Introduction
To build a successful application, you need to understand how data flows through your system. You must also understand the latency—how much time it takes for data to travel between different components of your system. If latency is too high, this can lead to poor performance and even failure of the application.
In-Memory Computing
In-memory computing is a type of data storage that allows for faster processing. Unlike cloud computing, in-memory processing takes place on the device, rather than in the cloud. This means that you can retrieve information quickly and efficiently, which reduces latency and improves performance.
For example: If you’re trying to find all the restaurants within 10 miles from your location who have gluten free options on their menu, this would require a lot of data processing without an in-memory system because it would need to send requests across multiple servers before getting back any results; however with an in-memory system running locally on your phone or computer (or both), this task would take seconds instead of minutes or hours–and could even be completed while offline
Edge Computing
Edge computing is a data center architecture that processes data as close to the source as possible. It improves latency by reducing the distance between the data source and the data center, allowing companies to provide real-time insights across their entire business.
Edge computing can be implemented in several ways:
- A remote processing unit (RPU) is installed at each site and connected via Internet Protocol (IP) backhaul or dedicated fiber to a central cloud infrastructure.
- An edge server connects directly to sensors or other devices in remote locations via local area network (LAN). The edge server performs all necessary analytics onsite before sending any results back to back-end systems for storage, analysis, and reporting purposes.
Edge computing reduces latency by providing data at the edge of an enterprise.
Edge computing is a way of delivering data to the edge of an enterprise. This can be done through cloud services, or by deploying your own data center at the edge of your network.
Edge computing reduces latency by providing data at the edge of an enterprise. For example, if you have an application that needs real-time video processing (like video conferencing or facial recognition), it would be faster for that application to run on servers in close proximity to where users are accessing those services than having them run on servers located far away from them in another city or country. Edge computing also helps reduce costs because it requires less bandwidth between different locations compared to traditional centralized architectures
Conclusion
Edge computing is a promising technology that can help enterprises reduce latency and increase the speed of their applications. The ability to process data closer to where it’s generated means less time spent on network communications, which improves overall performance.
