#iot edge

Mobodexter | Mobodexter

Kubernetes Edge Computing: Chick-fil-A, known for its addictive poultry sandwiches and also waffle french fries, is reported to become the third-largest U.S. fast-food chain behind McDonald’s and Starbucks. Behind the scenes, their business remains at the forefront of embracing a powerful modern technology like Edge computing and Kubernetes.

Chick-fil-A published a Medium post that it will undoubtedly be running Kubernetes on the Edge of 6,000 IoT devices in all 2,000 of its dining restaurants. Part of the chain’s internet of things (IoT) strategy to gather and examine more information to enhance customer service and also functional effectiveness. For example, they can forecast how many waffle french fries need to be cooked every minute of the day.

This case study shows why Kubernetes has quickly ended up being a crucial active ingredient in edge computing. A tried and tested and effective runtime system to help address particular challenges across telecommunications, media, transportation, logistics, farming, retail, and also other market sections.

The telco industry particularly has much to acquire from edge computing. As competitors among operators intensify, telco firms must distinguish themselves with new use-cases such as commercial automation, virtual reality, connected cars and trucks, sensor networks, and smart cities. Telcos significantly are using edge computing to ensure these applications function flawlessly while also driving down the prices of deploying and handling the network framework.

Just what is edge computing? 

Edge computing is a variation of cloud computing, with your infrastructure like compute, storage, as well as networking physically closer to the devices that create information. Edge computing enables you to position applications and solutions closer to the source of the data. This placement gives you the twin advantage of reduced latency as well as lower web traffic. Reduced latency boosts the performance of field devices by enabling them not just to react quicker, yet to also respond to even more events. Lowering web traffic helps in reducing prices as well as boosts overall throughput. Whether an application or service will be in the edge cloud or the core datacenter will rely on the specific use-case.

Intel Edge Computing Framework

How can you build an Edge cloud? 

Edge clouds need to be built with the very least two layers. Both layers will certainly maximize operational efficiency as well as designer performance, and each layer is created differently.

The initial layer is the Infrastructure-as-a-Service (IaaS) layer. In addition to giving compute and storage space sources, the IaaS layer needs to address the network performance needs of ultra-low latency and also high data transfer. IaaS is the layer where blade systems from HP, Dell, IBM, and Lenovo, and specialized systems like Lenovo Think system SE350 come in the picture.

The 2nd layer is the Kubernetes layer, which gives an ideal platform to run your applications and services. Whereas making use of Kubernetes for this layer is optional, it has confirmed to be a reliable system for those organizations leveraging edge computers today. You can release Kubernetes to field tools, edge clouds, core data centers, and the general public cloud. This multi-cloud implementation ability provides you total flexibility to deploy your applications anywhere in the field, Edge, or cloud. Kubernetes deals with your developers the capability to simplify their DevOps practices and minimize time spent integrating with heterogeneous operating environments.

Benefits of Kubernetes on Edge

In addition to very low downtime and outstanding performance, Kubernetes offers numerous built-in advantages to resolve Edge compute obstacles, including versatility, resource performance, performance and reliability, scalability, and observability.

1) Flexibility

Kubernetes decreases the complexities associated with running computing power across various geographically dispersed points of existence and varied architecture by supplying versatile tooling that allows designers to connect with the Edge seamlessly.

With Kubernetes behind our Edge platform, users can run application containers at scale through an agnostic network of distributed computing infrastructure, which in turn, extends complete flexibility to the users to be able to run the application anywhere along with the Edge computing.

2) Resource Efficiency

Containers are light-weight by nature and enable you to use the underlying infrastructure in an extremely effective method. However, managing thousands or, in many cases, countless containers throughout a distributed architecture gets complex very rapidly. Kubernetes supplies the underlying tools to effectively manage container workflows through automated networking, storage, and event logs.

The Kubernetes Horizontal Pod Autoscaler is one key feature that naturally provides itself to edge computing performances. It immediately scales the variety of pods up or down in a replication controller, release, or replica set based upon latency or volume thresholds. Think about a point of existence in an edge location that requires to manage abrupt traffic increases, like, in the case of a local sporting event. Kubernetes can auto-detect traffic from event logs and provide resources to scale to the fluctuating demand. This kind of auto-scaling takes the uncertainty out of forecasting and preparing for infrastructure needs. It also makes sure that you’re only provisioning for what your application requires in any given period.

