Various industrial organizations and commercial organizations are actively initiating and promoting research, standardization, and industrialization of edge computing. The architecture has a proper interface to connect with the cloud datacenters, for permanent data storage and other cloud-based services. Workloads on the edge, both local and device workloads, will interact with workloads on these clouds. By leveraging open edge computing solutions, it is now possible to create data-driven retail solutions that augment existing assets rather than replace . Rather than send data to a cloud server or main data center to be processed, move it closer to the population consuming it. Edge gateways are employed to provide interfaces to wired and radio-based transmissions. the edge computing provides only limited computational and storage resources with respect to the MCC. Developing new services that take advantage of emerging technologies like edge computing and 5G will help generate more revenue for many businesses today, but especially for telecommunications and media companies. Cloud, or the nexus of your environment, where everything comes together that needs to come together Figure 4 represents an architecture overview of these details with the local edge broken out to represent the workloads. Edge comprises of those devices which can perform temporary data processing and temporary storage before sending the actual data to the cloud for further storage and processing. An edge gateway is typically an edge cluster/server which, in addition to being able to host enterprise application workloads and shared services, also has services that perform network functions such as protocol translation, network termination, tunneling, firewall protection, or wireless connection. The ‘Edge’ refers to having computing infrastructure closer to the source of data. The IoT has introduced a virtually infinite number of endpoints to commercial networks. Photo: Stephen Gossett Photo: Stephen Gossett What Is Edge Computing? Figure 5 illustrates a more detailed architecture that shows which components are relevant within each edge node. A Vapor IO edge data center in Chicago. The infrastructure consists of four layers of storage and compute along with communication infrastructure to move data between layers. As soon as the camera recognizes a human in the video content, it will start transmitting the video to the local edge. The architecture focuses on reducing bandwidth usage and minimizing latency. Device Edge IoT devices are the basic building blocks of any smart city solution. The manufacturer might also have relationship with the CSP in which case the compute node might be at the base station owned by the CSP. Cars with autonomous driving capabilities need the brakes applied immediately or they run the risk of crashing. Analytic algorithms monitor how well each piece of equipment is running and adjust the operating parameters to improve its efficiency. Here we discuss the introduction to Edge Computing Architecture, various protocols used in this layer. As we discussed earlier, edge computing consists of three main nodes: 1. IBM works with many telecommunications companies to help explore these new technologies so that they can better understand how the technologies are relevant to current and future business challenges. Our next article in this edge computing series dives deeper into the different layers and tools that developers need to implement an edge computing architecture. It is important to recognize the importance of managing workloads in discreet ways as the less discreet, the more limited in how we might deploy and manage them. It just acts as an interface to connect the edge architecture with either fog domain or cloud environment. Content that cannot be handled at the local edge can be sent to the cloud or data center for in-depth analysis. Edge provides data computing capabilities nearer to the source of data. Edge device An edge device is a special-purpose piece of equipment that also has compute capacity that is integrated into that device. It is the distributed framework where data is processed as close to the originating data source possible. The advent of 5G has made edge computing even more compelling, enabling significantly improved network capacity, lower latency, higher speeds, and increased efficiency. 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.. The edge network layer and edge cluster/servers can be separate physical or virtual servers existing in various physical locations or they can be combined in a hyperconverged system. Edge computing moves services closer to the edge and enhances service delivery. At Source, we have devices, usually sensors, that collects or generates data. In most of the cases, depending upon the response time and bandwidth available, the edge can be just a hop distance from the main edge device, collecting the data. This bringing of storage and computing nearer to the devices improves response time and lessens the bandwidth. Device edge, where the edge devices sit 2. There are over 3,000 pieces of equipment on the factory floor including presses, assembly machines, paint robots and conveyers. Via the edge center, a mere 13 milliseconds sufficed. Operating and governing cities has become a challenging mission due to many interrelated issues such as increasing operation cost from aging infrastructure, operational inefficiencies, and increasing expectations from a city’s citizens. Edge computing nodes