The CI/CD pipeline can also monitor and change xNFs, as well as report and resolve issues that are discovered. In this final part in our series, we will discuss the underlying components of the network layer and how you can orchestrate, manage, and monitor the network components. Depending on the implementation, time-sensitive data in an … Key Differences between Data Lake and Data Warehouse, Cloud Service Models Explained: SaaS v PaaS v IaaS v DBaaS. Learn more here. A key feature of 5G technology is the ability to create network slices that run multiple logical networks as virtually independent operations over shared physical infrastructure. https://www.linkedin.com/in/oleksandr-bushkovskyi-32240073/. Gogs: Lightweight self-hosted Git service, Jenkins: Automation server that enables the CICD process, Openldap: Open-source implementation of the Lightweight Directory Access Protocol, Docker Registry: Registry for hosting docker images, Nginx Ingress: Ingress controller to support accessing some services with Ingress. You especially need to understand how the network layer integrates with the application layer. ); When information stream is a requirement for proper data analysis and related activities (such as virtual assistants and wearable IoT devices); Point of origin processing - when data processing happens within the IoT device itself (for example, as in self-driving cars); Intermediary server processing - when data processing is going through a nearby local server (as with virtual assistants). The grid computing model is a special kind of cost-effective distributed computing.In distributed computing, resources are shared by same network computers.In grid computing architecture, every computer in network turning into a powerful supercomputer that access to enormous processing power,memory and data storage capacity.. The purpose of this Hackathon is to demonstrate the usage of MEC system as an enabler for different use cases and business objectives, helping all stakeholders to develop a diverse, open MEC ecosystem. This process requires rapid-fire data processing to gain situational awareness. "Put another way, edge computing brings the data and the compute closest to the point of interaction." All these different layers communicate through the network layer. Now, let’s look at the process on a more technical level. With the rapid growth of generated resource quantity, it is difficult to adapt to this situation by using traditional cloud computing models. In VMware vCenter, the hosts where the VMs are running have an operating system called ESXi. The advanced 5G features and capabilities for IoT, intelligent edge, and AI/IVR are very real and attention should be paid to 5G network architecture in order to take advantage. Similar to Cloud, Fog provides data, compute, storage, and application services to end-users. IT infrastructure and operations (I&O) leaders tasked with managing these solutions should understand the associated business value and risks. In Part 1, we showed how edge computing is relevant to the challenges faced by many industries, but especially the telecommunications industry. Create a CI/CD pipeline with tools like Jenkins and Gogs to manage onboarding and testing of xNFs and network services. Things are sensors in this example. In our example, to configure an observer job, you will need to provide a Unique ID for the job, IBM Agile Lifecycle Manager instance name to identify the IBM Agile Lifecycle Manager, Topic (Kafka topic), Group ID (Kafka group ID), and connection details such as Kafka host and port to be used. The network layer includes the network components, such as routers and switches, that are needed to run the local edge. We build the MEC architecture in sequence, block by block starting from Ground Zero 🙂 Definition of MEC. A location is a geographical unit where one or more IoT devices are deployed. MEC is an evolution in cloud computing that brings the applications from centralized data centers to the network edge, and therefore closer to the end users and their devices. While we won’t delve too deeply into the intricacies of containers and their various engines (Docker, cri-o, rkt, etc), it is worth mentioning that containers generally run as single components that can be put together to form bigger applications. The scripts for roles go in roles folder and can have multiple tasks defined in them. The power, cooling, space and such other functional costs make these clusters expensive. At the moment, Tesla is one of the leading players in the autonomous vehicle market. Descriptor files define the input properties of an xNF service and the list of lifecycle. With the CI/CD DevOps tooling available in the CI/CD Hub, the engineer can package, test, and finally publish the xNF to be available on an xNF Catalog. We used Ansible resource manager for automation, so we create Ansible playbooks to create our xNF packages. Pushing updates to the 5G Core package to the GoGs repo will then trigger a webhook to set off the Jenkins pipeline that was created. Edge computing is a distributed information technology (IT) architecture in which client data is processed at the periphery of the network, as close to the originating source as possible. The sensors are made out of a NodeMCU and a DHT11, as explained in the first step of the first tutorial . Service design also includes definitions of relationships between xNFs and their intent-based lifecycle events. It will continue to enable many new use cases and open up opportunities for telecom providers to develop new services that reach more people. These services can probably be added on base Kubernetes, but it is helpful that OpenShift provides them and more by default. After building the CI/CD hub, we use it to onboard each of the xNFs in our network layer. Because 5G is core to the businesses of connectivity, telecommunications companies are investing heavily in edge computing as a key pillar for their overall 5G rollout. Overall, five key challenges come with the implementation of edge computing applications. Privacy Policy, ©2019 The App Solutions Inc. USA All Rights Reserved. In a way, fog is a standard and the edge is a concept based on that standard. PRIVATE VS. IBM Netcool Operations provides a consolidated view of events across local, cloud, and hybrid environments and delivers actionable insight into the performance of services and their associated dynamic network and IT infrastructures. Edge Computing Architecture is a new model for providing storage and substantial computing properties near to the devices. The events area contains a table of events and their characteristics. Edge computing works with Cloud computing, not against ... By asking the Edge vendor about these important architecture considerations, you will ensure you have an Edge stack which can be used quickly and securely to develop and deploy Edge applications at scale. This bringing of storage and computing nearer to the devices improves response time and lessens the bandwidth. The edge computing framework's purpose is to be an efficient workaround for the high workload data processing and transmissions that are prone to cause significant system bottlenecks. The solution for this is to create a network slice for the worker safety application. To summarize, the full end-to-end implementation of an edge use case will involve the following: Deployment and management of the application layer as described in Part 2 of this series. In an Edge architecture, devices can be of three types depending on their role: Edge Gateways, Edge Devices, and Edge Sensors and Actuators. 