3) Performance

Modern applications require lower latency than standard cloud computing designs can provide. By running workloads better to end-users, applications can recuperate vital milliseconds to give a much better user experience. As mentioned above, Kubernetes’ capability to respond to latency and volume limits (utilizing the Horizontal Pod Autoscaler) implies that traffic can be routed to the most optimal edge places to reduce latency.

4) Dependability & Scalability

Among the significant advantages of Kubernetes is that it is self-healing. In Kubernetes, you can restart containers that stop working, replace, and reschedule containers when nodes fail and eliminate containers that do not respond to your user-defined health check.

 In Kubernetes, services abstract this process, allows you to begin and stop containers behind the service. At the same time, Kubernetes carries on managing traffic reroutes to the right containers to avoid service disturbances.

Besides, since Kubernetes’ control plane can handle tens of countless containers running over numerous nodes, it allows applications to scale as needed, especially fitting the management of distributed edge workloads.

5) Observability

Knowing where and how to run edge work to make the most of efficiency, security, and performance needs observability. However, observability in a microservices architecture is complex.

Kubernetes offers a full view of production work, enabling optimization of efficiency and efficiency. The built-in monitoring system allows real-time insights (consisting of transaction traces, logging, and aggregated metrics) with instant provisioning figured out by configuration settings. 

Another point of observability is of specific interest when it concerns edge computing is dispersed tracing. Distributed tracing permits you to collect and construct an extensive view of requests throughout the chain of API calls made, all the way from user requests to interactions in between numerous services. With this info, you can determine traffic bottlenecks and opportunities for optimization.

Interest in edge computing is being driven by rapid data increases from smart tools in the IoT, the coming influence of 5G networks as well as the growing importance of performing artificial intelligence jobs at the Edge. All of which need the ability to deal with flexible needs as well as shifting workloads. Kubernetes Edge Computing is one of the rapidly growing technology that companies like Mobodexter are continually innovating and help customers to become productive and efficient with their IoT strategy. 

Footnotes:

Mobodexter, Inc., based in Redmond- WA, builds internet of things solutions for enterprise applications with Highly scalable Kubernetes Edge Clusters that works seamlessly with AWS IoT, Azure IoT & Google Cloud IoT.

Want to build your Kubernetes Edge Solution – Email us at [email protected]

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Mobodexter | Mobodexter

Edge Computing Data Center: Edge computing has many layers and flavors and is defined in varying size, scope, and market-based to fit the unique requirements of the customers. Edge data centers are designed based on the customer’s needs. Hence, the definition of Edge can range from 10kW to over 10MW.

The network, content, and cloud providers will continue to drive the of Edge’s expansion. Edge proof of concepts is seen in autonomous vehicles, smart cities, and other IoT use cases. These use cases will also help add new flavors to the Edge in 2019 and beyond. The critical business element is to be flexible, fast, and scalable to support the dynamic requirements for these edge use cases.

One can see a heightened focus on enterprise edge computing now than before. Most clients are moving their compute environments closer to their end-users with smaller, more distributed deployments. This evolution creates an excellent opportunity for data center providers. However, it also highlights the importance of flexibility, global scale, and interconnection.

For most medium-to-large enterprises, it’s not enough for a data center provider to be in just one or two markets. They should look for Edge data center providers that give them the flexibility to move capacity between markets without penalty.

Time to provide interconnection and dedicated infrastructure impede edge computing adoption. Enterprises are looking to data center providers that not only offer various, low-latency Edge to cloud providers and cloud edge POPs. They need to easily link their data center to the ecosystem of service providers in a metro region.

The evolution of edge computing that is taking shape now requires early pioneers to be self-sufficient. Many on-premise test facilities are already working with the support of co-alliances, and it appears to be a race just getting started. Artificial intelligence (AI) and Internet of Things (IOT) use cases are becoming standard. Edge computing will continue to see increased adoption to support these use cases.