that consist of smart cameras can do the initial level of analytics, including recognizing entities of interest. Both the components of the systems that are required to manage these applications in these architecture layers as well as the applications on the device edge will reside here. In short every data generating device will be considered as an edge device. For example, if the application is moved from one data center with always available support to 100’s of locations at the local edge that are not readily accessible or not in a location with that kind of local technical support, how one manages the lifecycle and support of the application must change. This trend has made it more challenging to consolidate data and processing in a single data center, giving rise to the use of “edge computing.” This architecture performs computations near the edge of the network, which is closer to the data source. We will be exploring every aspect of this architecture in more detail in upcoming articles. The actual devices running on-premises at the edge such as cameras, sensors, and other physical devices that gather data or interact with edge data. Device edge physical devices might not have the ability to leverage existing security standards or solutions due to their limited capabilities. While Data Security is a benefit in that the data can be limited to certain physical locations for specific applications, overall security is an additional challenge when adopting edge computing. 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. Also, the standard should allow for management of the full lifecycle of the application, from build through run and maintain. Application layer: Applications that cannot run at the device edge because the footprint is too large for the device will run here. The distributed IT architecture reduces network latency and bottlenecks. In future articles in this series, we will look at these application and network tools in more details. A lot of announcements try to position their existing products for edge deployments and few try to innovate for purpose built edge architectures. To operate at scale, the workloads being considered for edge computing localization need to be modified to be more portable. It is defined by a hierarchy of computing power and latency, both of which are highest on the top level and decreasing downwards. Predicting failure can be complex and requires the customized models for each use case. A high level comparison of key technical aspects of the MCC and the edge computing is outlined in Table I. 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. In addition, the network can become heavily loaded in such instances. You can also go through our other related articles to learn more –, All in One Data Science Bundle (360+ Courses, 50+ projects). Gather and analyze sensor data on the edge, Building out the edge in the application layer and device layer, Building and deploying a 5G network service for your edge apps, Create predictive maintenance models to detect equipment breakdown risks, next article in this edge computing series, Telecommunications, Media & Entertainment, Benefits and challenges of edge computing, Additional benefits, with additional challenges, Sample implementation of an edge computing architecture, Edge computing architecture and use cases (this article), Increase privacy of sensitive information, Enable operations even when networks are disrupted. Credit: Al-Mamun & Zhao. 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. 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. New York-based Oden Technologies developed an industrial automation and analytics platform that used a cloud-to-edge architecture to provide manufacturers with an AI-powered production recommendation system to optimize production and hit peak factory performance. A platform approach has emerged to span various developer skill sets. AI, Edge Computing Architecture Drive Embedded IoT Development AI support in the cloud and at the edge have furthered embedded IoT development. © 2020 - EDUCBA. This is a guide to Edge Computing Architecture. Edge cluster/server An edge cluster/server is a general-purpose IT computer that is located in a remote operations facility such as a factory, retail store, hotel, distribution center, or bank. Since reducing latency requires moving the workload closer to the edge and moving it to the edge components will mean less compute resources to run the workload, the overall size of various workloads might limit the potential of edge computing. Data generated by these devices is different depending upon the source. The first edge computing concept bringing the computa-tion/storage closer to the UEs, proposed in 2009, is cloudlet [4]. Initial video processing is done by the drones and the device edge. AMQP focuses on message-oriented environments and is an open standard application layer protocol. The architecture can include cloud-based features, as happens in most of the cases, and in some of the cases, cloud properties aren’t included in the edge architecture model. We also discussed the three key layers of an edge computing architecture: the device edge, local edge (which includes the application layer and application layer), and cloud edge. An edge cluster/server is typically used to run enterprise application workloads and shared services. It is common to find edge devices that have ARM or x86 class CPUs with 1 or 2 cores, 128 MB of memory, and perhaps 1 GB of local