Cloud Computing

Edge Computing: How it provides Edge Over Cloud Computing

Cloud computing

What is the need for Edge Computing if there is Cloud Computing?

Cloud computing has been one of the biggest driving forces in technological advancement in the last decade. It had leveraged businesses with tremendous amounts of data to operations and sales based on actionable derived data. Then, why edge computing preferred more than cloud computing? How edge computing and cloud computing are different than each other? On the other hand, the main reason behind it is the edge computing made businesses make well-informed decisions as never before.

As technology advancement is a never-ending process. Likewise, people always come up with even more advanced technologies to fulfill their varied needs. There were some drawbacks with cloud computing that needed to be fulfilled where edge computing came. Werner Vogels, CTO of Amazon Web Services (AWS), has noticed 3 major issues with cloud computing that edge computing solves.

Drawback #1: The Law of Physics

The first drawback of cloud computing is simple physics according to Vogels, and he calls it “the law of physics”. As cloud computing was based on a centralized data center, no matter how quick it receives and responds to a request, it does take some time. This time of latency doesn’t significantly affect most of the cases. However, it can adversely affect some applications like self-driving-car.

Drawback #2: The Law of Economics

After that, Vogels sees the second drawback of cloud computing as “the low of economics” where problems are associated with a volume of data. Further, Prioritizing the data processing from the tremendous amount of data that has been regularly received by the server is not an easy nut to crack. After that, The bandwidth of data processing significantly lacks to gather data in cloud computing. Most of the data collected to cloud servers is not important. Still, some of it is extremely important. In other words, this scenario cloud processes, all the received data unnecessarily consume a lot of processing power.

Drawback #3: The Law of Land

According to Werner Vogels, the third major drawback with cloud computing is associated with local regulation; he calls it “the law of the land”. However, When it comes to financial data, health records and other sensitive information that may be subject to rules that how data can be stored and transmitted. Because cloud couldn’t produce valuable insights as most of the data are barred to be stored, transmitted or processed.

How will Edge Computing Solve these issues?

Indeed, edge computing processes data at the edge of the network where data is generated. It had distributed, open IT infrastructure that features decentralized processing of data to enable mobile computing and IoT. In addition, the edge computing data gets processed itself at a device or local network or server, instead of being transmitted to a centralized server for processing.

Along with it, decentralized and near to origin data processing empower Edge computing in many ways including latency elimination, real-time data processing without time lag, and will have an increase in the level of security and privacy. Above all, it allows smart devices to respond to data instantaneously that is critical for technologies such as self-driving-car and others were the response in milliseconds matters.

However, edge computing is efficient as a huge amount of data processing happens at or near to source in it. Although, It is cost-effective, ensures effective use of the application in remote areas, and reduces Internet bandwidth usage. In other words, Edge computing provides local data processing ability that prevents data transmission to the cloud but it also adds a useful layer of security for sensitive data such as banking information, medical reports, and others.

Let’s look at a comparison table for the difference between Edge Computing and Cloud Computing

Edge Computing Cloud Computing
Basic data visualization Complex analytics
Basic data analytics and short term data historian features Big data mining
Data caching, buffering and streaming Sources of business logics
Data pre-processing, cleansing, filtering and optimization Machine learning rules
Device to Device communications Advance visualization
Fast and Secure Comparatively slow and less secure
Almost null latency time Have some latency time

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