Firstly, task placement is not only two options, i.e., either at local or in the cloud, but possible on any edge node. 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. 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. Beth Cohen, Distinguished Member of Technical Staff, Verizon, Gergely Csatári, Senior Open Source Specialist, Nokia, Shuquan Huang, Technical Director, 99Cloud. "Edge" is a term with varying definitions depending on the particular problem a deployer is attempting to solve. This section covers two common high-level architecture models that show the two different approaches. Interestingly, while cloud transformation started later in the telecom industry, operators have been pioneers in the evolution of cloud computing out to the edge. 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. The Edge Servers can be in the same or different physical locations. 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. 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. The real challenge lies in efficient and thorough testing of the new concepts and evolving architecture models. The network connectivity between the edge nodes requires a focus on availability and reliability, as opposed to bandwidth and latency. 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 The systems even follow the transportation of the shrimp after they are harvested. Edge computing is an emerging paradigm which uses local computing to enable analytics at the source of the data. This setup allows more flexibility in managing the CU and DU while keeping the bandwidth utilization optimal, fulfilling the increasing user demands. This is encouraging content providers to migrate from a traditional, regional PoP CDN model to edge-based intelligent and transparent caching architectures. 3. (The third article in this series will cover the network layer.). 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. 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. The test results need to be collected and evaluated, before returning the SUT infrastructure to its original state. 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. Connectivity to the edge is a key component required to successfully implement the edge. : 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. [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. 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 IBM Cloud Pak for Multicloud Management, which runs on Red Hat OpenShift, provides consistent visibility, governance, and automation from on premises to the edge. 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. 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 complexity of edge architectures often demands a granular and robust pre-deployment validation framework. 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. 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. Make sure the file is transferred to IBM Cloud Pak for Multicloud Management. 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. 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. Edge computing pushes all the significant computational processing power towards the edges of the mesh. Edge Computing is an additional tier between Cloud and the Devices. The creation of the agreements normally is received and accepted in less than a minute. 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. An edge pattern is a descriptor file that describes which docker images to be downloaded and how they should be run on the device. Create a public GitHub repo and clone it to your local folder. 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 To reduce load on the network, the video starts streaming when a person is detected. 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. 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. With the YOLO model deployed on the TX2, whenever the camera detects a person, we can start video streaming to the server. In our case, the model is deployed to IBM Cloud Private. 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. 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. Edit the chart.yaml file to specify the custom name and version (as you can see in the screen shot below). How Edge Computing Is Evolving An example of this is StarlingX, as its architecture closely resembles the distributed model. The approach delivers the illusion of a single connected system without requiring intrusive changes. Set up a new or update an existing AnalyticProfile for tracking whether a person is wearing a hard hat. Information from the device layer is sent to the application layer for further processing. 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. “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. 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. For more information, see the IBM Video Analytics documentation on Managing Models in the Deep Learning Engine. These permutations of perspectives drive a paucity of aligned user stories to share with the OpenStack and StarlingX communities. 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. 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. This use case is also a great example of where equipment is deployed and running in poor environmental conditions. 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. If necessary, update to the current version of Docker by running the following commands: Install the Open Horizon agent on the device. Edge Computing Architecture is a new model for providing storage and substantial computing properties near to the devices. 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. If a distributed node becomes disconnected from the other nodes, there is a risk that the separated node might become non-functional. 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 system can also pre-filter data before sending it to the central cloud for further processing. Adaptability is crucial to evolve existing software components to fit into new environments or give them elevated functionality. The second phase is more difficult. 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. 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. Industry 4.0 is often identified with the fourth industrial revolution. 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. Configure your alerts. In such cases, the key network components have to be deployed on the edge. Our next article in this edge computing series dives deeper into the network edge and the tooling that is needed to implement it. The application layer runs on the local edge and has greater compute power than the device layer. 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. It is recommended to review the Distributed Compute Node (DCN) deployment configuration of TripleO which is aligned with this model. These environments can be very fragile; therefore, it requires high precision to create and sustain healthy and balanced ecosystems. 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. Testing is as much an art form as it is a precise engineering process. Therefore, by only caching 20% of their content, service providers will have 80% of traffic being pulled from edge data centers. 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. As owners of the network, telecom infrastructure is a key underlying element in edge architectures. The checks can be as simple as using the ping command bi-directionally, verifying specific network ports to be open and so forth. To implement the use case, this edge device needs to be registered to IBM Edge Application Manager. When you are done configuring the components, restart IBM Video Analytics. Edge computing optimizes Internet devices and web applications by bringing computing closer to 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. Foxconn is utilizing this reference architecture to deliver new solutions for industrial edge computing and private wireless applications. A tool to gather massive information from local “things” as an aggregation and control point. In our use case, we are using Jetson TX2 as the smart camera. Configure an analytics profile. Openstack.org is powered by VEXXHOST. 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. Now more than ever, edge computing has the promise for a very bright future indeed! 