June 21, 2019
When edge computing started making rounds of the internet, some of the articles came up with statements like:
“The Edge will eat the cloud”
“The End of Cloud Computing Era”
“Edge Computing will blow away cloud”
“Edge will replace the cloud”
However, these headlines might be stinging you because you just understood the cloud and the digital world came up with the more advanced solution i.e. edge computing.
But, are these statements true?
Well No! Edge computing is just the extended version or advanced functionality of cloud computing.
“The edge computing market size is expected to grow from USD 1.47 Billion in 2017 to USD 6.72 Billion by 2022, at a CAGR of 35.4% during the forecast period.”
Let’s dive-deep to know thick and thins about
This article will take you to the journey of edge computing and help you to get a clear vision of edge computing.
According to Wikipedia, Edge Computing is a distributed computing which brings computer data storage closer to the location where it is needed. Computation is largely or completely performed on distributed device nodes.
Let’s simplify more,
Instead of holding and processing data in a cloud or central data center, edge computing processes the data at the edge (at which it was originated) of the network.
It makes the system and application more efficient by removing the components, data services from a centralized server and placing the data to the closer to the edge.
This reduces the flow of traffic to the central server and provides real-time local data analysis.
What Are The Edge Devices?
These can be any device that produces data. It could be sensors, industrial machines, routers, WANs, switch, etc.
They will act as small data centers that are able to communicate important information with each other.
It is universally true that cloud computing helps to achieve mass centralization, data processing and enables us to focus around storage and scaling.
But challenges of latency still exist.
When data travels miles from cloud data-center to the end-user, it goes over thousands of miles to the end-users; so delays are considerable.
Thus, devices are unable to take instant action.
A split-second in the delay of the data going to the cloud and back to the device can result in a serious problem.
Latency is not completely avoidable.
A self-driven autonomous car
Safety is always the first concern of connected and autonomous cars. Driverless cars need to ensure that they are keeping to their lanes, recognizing and stopping at the red lights, decelerating by identifying pedestrians and other obstacles and much more. All this requires a massive amount of sensory data in real-time. The car needs to send terabytes of data to the central server and then receives the response and then act.
In case of any mishap, the car needs to make crucial decisions. if we send such massive data to the centralized cloud servers to process and get the response it can result in disaster, as It is not fast enough to respond to the immediate dangers in real-time.
In such a scenario, edge computing can help.
It reduces strain on the network and provides better reliability by reducing the time lag between data processing and transferring the data to the vehicle. It can perform data analytics and speed up the analytics process, allowing the driverless car to take instant action.
“It’s not in a cloud; it’s not in data centers, its right in the computer in the car. Engines can learn how to drive themselves without being reliant on connectivity,”
Businesses can benefit from reduced latency with instant access to real-time data in remote locations. The main purpose of edge computing is to decentralize data handling.
If you are collecting the data from the cloud and unable to provide the expected speed and agility, then edge computing is surely the best choice!
To learn more about the advantages of edge computing and it’s real-life use-cases read on.
When you think to develop IoT products, the cost is always the concern due to its network bandwidth, data storage, and computational power. But, edge computing can lower the IT cost by processing and analyzing the important data locally.
When data is stored and processed at the edge, you don’t need more cloud storage. As smaller operations are involved and less data management expenses of the local devices, it will reduce the data transaction costs.
When the number of devices is connected, it increases the chance of an overall attack on the networks. Traditional cloud computing is centralized so it is vulnerable to DDoS attacks and power outages.
But, edge computing distributes processing, storage across devices and data centers which make it difficult for any single disruption to take down the networks. Moreover, edge computing filters out sensitive information and transfers only non-sensitive data for further processing to adhere to strict security and compliance. As there are fewer data intercepted during transit, it is easier to implement security protocols.
As microdata centers locally store and process the data, the IoT applications consume less bandwidth and work even when the connection to the cloud is affected. Edge computing operates in conditions with limited connectivity so business operations can be carried out without worrying about the data loss.
As many edge computing devices and edge data centers are connected to the network, there are least chances for one failure to shut down entire services. Data can be routed through other paths to ensure that users can have access to the product and information as and when they need it.
With Edge Computing, it is easy to convert the communication protocols used by the legacy device into a language that is easily understood. This makes it easy to connect legacy tools with modern IoT platforms.
Thus, businesses can immediately capture the insights across their operations without investing in new equipment.
Although technology increases productivity, it has its drawbacks and edge computing is no exception.
Most of us have experienced occasional shortcomings like video delays, bad video quality, frozen screen-share and much more. Delivering high-quality video is complex due to the slow response back to the cloud.
By placing the server of a voice close to the participants, these kinds of quality problems can be reduced drastically. Edge video server would enable the resilient and responsive user experience for participants.
In urban areas sensors are placed for collecting data on traffic patterns and utility usage. It gathers a massive amount of information every day. All this information must be collected, stored and analyzed before responding to the problems.
Traditional cloud solutions aren’t able to provide immediate response for the device operating on the outskirts of the network. Edge computing makes it possible to respond to the changing condition in near real-time by collecting the data to carry out basic processing task.
Oil Rig has thousands of sensors producing a massive amount of data and most of them are not important, but determines that the system is working properly.
It is not necessary to send every data over the network as soon as it is produced. Instead, the edge computing system can compile the data and send daily reports to the cloud for long-term storage. By sending important data to the network, the edge computing system reduces the data traversing. It can even help in predictive maintenance to identify the potential breakdowns before they impact the production.
Edge computing has not yet become main-stream for business, but there is a number of companies that have already adopted and are streamlining productivity through edge computing.
Amazon announced the general availability of AWS Greengrass – the software that powers local edge gateways and appliances. It delivers three benefits:
Dropbox, leading file storage and sharing company, has developed its own edge network to deliver better connectivity and faster file access for its customers. The company shifted to edge to provide consistent and reliable service to all its customers.
With this shift, speed was increased by 300% and performance was improved to the next level. With edge computing, download and upload speed were increased throughout Asia and Europe and latency were improved by 5x.
General Electric supports a large number of edge devices – up to 200,000 connected devices, which enable faster and more efficient processing at low latency.
Edge solutions drive real-time insights of major industrial assets while also connected securely to the cloud. This technology delivered the outcome that resulted in increased revenue, efficiency and reliability across multiple industries.
Well, No! Edge computing doesn’t mean the death of the cloud era.
Edge computing is more of a complement to cloud computing; it can’t replace the cloud computing because there will always be the need for central processing. It needs a cloud to configure, deploy and manage IoT devices for analyzing large datasets from different data sources.
It makes a sense to perform IoT analysis at the edge when instant action is needed and for faster data processing, but the data will likely need to be collected and centrally stored.
Ultimately cloud computing will remain an integral part of any edge computing environment.
Edge computing being in an infancy stage, it is difficult to make any justifying statement.
However, from the above highlights about edge computing, it seems that edge computing is becoming a key part of IoT Development.
Edge computing is much more than simply redistributing data to the endpoints. There is no denying that it will enable businesses to perform advanced analytics in new ways. The rise in the localized data centers may make it easier for organizations to expand their network reach.
Going forward we may see more and more applications in healthcare, AR, VR, drones, smart cities and many more.
It even seems that the future holds the best for Edge computing and it is here to stay for many more years ahead.
What’s your opinion? Please share your feedback in the comment section below.
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