that we can deploy models on the device. 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 OpenStack project is provided under the Apache 2.0 license. Publish the helm chart from IBM Cloud Pak for Multicloud Management to IBM Cloud Private. That doesnât mean that edge is dead. In addition, the configuration options are significantly different among the different models. No matter which perspective, edge computing decentralizes and extends campus networks, cellular networks, data center networks, or the cloud. The purpose of this procedure is to ensure that the deployment step will be completed successfully and result in a test environment that is aligned with the requirements and plans. 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. Edge computing is highly dependent on lessons learned and solutions implemented in the cloud. This paradigm shift includes the use of open hardware and software components in the solutions. The diagram above shows that all of the key control functionality is located in the central site, including all identity management and orchestration functions. 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. Otherwise, no alert is issued. Add the private repo and the ca.crt file on the target cluster’s file system. "Edge" is a term with varying definitions depending on the particular problem a deployer is attempting to solve. Set up a camera view where a danger zone can be defined and a person can be detected when entering the defined area. 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. [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. Then, the containers can run. Now that we trained a model and deployed it to the edge server, you can now use that model to recognize hard hats. Testing the integrated systems to emulate the configuration and circumstances of production environments can be quite challenging. Run the following commands to register your device to IBM Edge Application Manager to register the services, patterns, and policies. How does this help? New test cases need to be identified along with values that are representative to typical circumstances and system failures. We can use a cloud architecture diagram defines the components as well as the relationships between them. Edge Computing Architecture is a new model for providing storage and substantial computing properties near to the devices. With the explosion of video streaming, online gaming and social media, combined with the roll-out of 5G mobile networks, the need to push caching out to the far-edge has increased dramatically. 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. 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. The building blocks are already available to create edge deployments for OpenStack and Kubernetes. 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. The architecture diagram below shows a detailed view of the edge data center with an automated system used to operate a shrimp farm. Now, you need to package and publish the helm chart. Some of the system functions and elements that need to be taken into consideration include: 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. Information from the device layer is sent to the application layer for further processing. 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. There are different options that can be used to overcome the operational challenges of this model. 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. While the management and orchestration services are centralized, this architecture is less resilient to failures from network connection loss. The models need to be containerized and deployed to the edge. There are still potential obstacles, such as not having all the images available locally due to limitations of storage and cache sizes. Once the deployment plan has been created and the resources have been selected, it needs to be confirmed that the infrastructure is configured correctly during the pre-deployment phase before installing the applications and services on top. This can be useful if you are running this engine on a system without a GPU and you have installed Maximo Visual Inspection on a separate system with a GPU. When all the preparations are done, the next step is benchmarking the entire integrated framework. Edit the deployment.yaml file in the templates folder to add any additional parameters like GPUs in the resources section of yaml file. Some edge sites might only have containerized workloads while other sites might be running VMs. The concept is that factories are using computers and automation in new ways by incorporating autonomous systems and machine learning to make smarter factories. In addition the Identity Provider (IdP) service can either be placed in the central data center or remotely with connection to the identity management service which limits user management and authentication. 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. Run the docker –version command to check your installed Docker version. They are the Centralized Control Plane and the Distributed Control Plane models. To fulfill the control systemsâ real-time and functional safety needs, they can use technologies such as Time Sensitive Networking (TSN) on the lower layers of the architecture. 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). Foxconn is utilizing this reference architecture to deliver new solutions for industrial edge computing and private wireless applications. For example, substitute the image file name and URL with your set up to run the following commands. As use cases evolve into more production deployments, the common characteristics and challenges originally documented in the âCloud Edge Computing: Beyond the Data Centerâ white paper remain relevant. An edge pattern is a descriptor file that describes which docker images to be downloaded and how they should be run on the device. to advance next-generation edge computing solutions. In the following steps, we will go through the process of deploying these Docker images to IBM Cloud Private using the helm charts. This section will guide you through some use cases to demonstrate how edge computing applies to different industries and highlight the benefits it delivers. Video data can be processed at the edge, either at the application layer or the device layer. Testing is as much an art form as it is a precise engineering process. This allows frameworks to be created that support running an automated unit test suite that addresses requirements such as repeatability, replicability and reproducibility. 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. Log in to the target cluster’s IBM Cloud Private, and navigate to Manage > Resource Security > Image Policies > Add Image Policy. This approach reduces the need to bounce data back and forth between the cloud Interestingly, while cloud transformation started later in the telecom industry, operators have been pioneers in the evolution of cloud computing out to the edge. This process, that is applied in the field of research, can also be utilized to help build new components and solutions that fit the requirements of edge computing use cases even though some of the steps still need more tools to perform all checks as if they were simple unit tests. As owners of the network, telecom infrastructure is a key underlying element in edge architectures. 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. 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. In your browser for IBM Cloud Pak for Multicloud Management, navigate to Manage > Helm Repositories > Add Repository > . For systems built on environments such as OpenStack and Kubernetes services, frameworks like Kolla, TripleO, Kubespray or Airship are available as starting points. 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. Use all lowercase letters for its name. There are hybrid solutions on the market that try to leverage the best of both worlds by deploying full installations in the central nodes as well as large/medium edge data centers and have an orchestration type service on top, such as ONAP, an orchestration tool used in the telecom industry. Adaptability is crucial to evolve existing software components to fit into new environments or give them elevated functionality. 