Difference between Edge Computing and Cloud Computing

Parameter Edge Computing Cloud Computing
Definition Edge Computing is a distributed computing architecture that brings computing and data storage closer to the source of data. Cloud Computing is a model for delivering information technology services over the internet.
 
Location of Processing Processing is done at the edge of the network, near the device that generates the data. Data Analysis and Processing are done at a central location, such as a data center.
 
Bandwidth Requirements Low bandwidth is required, as data is processed near the source. Higher bandwidth is required as compared to edge computing, as data must be transmitted over the network to a central location for processing.
 
Costs Edge Computing is more expensive, as specialized hardware and software may be required at the edge. Cloud Computing is less expensive, as users only pay for the resources they actually use.
 
Scalability Scalability for Edge Computing can be more challenging, as additional computing resources may need to be added at the edge. Easier, as users can quickly and easily scale up or down their computing resources based on their needs.
Use Cases Applications that require low latency and real-time decision-making, such as IoT devices, autonomous vehicles, and AR/VR systems. Applications that do not have strict latency requirements, such as web applications, email, and file storage.
 
Data Security Data security can be improved, as data is processed near the source and is not transmitted over the network. Data Security is more challenging, as data is transmitted over the network to a central location for processing.
 

Difference between Edge Computing and Cloud Computing

Edge Computing and Cloud Computing are the two paradigms in this modern digital world. Both are the growing paradigms for storing data on Cloud. In this article, we will first go through understanding the terms Edge Computing and Cloud Computing. We will discuss the Advantages along with Disadvantages of Edge and Cloud Computing in detail. By the end of the article, we will go through the fundamental difference between Edge and Cloud Computing. 

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Edge Computing is a distributed computing architecture that brings computing and data storage closer to the source of data. Data processing takes place at the network’s edge, adjacent to the device that generated the data, as opposed to a central location, such as a data center. Reduced latency and bandwidth needs are desired outcomes of edge computing when transferring large amounts of data to a processing center. Edge computing facilitates real-time decision-making by processing data close to the edge and accelerating data transfer to and from the cloud....

Cloud Computing

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Difference between Edge Computing and Cloud Computing

Parameter Edge Computing Cloud Computing Definition Edge Computing is a distributed computing architecture that brings computing and data storage closer to the source of data. Cloud Computing is a model for delivering information technology services over the internet.  Location of Processing Processing is done at the edge of the network, near the device that generates the data. Data Analysis and Processing are done at a central location, such as a data center.  Bandwidth Requirements Low bandwidth is required, as data is processed near the source. Higher bandwidth is required as compared to edge computing, as data must be transmitted over the network to a central location for processing.  Costs Edge Computing is more expensive, as specialized hardware and software may be required at the edge. Cloud Computing is less expensive, as users only pay for the resources they actually use.  Scalability Scalability for Edge Computing can be more challenging, as additional computing resources may need to be added at the edge. Easier, as users can quickly and easily scale up or down their computing resources based on their needs. Use Cases Applications that require low latency and real-time decision-making, such as IoT devices, autonomous vehicles, and AR/VR systems. Applications that do not have strict latency requirements, such as web applications, email, and file storage.  Data Security Data security can be improved, as data is processed near the source and is not transmitted over the network. Data Security is more challenging, as data is transmitted over the network to a central location for processing....