The explosive growth of internet of things (IoT) devices, and the increasing computing power of these devices, have resulted in unprecedented volumes of data. And data volumes will continue to grow as 5G networks increase the number of connected mobile devices.
In the past, the promise of cloud and artificial intelligence (AI) was to automate and speed innovation by driving actionable insight from data. But the unprecedented scale and complexity of data that’s created by connected devices has outpaced network and infrastructure capabilities.
Sending all that device-generated data to a centralized data center or to the cloud causes bandwidth and latency issues. Edge computing offers a more efficient alternative: data is processed and analyzed closer to the point where it is created. Because data does not traverse over a network to a cloud or data center in order to be processed, latency is significantly reduced. Edge computing — and mobile edge computing on 5G networks — enables faster and more comprehensive data analysis, creating the opportunity for deeper insights, faster response times and improved customer experiences.
1. Local devices that serve a specific purpose, such as an appliance that runs a building’s security system or a cloud storage gateway that integrates an online storage service with premise-based systems, facilitating data transfers between them.
2. Small, localized data centers (1 to 10 racks) that offer significant processing and storage capabilities.
3. Regional data centers with more than 10 racks that serve relatively large local user populations.
Regardless of size, each of these edge examples is important to the business, so maximizing availability is essential.
It’s critical then, that companies build edge data centers with the same attention to reliability and security as they would for a large, centralized data center. This site is intended to provide the information you need to build secure, reliable, and manageable high-performance edge data centers that can help fuel your organization’s digital transformation.
A retailer, for example, may use data from IoT applications to better serve customers, by anticipating what they may want based on past purchases, offering on-the-spot discounts, and improving their own customer service groups. For industrial environments, IoT applications can be used to support preventive maintenance programs by providing the ability to detect when the performance of a machine varies from an established baseline, indicating it’s in need of maintenance.
The list of potential use cases is virtually endless, but they all have one thing in common: collecting lots of data from many sensors and smart devices and using it to drive business improvements.
Many IoT applications rely on cloud-based resources for compute power, data storage and application intelligence that yields business insights. However, it’s often not optimal to send all the data generated by sensors and devices directly to the cloud, for reasons that generally come down to bandwidth, latency and regulatory requirements.
Similarly, autonomous vehicles, which operate with low connectivity, need real-time data analysis to navigate roads. Gateways hosted within the vehicle can aggregate data from other vehicles, traffic signals, GPS devices, proximity sensors, onboard control units and cloud applications, and can process and analyse this information locally.
This is hardly surprising given the increasing popularity of the IoT both in business and consumer use. And while we may still be a way off fully-autonomous vehicles those that are on the road already, or will be shortly, still need this type of technology to operate properly.
The analyst house has also predicted in its Hype Cycle for Emerging Technologies 2019 report that additional edge technologies notably AI and analytics will come to play a key role in this technology in the coming five to 10 years.
Another drawback with edge computing is that it requires more local hardware. For example, while an IoT camera needs a built-in computer to send its raw video data to a web server, it would require a much more sophisticated computer with more processing power in order for it to run its own motion-detection algorithms. But the dropping costs of hardware are making it cheaper to build smarter devices.
Download PDF - Edge Computing 1
Sources / References:
https://www.ibm.com/in-en/cloud/what-is-edge-computing
https://www.apc.com/us/en/solutions/business-solutions/edge-computing/what-is-edge-computing.jsp
https://www.itpro.co.uk/cloud/31389/what-is-edge-computing
https://www.cbinsights.com/research/what-is-edge-computing/
https://www.cloudflare.com/learning/serverless/glossary/what-is-edge-computing/
In the past, the promise of cloud and artificial intelligence (AI) was to automate and speed innovation by driving actionable insight from data. But the unprecedented scale and complexity of data that’s created by connected devices has outpaced network and infrastructure capabilities.
Sending all that device-generated data to a centralized data center or to the cloud causes bandwidth and latency issues. Edge computing offers a more efficient alternative: data is processed and analyzed closer to the point where it is created. Because data does not traverse over a network to a cloud or data center in order to be processed, latency is significantly reduced. Edge computing — and mobile edge computing on 5G networks — enables faster and more comprehensive data analysis, creating the opportunity for deeper insights, faster response times and improved customer experiences.