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 … 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. Configure an analytics profile. The creation of the agreements normally is received and accepted in less than a minute. 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. The network connectivity between the edge nodes requires a focus on availability and reliability, as opposed to bandwidth and latency. What is edge computing? 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. 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. 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. If a person is not wearing a hard hat, IBM Video Analytics fires an alert. With the emergence of 5G as a technology transformation catalyst, companies are considering edge computing as part of their overall strategy. Edge Computing Perspectives: Architectures, Technologies, and Open Security Issues Abstract: Edge and Fog Computing will be increasingly pervasive in the years to come due to the benefits they bring in many specific use-case scenarios over traditional Cloud Computing. 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. In such cases, the key network components have to be deployed on the edge. Testing code on lower levels, such as unit tests or checking responses of components through API tests, is straightforward. 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. 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. 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 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. 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. 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. In our use case, we are using Jetson TX2 as the smart camera. Be aware that the majority of these tools are designed with the limitations of one datacenter as their scope, which means that there is an assumption that the environment can scale further during operation, while edge infrastructures are geographically distributed and often have limited resources in the remote nodes. 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. To implement the use case, this edge device needs to be registered to IBM Edge Application Manager. 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. Then, click Configure and select the IBM Cloud Private that is linked to your IBM Cloud Pak for Multicloud Management. How Edge Computing Is Evolving The behavior of the edge data centers in case of a network connection loss might be different based on the architectural models. 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. “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. 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. Edge Computing is an additional tier between Cloud and the Devices. On the plus side, it provides a centralized view of the infrastructure as a whole, which has its advantages from an operational perspective. 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. These environments can be very fragile; therefore, it requires high precision to create and sustain healthy and balanced ecosystems. Create a Helm Chart Repository using the following command. Building an edge infrastructure consists of various well known components that were not implemented specifically for edge use cases originally. We will see how to build a hardhat detection model using Maximo Visual Inspection. 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. The Pareto Principle, or 80-20 rule, applies to video streaming; that is, 80% of customers will only consume 20% of the available content. Therefore, by only caching 20% of their content, service providers will have 80% of traffic being pulled from edge data centers. Edit the values.yaml file to update the Docker image and node port information (as you can see in the screen shot below). However, there are common models that describe high-level layouts which become important for day-2 operations and the overall behavior of the systems. 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. When the containers are running, you can view the container image status by running the docker ps command. 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 exact number of levels will depend on the size of the operator network. 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. 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. 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. On the device layer, any tools or components must be able to manage workloads placed across clusters and the device edge. 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. Make sure to include varied scenarios with different lighting conditions. 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. Some of this information can then be sent to the cloud or other location. 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. 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. If a distributed node becomes disconnected from the other nodes, there is a risk that the separated node might become non-functional. 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. This command automatically generates sample yaml files including chart.yaml, values.yaml, service.yaml, and deployment.yaml. Some Linux distributions can be set up to run older Docker versions. arXiv:1702.05309v2 [cs.IT] 13 Mar 2017 Mobile Edge Computing: A Survey on Architecture and Computation Ofﬂoading Pavel Mach, IEEE Member, Zdenek Becvar, IEEE Member Abstract—Technological evolution of mobile user a Point of Sales system in a retail deployment or the industrial robots operating in an IoT scenario. 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. This minimizes the need for long distance communications between client and server, which reduces latency and bandwidth usage. The highest focus is still on reducing latency and mitigating bandwidth limitations. We will use the inference server to create a docker image of the hardhat model. 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 must be by its very nature highly adaptable. In this article, we dive deeper into the application and device layers, and describe the tools you need to implement these layers. This enables it to provide the extreme high bandwidth required between the radio equipment and the applications or to fulfill demands for low latency. 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. An example of this is StarlingX, as its architecture closely resembles the distributed model. 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. 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. 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 edge data center doesn't have full autonomy, therefore distributing configuration changes might fail if there is lost access to the image library or the identity management service. The diagram below describes the general process that is executed when performing experimental campaigns. This is accomplished using IBM Maximo Visual Inspection. 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. 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. The complexity of the applications that can be run depends on the footprint of the edge server. The sizing of the servers is dependent on the workload that will be run. Plus, it also suits the needs of scenarios where autonomous behavior is not a requirement. Further processing of the data collected by various sensors is done in the centralized cloud data center. From a birdâs eye view, most of those edge solutions look loosely like interconnected spider webs of varying sizes and complexity. Package your helm chart into a .tgz file. It is playing a major role in delivering scalable services in the day-to-day life of an Internet user.
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