With the Internet of Things (IoT) gaining popularity and widely used, more processing power and huge data is getting generated on the periphery of computer networks. Earlier, such data generated by IoT devices is transferred to the network server which is located in a centralized location. After processing the data, further tasks are sent back to such devices on the edge of the network.
This generates a whole new problem in processing a vast amount of data. First, the time taken for processing the data that was transferred from the edge device to the central server may cause a delay. Secondly, transferring huge data across the network and devices may put stress on the bandwidth. This process may build up latency that would result in huge losses to the organization.
Currently, most of the existing IoT applications perform their computations in the cloud with the help of large centralized servers. With edge computing, it relocates the entire process from data storage to computing in a decentralized location – closer to the end-user. It brings data storage and computation closer to the data source or the edge of the network. It deals with running an application as close as possible where data is getting generated so that it eliminates lag-time and thus saving bandwidth.
Edge Computing Definition – Edge Computing is an alternative approach to computing and storing data in the cloud environment. It involves the distribution of computing resources and application services along the communication channel through a decentralized computing infrastructure. It places the resources closer to the end user’s device rather than at a centralized data center located far away in the network. It provides more processing data close to the source. This process minimizes the data dependency on the application services and speeds up the data processing process.
Although Edge and Cloud Computing are two different technologies, both cannot replace or interchange one another when it comes to their applicability. The process involved in edge computing is used for time-sensitive data, while cloud computing processes data that are not time-driven. Edge computing is preferred over cloud computing in remote locations that require local storage and while operating specialized and intelligent devices.
In the case of cloud computing, when smart devices process the data they are piled up before it is being dispatched to the cloud. This leads to overloading in the cloud’s data centers and network system. Thus, cloud systems offer challenges in terms of latency in accessing data and inefficiency. Edge computing, on the other hand, helps analyze data close to the source. This not only minimizes data dependency on the application service but also speeds up processing the data.
Most of the Edge Computing Vs Cloud Computing debates discuss which technologies are exclusive approaches to an IT Infrastructural requirement. Rather it should be discussed alongside the other as one does not eliminate the capacity to use the other. Both have different critical and distinguishable roles to play within the infrastructure. This being said, there are several benefits of deploying edge over cloud computing. They are:
Collection, analyzing, processing of data, and performing other actions are done locally on the edge of the network. Therefore, such processes are completed in a millionth of a second. Moreover, it brings analytic capabilities much closer to the end user’s device and provides options for enhancing the infrastructural performance.
When it comes to cloud computing, bandwidth, latency, and data migration doesn’t come cheap. And the inefficiency in cloud computing can be significantly reduced by deploying edge computing. Moreover, it provides protection while handling sensitive IoT data and addresses other security and compliance protocols in delivering operational performance.
Besides giving no delays in terms of performance, edge computing also gives a real-time data analysis that works great on processing large amounts of data in no time. Moreover, since the data is processed locally and stored it requires less network traffic and delivers fast performance.
Some of the areas where applications of edge computing have demonstrated to be useful are listed below:
Self-driven vehicles require a transfer of massive volumes of data from the traffic and nearby surroundings to work in real-time efficiently. Latency issues might result in hazardous circumstances.
All streaming services create a massive amount of workload on the network bandwidth. A smooth streaming service is possible via edge caching as it facilitates users for easier and quicker service without disruption.
Smart Homes and Smart City services take too much network bandwidth and cannot rely on conventional cloud computing applications. It needs faster data processing and response time to cater to emergency needs. Processing of massive data closer to the source reduces latency and increases the response time. Thus, a combination of cloud as well as edge computing is necessary for law enforcement, emergency, and medical services.
Drones and UAVs have a similar application to AI-powered cars when it comes to data processing and response time dynamically. Any delay or data latency may result in disastrous situations. Thus, moving the data computation closer to the source improves the quality of the service by reducing latency and data disruption.
The cloud vs edge computing issue does not find merit in deducing which one is better than the other. Rather, edge computing fills the gap and provides solutions that cloud computing could not give. When it comes to the retrieval of huge data and resource-consuming applications that need a real-time solution, edge computing offers flexibility and brings the data closer to the end-users.
Therefore, both cloud computing and edge computing complements each other in delivering a responsive system that is free from disruptions. They both work effectively together and in certain applications, edge computing addresses some of the shortcomings of cloud computing. High latency, data privacy, fast performance, and geographical flexibility being some of the shortcomings covered by edge computing over cloud computing.
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