Top 5 Edge Computing Platforms in 2022 and Beyond

Introduction to Edge Computing Platform

Nowadays, with the arrival of 5G fast wireless, placing compute storage and deployments of Internet of Things (IoT) analytics close to where the data is created makes a case for edge computing.

The expansion of the Internet of Things devices and new applications needs real-time computing power. The 5G wireless are considering edge computing systems to fast track the support of real-time applications or creation like AI, analytics, robotics and self-driving cars, and many more. Initially, the goal of edge computing was to address the cost of high frequency for the data moving over long distances because of the extension of IoT-generated data.


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What is Edge Computing?

Edge computing is a networking ideology that focuses on conducting computing as close to the origin of data to decrease bandwidth and latency use. Conducting computation to the network's edge decreases the amount of long-distance communication which has to happen between a server and client.

Edge computing is a shared computing framework that brings venture applications closer to data sources like local edge or IoT devices. The presence of data at its source can have strong business benefits, improved response times, faster insights, and better high-frequency availability.

Why the need for Edge Computing?

Edge computing helps undertake high frequency, addressing cost and latency issues across many IoT applications. Here are the three key reasons why there is a need for edge computing:

Reduce the Signal to Noise Ratio

Edge computing helps in reducing the signal-to-noise ratio allowing companies to prioritize their data. Such as focusing on crucial data which needs to be processed, stored, and analyzed shortly. For example, monitoring a refrigeration unit. The data collected is machine-dominated, and machine-generated is ok at measuring state data.

From time to time, the machine will generate an event. For example, it will generate that it is not ok, so here this is what the observing company will care about. Everything other than this will be noise data. Edge computing helps in classifying the data which need attention.

Reduce the amount of Data Stored and Transmitted in the cloud

Nowadays, the amount of data continuously produced at the edge is growing rapidly faster than the potential for networks to process it.

Rather than sending data to a remote data center or to the cloud to do the work, sending it to endpoints should communicate data to an Edge computing device that will process that data.
The goal is to decrease latency and cost while governing the network bandwidth. An advantage of Edge Computing is that it reduces data needed to be stored and transmitted in the cloud.

Reduce the lag time in Processing/Transmission

Edge computing lowers the lag between the processing, transmission, and the action required at the end. Event processing and analysis can be done desperately and cost-effectively because much of the source data does not need to be operating in the cloud to be analyzed and processed. Cloud data centers can be thousands of distances far away from any connected device. The particular kinds of intermission for autonomous vehicles, precision manufacturing, and robotics surgery are a relative lifetime. This cycle can be reduced to just a few milliseconds with Edge computing.


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What are the Best Edge Computing Platforms of 2022?

Edge Computing platforms can be established for an extensive range of use cases, such as healthcare, for making clinical decisions in industrial environments like mining, manufacturing, and telecom. Here can be either open-source or paid, and the selection of platforms should depend on the organization.

Here are the top 5 edge computing platforms.

Azure IoT Edge

Azure IoT Edge is a part of Microsoft's intelligent cloud-to-edge computing solution suite. Securely and remotely manage and deploy cloud-native workloads - such as Azure services and AI to run directly on your IoT device.

What are the features of Azure IoT Edge?

The features of Azure IoT Edge are described below:

IoT Enablement: Azure IoT Edge platform is built for the IoT applications and can be capitalized alongside containers, certified IoT hardware, and Azure Stream Analytics.

Analytics Insights: The Microsoft Azure Admin center provides a comprehensive understanding of Edge operations.

Edge security: Azure IoT Edge platform is unified with Azure Defender for IoT, and Edge devices can be casually monitored.

Cloud to Edge Infrastructure: One can obtain Azure's IoT Hub, which supports architecture provisioning and Zero-touch devices.

Eclipse ioFog

This platform is a complete edge computing platform that provides all of the pieces needed to run and build applications at an edge at an enterprise scale. It is an integrated development environment backed by IBM and built by the Eclipse Foundation.

What are the feature of Eclipse ioFog?

IoT enablement: It can be adjusted to various use cases through Kubernetes development.

Analytics insights: The controller hub gives you analytics insight and remote visibility.