persistent storage. As we discussed earlier, edge computing consists of three main nodes: Figure 4 represents an architecture overview of these details with the local edge broken out to represent the workloads. The CoAP proposes a transfer protocol based on Representational State Transfer (REST) on top of HTTP functionalities. Interesting work can be performed on edge devices, such as an assembly machine on a factory floor, an ATM, an intelligent camera, or an automobile. According to Bittman, edge computing is going to become necessary to support these connected devices—and business needs—in the future. First, size does matter. An edge cluster/server is typically constructed with an industrial PC or racked computer form factor. Edge computing is a part of the overall architecture as it was necessary to provide key services at the edge. Edge computing has the potential to dramatically increase the efficiency of systems built using IoT devices. An edge gateway acts as a node between edge devices and a core network. The protocols used for the data transfer can be Ethernet, Bluetooth, Wi-Fi, NFC, ZigBee, etc. There are many potential points of failure with the current communications flow. So, to address these security challenges, the infrastructure upstream in the local edge might have additional security concerns to address. Funneled through a local gateway device, edge-based architectures allow faster access and take much of the pressure off of networks. It is used in mobile-based social network applications and it makes complexity less by using HTTP methods(get, post, put, and delete). EDGE COMPUTING ARCHITECTURE. Second, adopting a portability standard or set of standards can be difficult with many varied workloads to consider. To perform real-time tasks, an architecture for edge computing nodes is designed [17]. With edge, with the communication primarily between the consumer/requestor and the device/local edge node, there is a resulting increase in availability of the systems. These permutations of perspectives drive a paucity of aligned user stories to share with the OpenStack and StarlingX communities. The data sources in an edge computing environment can be applications capturing data, sensors, appliances, or any data capturing device. With many standards in this ecosystem newly created or quickly evolving, it will be difficult to maintain technology decisions long term. Edge computing is composed of technologies take advantage of computing resources that are available outside of traditional and cloud data centers such that the workload is placed closer to where data is created and such that actions can then be taken in response to an analysis of that data. A mobile phone can be employed at the edge. 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. The entire network layer is mostly virtualized or containerized. Using video to identify key events is rapidly spreading across all domains and industries. The focus of the project was to allow CSPs to manage and deliver multiple high-value products and services so that they can be delivered to market more quickly and efficiently, including capabilities around 5G. With the advent of 5G, it is possible to rapidly communicate with the edge, and applications that are running at the edge can quickly respond to the ever-growing demand of consumers. 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. 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 let’s remember that edge computing plays a key role as part of a strategy and architecture, an important part, but only one part. Depending on the current application environment, this move might be a large effort. MQTT is built on top of the TCP protocol and is suitable for devices with low resource availability, unreliable or low bandwidth links. 5G promises data speeds in excess of 20 Gbps and the ability to connect over a million devices per square kilometer. Decentralized architecture of edge computing enables the other network devices to become resilient to a greater extent. The various edge devices capture data and communicate via IoT protocols, sending data to the edge gateways. These containers include visual analytics applications and network layer to manage the underlying network functionality required for the new service. By Jason Gonzalez, Jason Hunt, Mathews Thomas, Ryan Anderson, Utpal Mangla Updated May 27, 2020 | Published February 17, 2020. 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. 7. Define a reference architecture for edge and far edge deployments including OpenStack services and other open source components as building blocks. In 2019, IBM partnered with Telecommunications companies and other technology participants to build a Business Operation System solution. It is designed to enable real-time applications to work efficiently and to provide responses within a specified time. Teams will require more than the traditional network operations tools (and even beyond software-defined network operations tools), as support teams will need tools to help manage application workloads in context to the network in various distributed environments (many more than previously), which will each have differing strengths and capabilities. For reference, let’s discuss some potential industry use cases for consideration, both examples and real solutions. Edge node An edge node is a generic way of referring to any edge device, edge server, or edge gateway on which edge computing can be performed. Consider this example: A major