Operators collect data from sensors all over the platform as part of a daily routine, measuring things like pressure, temperature, wave height, and other factors that affect operating capacity. It is recommended to review the Distributed Compute Node (DCN) deployment configuration of TripleO which is aligned with this model. The service is provisioned, and drones start capturing the video. As I stated earlier, you cant expect to install any old database for edge computing and achieve success. All edge computing architectures have an important requirement: using the right kind of database. 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. Third, work will need to be done on how best to break up workloads into sub-components to take advantage of the distributed architecture of edge computing. If a distributed node becomes disconnected from the other nodes, there is a risk that the separated node might become non-functional. Its a simple solution: eliminate the risks of a disaster by putting a data center on the oil drilling platform itself. 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. Also, the standard should allow for management of the full lifecycle of the application, from build through run and maintain. In this article, we will explain what edge computing is, describe relevant use cases for the telecommunications and media industry while describing the benefits for other industries, and finally present what an end-to-end architecture that incorporates edge computing can look like. In the cloud layer, you see a database server installed in the central data center, as well as the interconnected data centers across cloud regions. Analytic algorithms also detect and predict when a failure is likely to occur so that maintenance can be scheduled on the equipment between runs. 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. Advancement of many technologies like IoT, edge computing, and mobile connectivity has helped smart city solutions to gain popularity and acceptance among a citys citizens and governance alike. 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. Data intensive applications that require large amounts of data to be uploaded to the cloud can run more effectively by using a combination of 5G and edge computing. [] An Introduction to Edge Computing Architectures (Mark Gamble) []. The initial analysis and compute of the data can be executed within the vehicle. 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. In addition, your database needs to be embeddable. But edge computing is much more than simply installing a database at every level. While this depiction shows a single edge data center for simplicity, there could be n number of additional edge data centers to facilitate computing across a business ecosystem. To describe what it all means in practice, take a Radio Access Network (RAN) as an example. The checks can be as simple as using the ping command bi-directionally, verifying specific network ports to be open and so forth. Further processing of the data collected by various sensors is done in the centralized cloud data center. The good news is that edge computing is based on an evolution of an ecosystem of trusted technologies. And if the catastrophic happens and all network layers become unavailable, edge devices with embedded data processing serve as their own micro data centers, running in isolation with 100% availability and real-time responsiveness until connectivity is restored. The devices with applications need to operate within the network and the advent of 5G makes it even more compelling for these industries to start seriously considering edge computing. In future articles in this series, we will look at these application and network tools in more details. Edge computing is a distributed computing paradigm that brings computation and data storage closer to the sources of data. It takes too much time to collect data points on the component, send them to the cloud for processing, and then wait for a recommended course of action. Many major cloud service providers now offer edge computing services. For instance, the system can pre-process water quality data from the monitoring sensors and send structured information back to the central cloud. This element is usually located near a radio tower site with computational and storage capabilities. The numbers below refer to the numbers in Figure 6: As we continue to explore edge computing in upcoming articles, we will focus more and more on the details around edge computing, but lets remember that edge computing plays a key role as part of a strategy and architecture, an important part, but only one part. For a list of trademarks of The Linux Foundation, please see our Trademark Usage page. It incorporates multiple sub steps to prepare the physical infrastructure as well as the deployment of the system under test (SUT). 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. These are some of the key components that form the edge ecosystem: IoT sensors are fixed function equipment that collects and transmits data to an edge/cloud but does not have onboard compute, memory, and storage. I am a passionate product marketer with a technical and solution consulting background and 20+ years of experience in Enterprise and Open Source technology. For example, you might power POS systems for a chain of retail stores using edge data centers in each city where stores are concentrated. Is it about computing? The complexity of edge architectures often demands a granular and robust pre-deployment validation framework. This paradigm shift includes the use of open hardware and software components in the solutions. Typically, building such architectures uses existing software components as building blocks from well-known projects such as OpenStack and Kubernetes. Edge computing moves data processing and storage closer to applications and client devices by leveraging tiered, edge data centers along with embedded data storage directly on devices where appropriate. 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 Pareto Principle, or 80-20 rule, applies to video streaming; that is, 80% of customers will only consume 20% of the available content. Since this is a high-level discussion, the assumption is that there will be enough compute, storage and networking functionality to the edge to cover the basic needs; any specialized configurations or features are out of scope. The 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. Data storage should be integrated directly to the edge device in order to facilitate data processing when completely offline. Imagine an oil drilling platform in the middle of the North Sea. Critical time is lost. When planning out your own edge computing initiatives, you should only consider a database that meets all of the above data processing requirements. Duplicate components such as Industry Solutions/Apps exist in multiple nodes as certain workloads might be more suited to either the device edge or the local edge and other workloads might be dynamically moved between nodes under certain circumstances, either manually controlled or with automation in place. The test results need to be collected and evaluated, before returning the SUT infrastructure to its original state. These environments can be very fragile; therefore, it requires high precision to create and sustain healthy and balanced ecosystems. 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. 