Edge security: It has an open infrastructure which means it has properties. You can connect it with third-party security services.

Cloud to edge infrastructure: Through Eclipse ioFog you can connect cloud infrastructure to the edge with the help of built-in connectors.

ClearBlade

ClearBlade provides edge computing software for expensive IoT applications in industrial environments. It is an Austin-based company that was founded in 2007.

What are the best features of ClearBlade?

The features of ClearBlade are described below:

IoT enablement: It is adaptable to most IoT devices, protocols(ZeroMQ, ZigBee, BlueTooth), and IoT systems

Analytics insights: From these insights, one can easily stream and filter the data at the edge and obtain acumen from the consolidated edge platform.

Edge security: In Analytics insights, edge access is safe through multiple authorizations, authentication, and encryption layers.

Cloud to edge infrastructure: It carries code portability, which can be expanded in the cloud and directed to the edge.


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Alef private Edge Platform

Alef private Edge Platform gives edge connectivity products for healthcare, governments, the industrial sector, and education. It was founded in 2009 by a New York-based edge computing company.

The features of Alef private Edge Platform

IoT Enablement: Alef Private Edge Platform powers a combination of ideal for running IoT devices, 5G, and edge network connectivity.

Analytics Insights: In Alef's private Edge Platform, one can manage the environment from a view of operational analytics and a centralized platform.

Edge Security: In this platform, one can easily control to reduce the risk of exposure.

Cloud to Edge Infrastructure: This platform makes it easy to deploy edge architecture from the cloud without an understanding of 3GPP standards.

Google Distributed Cloud Edge

Google Distributed Cloud Edge is a completely managed product that brings service and Google cloud infrastructure. It was Launched in 2021 as part of the Google Distributed Cloud suite.

What are the best features of Google Distributed Cloud Edge?

Following are the best features of Google Distributed Cloud Edge:

IoT Enablement: In this Google Distributed Cloud Edge platform, one can purchase serverless infrastructure and containers to run on IoT applications.

Analytics Insights: Google's AI data analytics solutions help gather data insights and enable edge observability in these databases.

Edge Security: It accommodates privacy and security requirements and can combine with Third-party providers.

Cloud to Edge Infrastructure: In this platform, one can easily change from the edge using Google cloud architecture services and from on-premise to cloud.


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What are the benefits of Edge Computing?

The benefits of Edge Computing are described below:

Cost savings

Edge computing helps in minimizing the server resources and use of bandwidth. Cloud resources and Bandwidth are cost money and finite. Nowadays, every office and household is enhanced with thermostats, smart cameras, printers, and toasters, from which Statista predicts that by 2025 there will be over 75 billion IoT devices installed worldwide.

Performance

Another remarkable benefit of moving processes to the edge is to lower the discontinuation. Every time a device needs to communicate with another server, that creates a delay. If this process is conducted to the edge and the router is in charge of moving intra-office chats, this delay would not exist anymore. Same as when users of all web applications run into processes that have to communicate with an external server, they will definitely experience delays. So these delays can be escaped by bringing more processes to the network edge.

New functionality

Additionally, edge computing can develop new functionality that wasn't previously available. An organization can use edge computing to analyze and process their data at the edge, making it feasible to do so in real-time.


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What are the challenges of Edge computing?

The following are the challenges of Edge computing:

  • One main drawback of edge computing is that it can grow attack vectors. With the extension of more smart devices, like IoT devices and edge servers with built-in solid computers, there are new events for malevolent attackers to arrange these devices.
  • The second drawback with this edge computing is that it needs more local hardware. For example: while an IoT camera needs a built-in computer to address its raw video data to a web server, it would need a much more refined computer with more processing power to run its own motion-detection algorithm. But the reduction in hardware cost makes it expensive to build smarter devices.

Conclusion

The adoption of cloud computing has brought data analysis to a new level. Cloud connectivity has enabled the most accurate way to capture and analyze data. Still, things are now working very well with a computer on edge. As a result, the level of business activity has become huge.
Edge computing is an effective solution for data-driven tasks that require fast results and a high degree of flexibility, depending on the current state of affairs.