fire occurs, and it is often difficult to differentiate humans from other objects burning. Often driven by economic considerations, an edge device typically has limited compute resources. The initial analysis and compute of the data can be executed within the vehicle. MQTT simply consists of three components, subscriber, publisher, and a broker. The key to a modern edge network architecture is a cloud-based platform that allows network operations and security to be managed centrally but distributed to wherever enterprises need to extend traffic too. Edge-based infrastructures (device, edge, and server) sometimes known as ‘fog’ or grid computing, can be set up to dovetail with IoT and most widely distribu… Edge computing and IoT will evolve in tandem. This bringing of storage and computing nearer to the devices improves response time and lessens the bandwidth. 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. THE CERTIFICATION NAMES ARE THE TRADEMARKS OF THEIR RESPECTIVE OWNERS. The connection operation is based on a routing mechanism and makes MQTT as the best possible connection protocol for both IoT and M2M. The devices present at the edge of the network vary based upon the functionalities. The devices which are employed at the edge should be capable of providing storage and computing services. These clouds also host and run the applications that are used to orchestrate and manage the different edge nodes. It is inserted into a logical end point of a network (Internet or private network), as part of a larger cloud computing architecture. One suggested data center paradigm is to simply push data handling to the edge of a network. It provides reliable Communication through message delivery guarantee primitives which include at-most-once, at-least-once and exactly-once delivery. 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. Examples include routers, switches, or any other network components that are required to run the local edge. An outage in a production run costs you $250,000 per hour. Availability: Critical systems need to operate irrespective of connectivity. Workloads include application and network workloads that are to be deployed to the different edge nodes by using the appropriate orchestration layers. Multi-access edge computing (MEC), formerly mobile edge computing, is an ETSI-defined network architecture concept that enables cloud computing capabilities and an IT service environment at the edge of the cellular network and, more in general at the edge of any network. For one example how these types of models can be created, refer to this code pattern, “Create predictive maintenance models to detect equipment breakdown risks.” Some of these models need to run on the edge, and our next set of tutorials will explain how to do this. Many companies are positioning themselves to grab a share of the edge computing addressable market. The edge of a network can be at a distance from the actual edge device. Lastly, the local edge can now contact the appropriate authorities instead of transmitting the data to the data center which will be slower and since the network from the fire site to the data center might be down. An Edge Computing Architecture comprises of the following components, Hadoop, Data Science, Statistics & others. Example applications include complex video analytics and IoT processing. Although some edge devices can serve as a limited gateway or host network functions, edge gateways are more often separate from edge devices. But we’re just getting started. Nevertheless, the security concerns Fog and Edge Computing bring in have not been fully considered and addressed so far, especially when considering the underlying technologies (e.g. In some cases, applications will need to be containerized and run on a very small device. Edge computing architecture could accommodate that ephemerality, dynamism and need for real-time insight. This infrastructure requires effective use of resources that may not be continuously connected to a network such as laptops, smartphones, tablets, and sensors. Consider this example: A manufacturer of electric bicycles is trying to reduce downtime. CoAP is an application layer protocol for edge devices and applications, created by IETF Constrained RESTful Environments (CoRE) working group. There are two primary sublayers to this architecture layer. 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. Edge computing has its pros for low latency applications, forexample,safetyapplications(drivingSafetyandcontext awareness)aswellasnonsafetyapplications(videostreaming, ... e architecture of the system should be designed to helpdriverstoavoidaccidents.Toattainreliableandample environment information, the smart … The above picture shows a typical Edge Computing architecture. The continual addition of newer and smaller edge devices will require changes to existing applications so that enterprises can fully leverage the capabilities of 5G and edge computing. Edge computing places data acquisition and control functions, storage of high bandwidth content, and applicationscomputing closer to the end user. Embedding these devices into the city’s infrastructure and assets helps monitor infrastructure performance and provides insightful information about the behavior of these assets. 