In 2019 and 2020, 5G buildout will be one extensive, universal phenomenon. Telecom Carriers will need to deploy more compute and storage closer to users to address latency challenges. Also, the Content Delivery Network (CDN) providers will likely expand their regional presence to support streaming and eventually AR/VR applications.

The other primary driver for Edge will be Industry 4.0. There is enormous value in collecting, trending, and analyzing data from manufacturing operations in real-time. However, doing that in the cloud is cost-prohibitive for most data other than top-level reporting. This use case will require to compute on-premise.

In 2019 the edge use case is in High Bandwidth, Large Volume Applications like

Mobile HD video

Autonomous driving

Content delivery

Healthcare (imaging and diagnostics)

Caching and surveillance

Retail

The next round of use cases after that are going to be moved up with 5G enabled-networks for machines-2-machine communication, and machines to talk to humans. We need the new systems to allow the number of simultaneous connections all the devices require.

Today’s Edge computing use cases are still focused on localizing specific business applications/content within colocation facilities in less populated cities. This model is used to maximize performance for particular end-users or disaster recovery. SDN (software-defined networking) encourage to add edge data centers in non-urban cities a viable option. Additionally, with the commoditized cost of fiber, end-users have various choices and negotiating capability to make the most out of their edge deployments.

As we move forward, innovations like Amazon Web Services’ Outpost, Snowball, and Snowmobile will bring real Edge computing closer to mass adoption. Futuristic solutions featuring data center containers deployed in remote, global locations such as telecom towers are already being tested. With comprehensive 5G technology around the corner, smart cities, and game-changing network-based offerings nearing completion like self-driving cars, edge computing is the next step in the evolution. We are in the early stages and a couple of years out still for actual Edge computing adoption, but the momentum is starting to build.

Edge Computing Data Center: Two significant data center trends in 2018 were the dominance of massive, hyper-scale cloud deployments and the emergence of new edge computing architectures. Datacenter products will integrate hyper-scale and edge computing data centers. Edge will also support advanced technologies such as artificial intelligence, Internet of Things, blockchain, virtual reality, big data. Keep an eye on the 100,000+ cell towers in the U.S. as a platform for edge roll-outs.

Footnotes:

Mobodexter, Inc based in Redmond, WA builds IoT Edge solutions for enterprises applications on Kubernetes & Dockers.

Register now at developers.paasmer.co and enjoy free Edge trial license of Edge Intelligence software that runs on Raspberry Pi.

Follow our all our weekly IoT Blogs: https://blogs.mobodexter.com

Our IoT Newsletter reaches 3000+ subscribers– Subscribe Now.

Become our affiliate partner today.

Mobodexter | Mobodexter

Edge Computing Layers: Edge computing will create a transformational impact on American society and business, thereby enabling new technologies and services across instant wireless connectivity.  For that to happen, edge computing must navigate a confusing maze and conflicting definitions, that are caused by the diverse nature of edge infrastructure and business models.

Significant stakeholders in edge computing are taking steps to clarify the various infrastructure layers and use cases. Multiple associations are being created by large corporations to define edge computing standards. Thes steps are essential to lay the groundwork for success. Edge computing is not a single entity like a computer, but several technologies that will work together to create a solution to solve a particular problem.

However, there are many challenges. Ege computing implementation timing remains unclear with many enterprises on when the business demand for the different flavors of edge infrastructure will materialize. This uncertainty is complicating decisions on how and when to fund these projects. 

Edge needs new technology:

Edge computing is not a single technology. It is instead a phrase that describes several layers of infrastructure, some of which are updates of several current models like a high-speed cellular connection – 5G. Edge computing solution is a response to new technologies such as autonomous vehicles and distributed AI applications which need low latency and proximity to users. These technologies are transforming the future of the Internet.

The transformation to edge computing will be an enormous effort for enterprises. It will take many years, cost billions and billions of dollars, and ultimately become massive in scale.

Multiple Layers and definitions:

For edge computing to be deployed at a vast scale, the edge will need to be compact, cheaper, and take the network to other new places. The critical components of edge computing include wireless networks, mobile devices, telecom towers, small cells, and distributed antenna systems as well as data centers and cloud platforms. Edge is a universe that transforms traditional business models built on specialization.

