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Contents

IoT architecture
CLOUD AND EDGE
Edge computing
Cloud Computing
Cloud Computing Vs Edge Computing
Challenges of edge computing
Benefits of Edge computing
Edge Computing vs. Fog Computing

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IoT architecture and big data analytics

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IoT architecture and big data analytics

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CLOUD AND EDGE

A cloudA cloud is an IT environment that abstracts, pools, and shares IT resources across a network. An edgeA cloud is an IT environment that abstracts, pools, and shares IT resources across a network. An edge is a computing location at the edge of a network, along with the hardware and software at those physical locations. Cloud computing is the act of running workloads within clouds, while edge computing is the act of running workloads on edge devices.

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Edge cloud

Cloud computing has brought incredible transformation to businesses of all sizes. Today, enterprises are augmenting cloud computing with edge computing for certain types of workloads, such as latency-sensitive applications

Edge cloud computing extends the convenience of the cloud to edge networks. Edge clouds are hosted by micro-data centers that store, analyze, and process data faster than is possible using a connection to a data center.

Edge cloud solutions enable data collection and analytics closer to the source to deliver actionable business intelligence and better customer experiences.

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Edge Cloud and Data Centers

Typically, cloud services run on large-scale data centers. These may be private clouds hosted by on-premises infrastructure or public cloud services from providers such as Amazon Web Services, Google Cloud, or Microsoft Azure.

While cloud computing excels in resource-intensive data processing and workloads like AI training, it creates issues with latency for some workloads as data must travel all the way to the data center and back again. In many cases, businesses can achieve real-time intelligence with edge computing.

An edge cloud strategy places intelligent edge nodes closer to local resources, equipment, and devices, with software to deliver services in a way that’s similar to using public cloud services.

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Edge Servers

Edge servers act as micro-data centers, delivering the computing power for an edge cloud.

While they often look like a typical server, many other form factors exist.

For example, a ruggedized laptop, a purpose-built appliance, or a robust edge device featuring onboard intelligence can perform as an edge server.

A distributed content delivery network (CDN) combines the benefits of origin servers and edge servers to bring content closer to end users.

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CLOUD AND EDGE

Kind of.

Edge devices can contribute to a cloud, if the storage can contribute to a cloud, if the storage and computing capabilities provided by those devices at the endpoints of a network are abstracted, pooled, and shared across a network—essentially becoming part of a larger cloud infrastructure.

Edge computing is not part of a cloud. What makes edge computing so useful is that it is purposefully separate from clouds and cloud computing.

"Edge computing can apply to anything that involves placing service provisioning, data, and intelligence closer to users and devices."

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CLOUD AND EDGE

Here’s how we see it:

Clouds are places where data can be stored or applications can run. They are software-defined environments created by datacenters or server farms.

Edges are also places where data is collected. They are physical environments made up of hardware outside a datacenter.

Cloud computing is an act; the act of running workloads in a cloud.

Edge computing is also an act; the act of running workloads on edge devices.

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IOT, EDGE COMPUTING and CLOUD

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CLOUD , EDGE computing and Edge cloud

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CLOUD AND EDGE

An edge (location) is not the same thing as edge computing (action). Collecting data at the edge of a network and transferring it to a cloud with minimal (if any) modification is not edge computing—it’s just networking.

But, if that data is collected and processed at the edge, then it’s edge computing.

Edge computing is separate from clouds for 2 main reasons:

Time sensitivity. The rate at which a decision needs to be made doesn’t allow for the lag that would normally take place as data is collected by an edge device, transferred to a central cloud without modification, and then processed before a decision is sent back to the edge device for execution.

Data volume. The sheer volume of data collected is too much to send—unaltered—to a cloud.

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Cloud, edge, and IoT

They all can be connected. But they don’t have to be connected.

Clouds can exist without the Internet of Things (IoT) or edge devices.

IoT and edge can exist without clouds.

IoT can exist without edge devices or edge computing.

IoT devices may connect to an edge or a cloud. Some edge devices connect to a cloud or private datacenter, others edge devices only connect to similarly central locations intermittently, and others never connect to anything—at all. Ever.

But edge computing, when used as part of manufacturing, mining, processing, or shipping operations rarely exists without IoT. That’s because IoT devices—everyday physical objects that collect and transfer data or dictate actions like controlling switches, locks, motors, or robots—are the sources and destinations that edge devices process and activate without relying on a central location or cloud.

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For example:

Home automation is generally an IoT exercise. Your phone and smart home devices (light bulbs, thermostats, and outlets) are all IoT devices, because they simply send data—and execution decisions—back and forth (sometimes through a cloud). Neither your phone nor smart devices are processing the data they collect.