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. 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. Our next article in this series will dive deeper into the different layers and tools that developers need to implement an edge computing architecture. And your database plays a pivotal role in making it all happen. As soon as the camera recognizes a human in the video content, it will start transmitting the video to the local edge. Rapid response to manufacturing processes is essential to reduce product defects and improve efficiencies. IoT devices are the basic building blocks of any smart city solution. 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. 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. or monitors in an operating room? When you move the processing of critical data to the place where it happens, you solve the problems of latency and downtime. 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. 5G promises data speeds in excess of 20 Gbps and the ability to connect over a million devices per square kilometer. At some point in time, it is determined that a new model needs to be deployed to the edge device as new unexpected features begin to appear in the video so a new model is deployed. But (spoiler alert!) Therefore, by only caching 20% of their content, service providers will have 80% of traffic being pulled from edge data centers. For more information about signaling workloads, reference Chapter 2.1 of the CNTT Reference Model under Control Plane for a list of examples. 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. By harnessing and managing the compute power that is available on remote premises, such as factories, retail stores, warehouses, hotels, distribution centers, or vehicles, developers can create applications that: To move the application workload out to the edge, multiple edge nodes might be needed, as shown in Figure 1. The project is supported by the OpenInfra Foundation. They had to create their own extended infrastructure beyond the cloud, and they had to consider where that infrastructure would live: on premises? 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. 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. 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. Trying to create a one size fits all solution is impossible for edge use cases due to the very different application needs in various industry segments. With many standards in this ecosystem newly created or quickly evolving, it will be difficult to maintain technology decisions long term. The approach delivers the illusion of a single connected system without requiring intrusive changes. For systems built on environments such as OpenStack and Kubernetes services, frameworks like Kolla, TripleO, Kubespray or Airship are available as starting points. or 5G? 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. 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? When an item of interest is detected, it is sent to the local edge for further processing. 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. This use case is also a great example of where equipment is deployed and running in poor environmental conditions. Connected cars can gather data from various sensors within the vehicle including user behavior. While a focus of this article has been on application and analytics workloads, it should also be noted that network function is a key set of capabilities that should be incorporated into any edge strategy and thus our edge architecture. 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. These considerations are critical for applications that handle sensitive data, such as in healthcare or finance. Edge computing all about storing and processing data closer to the users and applications that consume it. This blog originally ran on the IBM website. The cloud data centers still serve a crucial role in an edge computing architecture because theyre the final repository of information. Finally, a database is embedded directly to select edge mobile and IoT devices, allowing them to keep processing, even in the event of total network failure. With the right tools in place to address management of these varied workloads along with their entire application lifecycle, it can be an easier task to introduce new devices or capabilities or replace existing devices as the technologies and standards evolve. Testing code on lower levels, such as unit tests or checking responses of components through API tests, is straightforward. This tiered approach insulates applications from central and regional data center outages. But were just getting started. Discussing and developing additional details around the requirements and solutions in integrating storage solutions and further new components into edge architectures is part of the future work of the OSF Edge Computing Group. Communications service providers (CSPs) can use edge computing and 5G to be able to route user traffic to the lowest latency edge nodes in a much more secure and efficient manner. openstack.org is powered by VEXXHOST. To address this challenge in a reasonable way, workloads can be prioritized based on a number of factors, including benefit of migration, complexity, and resource/time to migrate. With an edge computing architecture, users and devices always have speedy access to data, even in the event of internet latency or outage. Using video to identify key events is rapidly spreading across all domains and industries. 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. Your database must also have the ability to instantly replicate and synchronize data across database instances, whether theyre in the cloud or in an edge data center. These run on a local area network, which could be fiber, wireless, 5G or older networks such as 4G and earlier. Retailing on Black Friday? Testing is as much an art form as it is a precise engineering process. 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. These containers include visual analytics applications and network layer to manage the underlying network functionality required for the new service. Similarly to the telecommunication industry, manufacturing also has very strict requirements. harnessing the benefits of edge computing pretty much comes down to one thing: data where and how you process it, and how you flow it to and from the edge. in a private cloud? 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. 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. co-located? In the early days of edge computing, architects had to build it all from scratch. 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. Within the edge layer, you see individual devices, smart phones, tablets and laptops carried by users, as well as IoT devices that all communicate with the edge data center. This allows frameworks to be created that support running an automated unit test suite that addresses requirements such as repeatability, replicability and reproducibility. When all the preparations are done, the next step is benchmarking the entire integrated framework. 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. The central locations are typically well equipped to handle high volumes of centralized signaling and are optimized for workloads which control the network itself. 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.
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