The data center industry defines an edge is a module. The small cell industry defines an edge is a small cell. Device makers envision analytics will run on sensors.

For cloud computing, it will take time for the market to grasp a holistic understanding of the edge and its many layers. Industry veterans believe things have progressed further due in part to the scope of the business opportunity.

Five layers of infrastructure:

Data Sources: Wearables, Things & Sensors

Processing Endpoints: Cars, Robots or Personal Computers

Local Data Hubs: Data processing at a small IT room or a building

Urban Data Hubs: Data processing at a cell tower or metro data center.

Regional Data Hubs: Cloud availability zones and colocation center.

The Linux Foundation offers a chart of edge layers and provides four principles that frame the participants’ lens on edge:

The edge is a location and not a single thing.

There are many edges, but the edge of the last mile network is essential.

The edge has two sides: an infrastructure edge and a device edge.

Data Computing will happen on both sides, with edge working in coordination with the centralized cloud.

Disruption demands new solutions:

This revolution is going to cost to implement. Many executives predict that the buildout of edge computing will pressure the economics of data infrastructure. Users will not pay a premium to go faster. Demand for services has a way of doing economics work, even in cases like this where the disruption is along the way.

There will be data centers in these new places, but they may be built and powered very differently than the server farms of Silicon Valley and Northern Virginia. The core compute doesn’t exist in one place like a datacenter anymore. It is instead distributed across multiple layers and locations. Edge Computing Layers are essential for real-world implementation, and they need careful strategic planning to create a transformational business impact.

Footnotes:

Mobodexter, Inc based in Redmond, WA builds IOT Edge solutions for enterprises applications on Kubernetes & Dockers.

Register now at developers.paasmer.co and enjoy free Edge trial license of Edge Intelligence software that runs on Raspberry Pi.

Follow our all our weekly IoT Blogs: https://blogs.mobodexter.com

Our IoT Newsletter reaches 3000+ subscribers– Subscribe Now.

Become our affiliate partner today.

Mobodexter | Mobodexter

Edge computing nodes: The potential “edge-native” applications are wide-ranging: it ranges from mobile and cloud gaming, augmented and virtual reality applications, safety, to surveillance in smart cities. It also extends to customer engagement in assisted and autonomous driving, autonomous drones, intelligent retail and defect detection services and quality control in intelligent manufacturing.

All such “edge-native” or “edge-assisted” applications derive motivations from ultra-low latencies, managing data volume, and data privacy issues. The type of edge deployments and related services that are taking shape fall under three main categories: (1) Telcom edge or Multiple access edge computing (MEC) driven by telecom operator offerings targeted to developers. (2) Hosted cloud edge services such as from Microsoft Azure, AWS, Cloudflare, and Packet. (3) Private edge deployments have seen today in enterprises such as in the industrial IoT.

As the shape and size of each implementation will differ based on usage case, so too will the matching edge node (or edge computing system). In edge nodes, one size will certainly NOT fit all.

Following are a few of the essential factors to consider that are driving edge node style:

( 1)Type of edge network or service: This factor to consider will drive connection options such as 4G or 5G or private LTE or WiFi for regional and cloud connection of the node. It will also drive the software application that the node will need. This type of edge node will find use in business security, iot, personal privacy functions, and multi-tenancy options.

( 2) Type of work: The moving information gravity mainly drives Edge-native applications to the edge, such as from sensing units, video cameras, Lidars, and others. The task for this class of applications is associated with pre-processing or processing this information. Thereby leading to a limited set of specialized work, e.g., maker vision, time-series information analytics, deep knowing reasoning, signal processing, and comparable.

The majority of these work benefit substantially from unique function accelerators such as ASICs, FPGA, and GPU to satisfy efficiency/ watt and efficiency/ expense objectives. Very important in this context is how the node’s compute abilities appear like to designers. The type of APIs would these edge accelerators be provided.

( 3) Type of Data : Data source user interfaces, security, and personal privacy, storage is driven by volume and life expectancy, information company. The information is processed throughout end-points, edge, cloud, along with arranged and saved is a considerable part of edge computing. We depend on partners who are professionals in this location to assist us to resolve this.