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. The following screenshot shows an example of deployment location input while provisioning a new 5G network slice service. IoT devices brought-in so much data that even seemingly boundless computing capabilities of the cloud were not enough to maintain an instantaneous process and timely results. This aspect helps to maintain its timely and consistent performance. In addition to organizer features, it is able to check the heart and caloric rates. The device layer has devices which can run small programs and transmit the required data to the application layer. To onboard our xNF components, you need to wrap the xNF software components and push them to Agile Lifecycle Manager’s resource repository. This step wraps third party xNF software into agile service building blocks that can be tested individually for performance and to reduce errors that need manual intervention in production. “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. The term edge computing may sound simple, as the two words separately seem self-explanatory. Edge computing is a distributed, open IT architecture that features decentralised processing power, enabling mobile computing and Internet of Things (IoT) technologies. Examples of xNFs include firewalls, routers and gateways. With the CI/CD hub, we can create pipelines in the Jenkins component, so that every time an update is pushed, it can trigger a set of tasks that can help ensure that our changes are correctly packaged and deployed. On-premise edge computing retains sensitive data on-premises while still taking advantage of the elasticity offered by the edge … Some of the key components of the network layer include: We will describe how we built and deployed a network service running on the network layer. Both OpenStack and OpenShift provide VIM capabilities to manage the virtual infrastructure where vNFs (to OpenStack) and cNFs (to OpenShift) can be deployed. Introduction to Cloud Computing Architecture. In edge computing, data is processed by the device itself or by a local computer or server, rather than being transmitted to a data center. Onboarding all xNFs onto our MANO platform, designing the network services using those xNFs. Onboard xNFs on an orchestrator platform like IBM Agile Lifecycle Manager, and create network service designs needed for the 5G network slice. What Is a Lift and Shift Cloud Migration? This enables much faster customer turnaround with lesser chances of getting into a bottleneck at the counter. The physical world is divided in locations. In edge computing, data is processed by the device itself or by a local computer or server, rather than being transmitted to a … All of this is managed by the network layer. We identified xNFs that are needed to build the 5G network slice service, and we have a CI/CD pipeline in place, and now we need to complete the following two steps before the network slice service can be finalized and added to a service catalog. This third article will cover the network layer. These functions run in virtualised environments as cloud-based operations across a distributed edge architecture. Due to the advantages of power, cost and space, conventional analytical clusters do not support edge computing. In Part 2, we explored the application layer and device layer in greater detail and discussed the tools needed to implement the two layers. Introduction. Network slices offer operators the flexibility to allocate speed, capacity, and coverage in logical slices according to the demands of each use case by balancing the disparate requirements such as availability/reliability, bandwidth, connectivity, cost, elasticity, and latency. 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.. Deployment of those services onto OpenStack and OpenShift. In our use case, we are using observers to monitor events from IBM Agile Lifecycle Manager, Red Hat OpenShift, and OpenStack. It is therefore essential that the edge network layer supports both until there are sufficient cNFs to run the network. You can use the event viewer to monitor and manage events through an interactive interface. Integrating all the layers of our edge computing architecture. Then, choose the levels of networked resources around the seed that you wish to display, and click the Render button to render the view. Deployment and management of the network layer as described in this article. When an object is detected, the video stream is sent to the application layer for further analysis. The Edge TPU allows you to deploy high-quality ML inferencing at the edge, using various prototyping and production products from Coral. Edge computing is a viable solution for data-driven operations that require lightning-fast results and a high level of flexibility, depending on the current state of things. Implementing edge computing clearly involves much more than what is provided in the articles in this series, but we hope the articles help you get started or progress in rolling out edge computing solutions in your enterprise. Cloud computing is centralized. Get complete visibility and control across your network to help protect your IoT deployment and your business. 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. Most of them are related to application scenarios specifically targeted to vertical markets of the 5G era. Target applies edge computing analytics to manage their supply chain. Then, we create scripts for lifecycle events such as Install, Start, Stop, Configure and Integrity check. The main difference of cloud and edge computing is in the mode of infrastructure. IoT operation combines data processing on the spot (for initial proceedings) and subsequently on the cloud (for analytical purposes). To onboard and manage the xNF components, we use the following MANO and operations products: IBM Agile Lifecycle Manager enables automated operations by managing the end-to-end lifecycle of virtual network services, from release management of third party xNF software packages right through to the continuous orchestration or running of vNF and Service instances. Edge computing provides a self-driving car with this. After the xNF package is complete, it can be onboarded to IBM Agile Lifecycle Manager using its command line tool lmctl by specifying the target resource manager instance. Set up the network function virtualized infrastructure (NFVi) with infrastructure managers (VIMs).