What Is Edge Computing Architecture? | Synopsys Cloud
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Defining edge computing architecture can be challenging. Because edge computing is an increasingly popular style of architecture that supports multiple use cases, articulating a single, comprehensive definition can be difficult. In fact, you’ll likely receive a different definition depending on who you ask.

Elements of edge computing are wide-ranging and can include mobile devices, IoT, smart environments, the cloud, and on-premise infrastructures. Edge computing can also apply to driverless cars, robotics, manufacturing, and more. 

This article will outline a “big-picture” snapshot of edge computing, discuss its fundamental properties, and then examine a few potential uses. 


All About Edge Computing

Edge computing brings data and computational power closer to the sources of the produced data to provide savings on bandwidth and response times. It has no single topology but refers to the architecture as a whole. 

Because edge computing relies on the cloud to store and process data close to the applications and users that consume it, a high-speed internet connection is necessary for reliability. With data situated near its consumed location, faster applications with greater availability are possible. 


Edge Computing Advantages

By keeping data near the edge of the network, edge computing provides advantages that include:

  • Enhanced bandwidth and availability
  • Improved response times 
  • Faster data analysis in addition to the higher availability we’ve discussed. 
  • Reduced latency 
  • Safer data processing through distributed processing and storage.  

With IoT devices and edge servers, companies can scale efficiently and cost-effectively without investing in private data centers that require maintenance. 

To accomplish this kind of scaling, edge devices collect and store data before sending it to specific servers, utilizing data visualization and analytics, caching and buffering, data filtering, and real-time data processing. Consequently, expansion can be as easy as partnering with a local edge data center and testing new markets. New infrastructure is not required—only edge devices for setup. If the market is not optimal, uninstallation is simple.


Edge Computing Applications

With edge computing architectures, a world of applications emerges.

  • Using analytic algorithms, edge devices can monitor equipment functionality and help manufacturers adjust and respond in real-time, reducing defects and improving efficiency. 
  • An edge center supports operations in remote locations (such as oil and gas) where real-time analytics can be leveraged to monitor systems and machines.
  • In healthcare, edge devices that monitor patients help reduce the amount of data sent through the network to avoid server overloads. 
  • Edge computing reduces lag for online gaming, allowing for larger multiplayer servers with fast performance.
  • Cars with edge devices can collect data from sensors and respond in real-time to events on the road —an essential and necessary feature for autonomous vehicles.

Interconnectivity With Edge Computing Architecture

Edge computing moves processing power closer to the client with edge data centers and embedded data storage directly on devices. As a result, applications are shielded from data center outages.

Edge computing can be viewed as a layered approach, with the top layer comprised of data centers, including central and interconnected regional data centers. The edge layer is one level deeper and is composed of smart cars, oil platforms, retail shops, medical clinics, and the list goes on. Edge devices comprise this layer and include IoT devices, smartphones, sensors, and other internet-connected devices. The edge layer runs on a local network powered by wireless, 5G, fiber, or older legacy hardware.

Inside the edge layers, there are individual devices, smartphones, laptops, and other IoT devices that are all capable of communicating with the edge data center.

A key feature in this architecture is that databases are embedded within edge devices and allow continuous processing if a large-scale cloud database server fails. Individual devices synchronize captured data across the environment—between cloud and edge databases and between embedded databases on devices—ensuring it is always available.

As major cloud service providers increasingly offer edge computing services where data centers can be established in specific cities, on-premises, or in 5G networks, edge computing initiatives have discovered increased flexibility and simpler implementation.


Synopsys, EDA, and the Cloud

Synopsys is the industry’s largest provider of electronic design automation (EDA) technology used in the design and verification of semiconductor devices, or chips. With Synopsys Cloud, we’re taking EDA to new heights, combining the availability of advanced compute and storage infrastructure with unlimited access to EDA software licenses on-demand so you can focus on what you do best – designing chips, faster. Delivering cloud-native EDA tools and pre-optimized hardware platforms, an extremely flexible business model, and a modern customer experience, Synopsys has reimagined the future of chip design on the cloud, without disrupting proven workflows.

 

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About The Author

Gurbir Singh is group director, Cloud Engineering, at Synopsys. He has a demonstrated history of leadership in the software industry. In his current role, he leads the development of the Synopsys Cloud product, which enables customers to do chip design on the cloud using EDA-as-a-Service (SaaS) as well as flexible pay-per-use models. Gurbir has run organizations to develop cloud SaaS products, machine learning applications, AI/ML platforms, enterprise web applications, and high-end customer applications. He is experienced in building world- class technology teams. Gurbir has a master’s degree in computer science, along with patents and contributions to publications. 

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