( 4) Decisions are driven by data processing: Edge will exist to set off low latency choices. Hence edge nodes will require the ideal user interfaces to carry out such decisions whether autonomously or with human-in-the-loop.

( 5) Scaling the deployment and operation of date: In our viewpoint, this is among the essential elements of edge releases, and edge nodes will require to supply the best assistance for this function.

Conclusion: The choice of Edge computing nodes depends on many factors like the type of network, type of workload, type of data, requirements for data processing, and scaling requirements. These are a complex set of requirements typically managed and solved with support from Edge computing experts.

Footnotes:

Mobodexter, Inc based in Redmond, WA builds IOT Edge solutions for enterprises applications on Kubernetes & Dockers.

Register now at developers.paasmer.co and enjoy free Edge trial license that can run on Raspberry Pi.

Follow our all our weekly IoT Blogs: https://blogs.mobodexter.com

Join our IoT Hub of 3000+ subscribers to get these blogs in Email –  Subscribe.

Become our affiliate partner today.

Mobodexter | Mobodexter

Edge computing nodes: The potential “edge-native” applications are wide-ranging: it ranges from mobile and cloud gaming, augmented and virtual reality applications, safety, to surveillance in smart cities. It also extends to customer engagement in assisted and autonomous driving, autonomous drones, intelligent retail and defect detection services and quality control in intelligent manufacturing.

All such “edge-native” or “edge-assisted” applications derive motivations from ultra-low latencies, managing data volume, and data privacy issues. The type of edge deployments and related services that are taking shape fall under three main categories: (1) Telcom edge or Multiple access edge computing (MEC) driven by telecom operator offerings targeted to developers. (2) Hosted cloud edge services such as from Microsoft Azure, AWS, Cloudflare, and Packet. (3) Private edge deployments have seen today in enterprises such as in the industrial IoT.

As the shape and size of each implementation will differ based on usage case, so too will the matching edge node (or edge computing system). In edge nodes, one size will certainly NOT fit all.

Following are a few of the essential factors to consider that are driving edge node style:

( 1)Type of edge network or service: This factor to consider will drive connection options such as 4G or 5G or private LTE or WiFi for regional and cloud connection of the node. It will also drive the software application that the node will need. This type of edge node will find use in business security, iot, personal privacy functions, and multi-tenancy options.

( 2) Type of work: The moving information gravity mainly drives Edge-native applications to the edge, such as from sensing units, video cameras, Lidars, and others. The task for this class of applications is associated with pre-processing or processing this information. Thereby leading to a limited set of specialized work, e.g., maker vision, time-series information analytics, deep knowing reasoning, signal processing, and comparable.

The majority of these work benefit substantially from unique function accelerators such as ASICs, FPGA, and GPU to satisfy efficiency/ watt and efficiency/ expense objectives. Very important in this context is how the node’s compute abilities appear like to designers. The type of APIs would these edge accelerators be provided.

( 3) Type of Data : Data source user interfaces, security, and personal privacy, storage is driven by volume and life expectancy, information company. The information is processed throughout end-points, edge, cloud, along with arranged and saved is a considerable part of edge computing. We depend on partners who are professionals in this location to assist us to resolve this.

( 4) Decisions are driven by data processing: Edge will exist to set off low latency choices. Hence edge nodes will require the ideal user interfaces to carry out such decisions whether autonomously or with human-in-the-loop.

( 5) Scaling the deployment and operation of date: In our viewpoint, this is among the essential elements of edge releases, and edge nodes will require to supply the best assistance for this function.

Conclusion: The choice of Edge computing nodes depends on many factors like the type of network, type of workload, type of data, requirements for data processing, and scaling requirements. These are a complex set of requirements typically managed and solved with support from Edge computing experts.

Footnotes:

Mobodexter, Inc based in Redmond, WA builds IOT Edge solutions for enterprises applications on Kubernetes & Dockers.

Register now at developers.paasmer.co and enjoy free Edge trial license that can run on Raspberry Pi.

Follow our all our weekly IoT Blogs: https://blogs.mobodexter.com

Join our IoT Hub of 3000+ subscribers to get these blogs in Email –  Subscribe.

Become our affiliate partner today.