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. Navigate to the Catalog, search for and click on your chart name. IBM Video Analytics is used to manage the video stream from a camera. Your GitHub repo now has the helm package (.tgz file) and ththe index.yaml file. 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. 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. 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. This section will guide you through some use cases to demonstrate how edge computing applies to different industries and highlight the benefits it delivers. 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. 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. 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. 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. 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 … We covered two key components of the edge: the application layer and the device layer. As discussed earlier, there is no single solution that would fulfill every need. Testing can help with both enhancing architectural considerations as well as identifying shortcomings of different solutions. This minimizes the need for long distance communications between client and server, which reduces latency and bandwidth usage. Devices can also be large, such as industrial robots, automobiles, smart buildings, and oil platforms. Models need to be deployed to the camera to identify a human which will trigger the camera to start streaming. See the installation documentation for detailed instructions. In addition, the configuration options are significantly different among the different models. This is accomplished using IBM Maximo Visual Inspection. Edge Computing has been a growing topic for the past few years. Make sure to include varied scenarios with different lighting conditions. This is especially true in edge architectures where resources must be available over complex networking topologies. Principal Software Engineer, Dell Technologies, Ildikó Váncsa, Ecosystem Technical Lead, OpenStack Foundation. Edge must be by its very nature highly adaptable. 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. 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 "last-mile" must become increasingly shorter to meet customer demand for better performance and user experience with these applications that are highly sensitive to network latency. This document highlights the OSF Edge Computing Group’s work to more precisely define and test the validity of various edge reference architectures. Edit the deployment.yaml file in the templates folder to add any additional parameters like GPUs in the resources section of yaml file. The exact number of levels will depend on the size of the operator network. Typically, building such architectures uses existing software components as building blocks from well-known projects such as OpenStack and Kubernetes. This agreement status shows the hand-off between the device and exchange server. Edge computing reference architecture The edge computing architecture has four regions: the device region, the edge server/gateway region, the edge network or micro data center, and the enterprise hybrid multicloud region. 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. Define a reference architecture for edge and far edge deployments including OpenStack services and other open source components as building blocks. 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. To implement out use case, the hardhat model that you created in the previous section needs to be deployed to the edge servers. 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. This can be challenging because most data center centric deployments treat compute nodes as failed resources when they become unreachable. Add the helm repository to 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 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. Similarly to the telecommunication industry, manufacturing also has very strict requirements. On the device layer, any tools or components must be able to manage workloads placed across clusters and the device edge. 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. 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. Set up a camera view where a danger zone can be defined and a person can be detected when entering the defined area. This operation should preferably be a functionality of the deployment tool. 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. 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). For each frame, click Box, and choose the hardhat object that you just created, and draw a box around the hardhat. 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. One common standard practice is the artifact review and badging approach. After the container images for the agreement are downloaded and verified, an appropriate Docker network is created for the images. These patterns and services are architecture specific. The architecture diagram below shows a detailed view of the edge data center with an automated system used to operate a shrimp farm. This greatly reduces load on backbone networks while improving user experience. 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 diagram below describes the general process that is executed when performing experimental campaigns. 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. 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. It is playing a major role in delivering scalable services in the day-to-day life of an Internet user. Edge architectures require a re-think of the design of the Base Band Unit (BBU) component. Finally, Navigate to Workloads > helm release section to find your release. The models need to be integrated with a video analytics system. 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. Published in: 2017 IEEE International Conference on Edge Computing (EDGE) For instance, the system can pre-process water quality data from the monitoring sensors and send structured information back to the central cloud. Learn more. This graphic captures the four perspectives of edge computing. Reusable portable microservices located at the edge nodes fulfill tasks that are part of new vision applications or deep learning mechanisms. Run the docker –version command to check your installed Docker version. 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. As can be seen from these discussions, edge computing related innovation and software evolution is still very much in its early stages. Fog computing refers to decentralizing a computing infrastructure by extending the cloud through the placement of nodes strategically between the cloud and edge devices. Some edge sites might only have containerized workloads while other sites might be running VMs. This bringing of storage and computing nearer to the devices improves response time and lessens the 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. Let’s dive into the details of each of these two layers and the respective components in the layers. This allows frameworks to be created that support running an automated unit test suite that addresses requirements such as repeatability, replicability and reproducibility. The diagram above shows that all of the key control functionality is located in the central site, including all identity management and orchestration functions. 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. 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. The video analytics system needs to be able to manage the video stream, determine if the individual is in a danger zone, then call the hard hat model to determine if an individual is wearing a hard hat and fire an alert accordingly. 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. 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. 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. The Edge computing architecture highlights the three industries that drive IBM edge solutions: telecommunications, industrial, and retail. 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. 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. Is fog computing file that describes which Docker images to be trained to identify human! 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