Advantages of Edge Computing | IT Careers

Understanding Edge Computing Advantages

Courtesy: Moor Insights & Strategy

Courtesy: Moor Insights & Strategy

Cloud computing’s popularity has resulted, in part, from Big Data’s growth. Many companies found it was less cost effective to maintain their own computational power, data centers, and storage than to engage cloud service providers. By paying only for actual use through price-tiered plans, adding new nodes and bandwidth became quick, easy, and economical.

Now, edge computing, or simply the Edge (sometimes called fog computing), extends the cloud as another means by which companies can keep sensitive or commonly accessed data outside centralized archives. As a hybrid style, the Edge approach emphasizes data access and processing on local devices, which keeps data near the edge of a company’s network, hence the name.

A particularly important Edge companion is the Internet of Things (IoT), where many devices exchange data with each other and ultimately with servers. Performing computations at the data generation source is just one Edge advantage. This way, networks only need to carry information from permanent storage to a central location, greatly reducing network traffic. In turn, bandwidth requirements fall, and so do costs associated with supporting it.

Edge advantages include:

Reduced latency and improved performance, because data resides near its place of use, minimizing data access delays. As a result, Edge helps maximize the value of real-time analytics, because data often loses value over time. To exemplify this, consider the relationship between a store and a shopping consumer.

By maximizing an approach depending primarily upon local devices, Edge also reduces demand on centralized servers, freeing main data center capacity. IoT devices can locally preprocess and analyze data, and using this approach efficiently separates archive data from operational data.

By processing more data locally, central computing environment failures often carry less serious consequences. As a result, the Edge usually continues operations with minimal wide area impact, resulting in a more robust environment.

Edge simplifies data management. By limiting centralized systems’ burdens only to key data archiving and data warehousing become simpler. Operationally, processes like indexing and ETL (data’s export, transform, and load) become easier to maintain, quicker, and more efficient.

Because smaller servers often accompany Edge computing practices, the macro-approach quickly becomes less environmentally demanding. Reduced traffic to central servers means these systems are also smaller.  Consequently, power, cooling, and space requirements often fall considerably, comparing Edge’s distributed model with centralized processing.

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