Deploying Edge Data Centers
While edge computing deployments can take many forms, they generally fall into one of three categories:1. Local devices that serve a specific purpose, such as an appliance that runs a building’s security system or a cloud storage gateway that integrates an online storage service with premise-based systems, facilitating data transfers between them.
2. Small, localized data centers (1 to 10 racks) that offer significant processing and storage capabilities.
3. Regional data centers with more than 10 racks that serve relatively large local user populations.
Regardless of size, each of these edge examples is important to the business, so maximizing availability is essential.
It’s critical then, that companies build edge data centers with the same attention to reliability and security as they would for a large, centralized data center. This site is intended to provide the information you need to build secure, reliable, and manageable high-performance edge data centers that can help fuel your organization’s digital transformation.
How IoT is Driving the Need for Edge Computing
The IoT involves collecting data from various sensors and devices and applying algorithms to the data to glean insights that deliver business benefits. Industries ranging from manufacturing, utility distribution, traffic management to retail, medical and even education are making use of the technology to improve customer satisfaction, reduce costs, improve security and operations, and enrich the end user experience, to name a few benefits.A retailer, for example, may use data from IoT applications to better serve customers, by anticipating what they may want based on past purchases, offering on-the-spot discounts, and improving their own customer service groups. For industrial environments, IoT applications can be used to support preventive maintenance programs by providing the ability to detect when the performance of a machine varies from an established baseline, indicating it’s in need of maintenance.
The list of potential use cases is virtually endless, but they all have one thing in common: collecting lots of data from many sensors and smart devices and using it to drive business improvements.
Many IoT applications rely on cloud-based resources for compute power, data storage and application intelligence that yields business insights. However, it’s often not optimal to send all the data generated by sensors and devices directly to the cloud, for reasons that generally come down to bandwidth, latency and regulatory requirements.
Real life examples of edge computing
Oil rigs provide a good example of how edge computing is used in the real world. Because of their remote offshore locations, they rely on the technology to mitigate lengthy distances to data centre and poor network connections. It's also costly, inefficient and time-consuming for rigs to send real-time data to a centralised cloud. Having a localised data processing facility helps a rig to run without delay or interruption.Similarly, autonomous vehicles, which operate with low connectivity, need real-time data analysis to navigate roads. Gateways hosted within the vehicle can aggregate data from other vehicles, traffic signals, GPS devices, proximity sensors, onboard control units and cloud applications, and can process and analyse this information locally.
What next for edge computing?
According to Gartner's Digital Business Will Push Infrastructures to the Edge report, data generated and processed by enterprises outside of a traditional data centre will increase from less than 10% in 2018 to 75% by 2022.This is hardly surprising given the increasing popularity of the IoT both in business and consumer use. And while we may still be a way off fully-autonomous vehicles those that are on the road already, or will be shortly, still need this type of technology to operate properly.
The analyst house has also predicted in its Hype Cycle for Emerging Technologies 2019 report that additional edge technologies notably AI and analytics will come to play a key role in this technology in the coming five to 10 years.
Drawbacks of edge computing
One drawback of edge computing is that it can increase attack vectors. With the addition of more ‘smart’ devices into the mix, such as edge servers and IoT devices that have robust built-in computers, there are new opportunities for malicious actors to compromise these devices.Another drawback with edge computing is that it requires more local hardware. For example, while an IoT camera needs a built-in computer to send its raw video data to a web server, it would require a much more sophisticated computer with more processing power in order for it to run its own motion-detection algorithms. But the dropping costs of hardware are making it cheaper to build smarter devices.
Download PDF - Edge Computing 1
Sources / References:
https://www.ibm.com/in-en/cloud/what-is-edge-computing
https://www.apc.com/us/en/solutions/business-solutions/edge-computing/what-is-edge-computing.jsp
https://www.itpro.co.uk/cloud/31389/what-is-edge-computing
https://www.cbinsights.com/research/what-is-edge-computing/
https://www.cloudflare.com/learning/serverless/glossary/what-is-edge-computing/
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