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An Introduction to Edge Computing
Internet of Things (IoT) is conquering the world by storm, as it has become one of the most influential buzzwords not only in the tech sector, but also many other businesses. From farms to factories and smart cities to homes, IoT technology is all over the place with a continually expanding set of connected systems and devices. According to Statista, the installed base of IoT devices is forecast to grow to almost 31 billion worldwide. As a result, cloud computing will emerge as an increasingly dominant trend as the enormous amount of data generated by billions of connected IoT devices need to be stored for processing and retrieval. Both the technologies - IoT and cloud computing are interconnected, with one providing the other a platform for success.
In a traditional IoT architecture, data are collected from geographically dispersed sensors and transported to a central repository where it is combined and processed collectively. Increasing efficiency, scalability and performance in everyday tasks, integration of cloud computing with Internet of Things enable enterprises to make better business decisions faster and respond to changing market conditions in real time.
IoT connections are expected to boom in coming years projecting a reach of 13.7 billion by 2021 (Cisco Systems predictions) thereby increasing the need for data center and cloud resources. Streamlining the unprecedented flow of traffic from all the connected devices, aggregating data and extracting actionable insights, an IoT/Cloud convergence proves to be the perfect partners for a data-driven world.
While cloud computing has made it possible to process massive amounts of data, it won’t be an ideal choice for all applications and use cases. Huge amounts of data being sent back and forth from the frontlines of sensors to servers clogs network bandwidth thereby slowing down response times. The answer to address all such limitations associated with traditional cloud computing infrastructure is something called “Edge computing”.
Unlike traditional cloud architecture that follows a centralized process, Edge computing decentralizes most of the processes by pushing it out to the edge devices and closer to the end user. Since storage capacity and processing power are decentralized, it would provide precise results for IoT deployments. Making it easier to operate and manage IoT devices, edge computing ensures low-latency access, reduced bandwidth consumption, offline availability and local Machine learning (ML) inference.
Low latency and faster real-time analysis of edge computing have a number of applications across various sectors such as automotive, consumer electronics, energy, health care and more. Autonomous vehicles are a strong use case in point, where data needs to be collected from the surrounding environment and cloud to make decisions quickly and safely. Patterns in sensor data should be detected, stored and transferred quickly to aid real-time decisions at local nodes. Decentralized architecture of edge computing negates the latency in communication of critical data thereby ensuring safety.
According to CB Insights Market Sizing tool, the global edge computing market is estimated to reach $6.72B by 2022. As more and more connected devices emerge into the world, tech giants are heavily investing in a sophisticated edge computing strategy. Up until now, Amazon, Microsoft and Google have forayed into Edge computing. Amazon was ahead in the emerging tech space by launching its edge platform- AWS Greengrass in 2017,while Microsoft jumped on the bandwagon with its Azure IoT Edge solution last year. Google also joined the race by rolling out two new products - an integrated software (Cloud IoT Edge) and custom hardware stack (Edge TPU) in order to leverage data directly at the edge.
To sum it up, we can say that Edge computing is not here to replace cloud computing, but to complement it. Since edge computing technology is still in its infancy, challenges are likely to rise. But with the increasing demand for edge devices and applications, there will be more opportunities for enterprises to test and deploy this technology across various verticals.
In a traditional IoT architecture, data are collected from geographically dispersed sensors and transported to a central repository where it is combined and processed collectively. Increasing efficiency, scalability and performance in everyday tasks, integration of cloud computing with Internet of Things enable enterprises to make better business decisions faster and respond to changing market conditions in real time.
IoT connections are expected to boom in coming years projecting a reach of 13.7 billion by 2021 (Cisco Systems predictions) thereby increasing the need for data center and cloud resources. Streamlining the unprecedented flow of traffic from all the connected devices, aggregating data and extracting actionable insights, an IoT/Cloud convergence proves to be the perfect partners for a data-driven world.
While cloud computing has made it possible to process massive amounts of data, it won’t be an ideal choice for all applications and use cases. Huge amounts of data being sent back and forth from the frontlines of sensors to servers clogs network bandwidth thereby slowing down response times. The answer to address all such limitations associated with traditional cloud computing infrastructure is something called “Edge computing”.
Unlike traditional cloud architecture that follows a centralized process, Edge computing decentralizes most of the processes by pushing it out to the edge devices and closer to the end user. Since storage capacity and processing power are decentralized, it would provide precise results for IoT deployments. Making it easier to operate and manage IoT devices, edge computing ensures low-latency access, reduced bandwidth consumption, offline availability and local Machine learning (ML) inference.
Low latency and faster real-time analysis of edge computing have a number of applications across various sectors such as automotive, consumer electronics, energy, health care and more. Autonomous vehicles are a strong use case in point, where data needs to be collected from the surrounding environment and cloud to make decisions quickly and safely. Patterns in sensor data should be detected, stored and transferred quickly to aid real-time decisions at local nodes. Decentralized architecture of edge computing negates the latency in communication of critical data thereby ensuring safety.
According to CB Insights Market Sizing tool, the global edge computing market is estimated to reach $6.72B by 2022. As more and more connected devices emerge into the world, tech giants are heavily investing in a sophisticated edge computing strategy. Up until now, Amazon, Microsoft and Google have forayed into Edge computing. Amazon was ahead in the emerging tech space by launching its edge platform- AWS Greengrass in 2017,while Microsoft jumped on the bandwagon with its Azure IoT Edge solution last year. Google also joined the race by rolling out two new products - an integrated software (Cloud IoT Edge) and custom hardware stack (Edge TPU) in order to leverage data directly at the edge.
To sum it up, we can say that Edge computing is not here to replace cloud computing, but to complement it. Since edge computing technology is still in its infancy, challenges are likely to rise. But with the increasing demand for edge devices and applications, there will be more opportunities for enterprises to test and deploy this technology across various verticals.