Satellite imagery—like the kind being used on the International Space Station (ISS)—like the kind being used on the International Space Station (ISS)— is an edge computing exercise. Edge devices physically located on the ISS are running containerized analytical code as a single-node Red Hat® OpenShift® cluster that connects to IBM Cloud on Earth. Only images that are worth transfering are sent down to the ground. Edge computing is a necessary step here because the sheer volume of data collected is too much to send to an Earth-based cloud.

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Cloud Computing Vs Edge Computing

NOW A DAYS FOR ENHANCING THE BUSINESS cloud computing plays a significant role in making the best decisions possible. A cloud-based platform enables developers to rapidly build, deploy and manage their applications, such as serving as a data platform for applications, build an app to scale and support millions of users and interactions, and more. It can store large amounts of data and perform analytics, create powerful visualisations, and more.

Then there is edge computing, which means that applications, services, and analytical processing of data is done outside of a centralised data centre and closer to end-users. Edge computing closely aligns the Internet of Things. It is a step back from the trendy cloud model of computing where all the exciting bits happen in data centres. Instead of using local resources to collect data and send it to the cloud, part of the processing takes place on the local resources themselves.

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Latency Problems In Cloud Vs Edge

Some internet-connected sensors in your warehouse, and these are sending lots of data back to some servers. When the data is transmitted to the remote cloud server, you can perform complex machine learning algorithms to try and predict maintenance needs for the warehouse. All these meaningful analytics are then sent to a dashboard on your personal computer where you can determine which actions to take next, all from the comfort of your office or home.

This is the power of cloud computing; however, as you begin to scale up operations at the warehouse, you might start to run into physical limitations in your network bandwidth, and latency issues.

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Latency Problems In Cloud Vs Edge

Instead of transmitting your data across the country when you upload to the cloud, you can also do data processing at the edge, like a smart camera with facial recognition where sending tons of data to an Amazon data centre might not be so convenient.

Edge computing attempts to bridge the gap by having that server more local, sometimes even on the device itself. This solves the latency problem at the cost of the sheer processing power you get via the cloud. Also, with collection and data processing ability now available on edge, businesses can significantly decrease the volumes of data which has to upload and stored in the cloud, saving time and funds in the process.

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Latency Problems In Cloud Vs Edge

While edge applications do not require communication with the cloud, they may still communicate with servers and web-based applications. Many of the typical edge devices have physical sensors such as temperature, lights, speakers, and running data processing capability closer to these sensors in the physical environment. It is this capability of edge computing that is transformational and used for running smart AI algorithms and real-time data processing on autonomous driving, drones and smart devices.

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Arch diagram for edge computing

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Arch diagram for edge computing

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Challenges of edge computing

Data lifecycles.

Security.

Connectivity

Limited capability

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An edge cloud offers several unique benefits:

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Benefits
Proximity	Edge technology keeps sensitive or proprietary information closer to the source and enables compliance with data localization laws.
Cost	The cost of bandwidth for large-scale data transmission adds up. Placing computing at the edge reduces these costs.
Real-Time Insights	No matter how fast a 5G or network connection may be, large data volumes take time to transfer over long distances. An edge cloud can reduce the latency for edge applications that depend on real-time information.

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One example of the edge cloud

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One example of the edge cloud at work is in the delivery of visual experiences. Streaming services, cloud gaming, and other visual workloads are growing significantly.

To accommodate them, cloud and communication service providers are shifting workloads to the network edge.

Edge cloud services hosted on micro-data centers help support these visual workloads, achieving the reduced latency needed for great customer experiences.

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Edge Computing vs. Fog Computing

“Fog computing and edge computing are effectively the same things. Both are concerned with leveraging the computing capabilities within a local network to carry out computation tasks that would ordinarily have been carried out in the cloud,” said Jessica Califano, head of marketing and communications at Temboo.

The main difference between edge computing and fog computing comes down to where data processing occurs.

“Edge computing usually occurs directly on the devices to which the sensors are attached or a gateway device that is physically “close” to the sensors.

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Edge Computing vs. Fog Computing

Fog computing moves the edge computing activities to processors that are connected to the LAN or into the LAN hardware itself so that they may be physically more distant from the sensors and actuators.” said Paul Butterworth, co-founder, and CTO at Vantiq.

Along these lines, with Fog computing, the information is prepared inside a node or IoT gate, which is arranged inside the LAN. As for edge computing, the information is handled on the device or sensor without moving anyplace.

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Edge Computing vs. Fog Computing

Fog and Edge computing are more suitable for the quick analysis required for real-time response.

edge computing is where data processing occurs.

Edge computing occurs most of the time our IoT sensors are connected.

Fog Computing shifts the edge computing tasks connected to the LAN hardware or for LAN direct to be more distant to the sensors.

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EDGE CLOUD ARCH

endpoint devices like smart cameras serve as a “front line” for edge computing

Edge servers and origin servers offer a secondary layer for edge computing.

A primary data center operates as another layer, residing furthest away from physical endpoints.

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