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. Containers cannot be deployed on them for this reason. Edge Computing covers a wide range of technologies including wireless sensor networks, cooperative distributed peer-to-peer ad-hoc networking and processing, also … Edge computing involves all types of computations which occur at the edge of a network outside the cloud. The edge server not only connects all edge devices in a secure manner but also allows for management of all those devices. In either case, it is important to be able to deploy and manage the applications on these edge devices. Edge computing evolves and extends cloud computing to transform the underlying architecture and create an environment ripe for application, service and business model innovation. Edge Computing Architecture is a new model for providing storage and substantial computing properties near to the devices. In a CSP, this would typically include migrating to a combination of network function virtualization (for network workloads) and container workloads (for application workloads and in the future, network workloads), where applicable and possible. Edge computing involves all types of computations which occur at the edge of a network outside the cloud. To address this challenge, new tools and training for technical support teams will be needed to manage, orchestrate, and automate this new complex environment. Edge computing is a very widely used term these days in lot of technology blogs, analyst reports, conferences and product announcements. "Edge" is a term with varying definitions depending on the particular problem a deployer is attempting to solve. This is a great virtue since a single machine failing on the cloud would mean thousands of IoT devices getting affected. Relevant information can be sent to the base station that then transmits the data to the relevant endpoint, which might be a content delivery network in the case of a video transmission or automobiles manufacturers data center. An edge computing architecture moves applications and data closer to the user, allowing better network response to end-user requirements. 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. The cloud can also be a source and destination for any data that is required by the other nodes. The following illustrates the implementation with further extensions having since been made. This website or its third-party tools use cookies, which are necessary to its functioning and required to achieve the purposes illustrated in the cookie policy. Cloud This architecture layer is generically referred to as the cloud but it can run on-premise or in the public cloud. Local Edge The systems running on-premises or at the edge of the network. Local edge, which includes both the infrastructure to support the application and also the network workloads 3. The benefits of edge computing technology include these core benefits: Performance: Near instant compute and analytics at the edge lowers latency, and therefore greatly increasing performance. This graphic captures the four perspectives of edge computing. However, for now, let’s take a brief look at one real implementation of a complex edge computing architecture. Introduction. Operating at peak efficiency and with no unplanned outages is the difference between having profit and not having profit. As emerging technologies, 5G and edge computing bring many benefits to many industries, but they also bring some challenges along with them. The evaluation of whether a business problem or use case could or should be solved with edge computing will need to be done on a case by case basis to determine whether it makes sense to pursue. In addition, the local edge is close to the device edge so latency will be almost zero. These points of failure include the company’s core network, multiple hops across multiple network nodes with security risks along the network paths, and many others. 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. There are challenges, though, including security ones. Edge Computing Enhances In-Store Retail. Transmitting all the data to the cloud or data center is expensive and slow. A router can be employed at the edge of the network etc. The service is provisioned, and drones start capturing the video. The appropriate containers are deployed to the different edge nodes. Each of these nodes is an important part of the overall edge computing architecture. Many network and application partners are already working on migrating capabilities to container-based approaches, which can aid in addressing this challenge. 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 5G, CSPs can also cater to real-time communications for next-generation applications like autonomous vehicles, drones, or remote patient monitoring. The enormous emergence of IoT devices has pushed the bandwidth demands to the extreme levels, resulting in delay. No matter which perspective, edge computing decentralizes and extends campus networks, cellular networks, data center networks, or the cloud. With a basic understanding of edge computing, let’s take a brief moment to discuss 5G and its impact on edge computing before we discuss the benefits and challenges around edge computing. Examples of such applications include specialized video analytics, deep learning AI models, and simple real time processing applications. A common theme across all these industries is the network that will be provided by the CSP. IBM’s approach (in its IBM Edge Computing solutions) is to deploy and manage containerized applications on these edge devices. an edge-computing architecture simply means the edge of the network. 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