edge computing

This blogpost continues our ongoing series on vamsitalkstech.com about next-generation wireless technology (aka 6G). In this post, we examine the architectural foundations of 6G networks, following up on concepts introduced…

As telecommunications networks undergo a transformation towards 5G and the forthcoming 6G architectures, the adoption of cloud-native network functions (CNFs) becomes paramount. These CNFs, designed to operate within cloud environments,…

Traditional telecom core networks were designed for voice and data services in a pre-AI world. Today, these networks face significant challenges as AI adoption accelerates across industries. The exponential growth…

As AI becomes more pervasive in the enterprise, edge computing will play an increasingly important role. AI models are often data-intensive and require real-time processing to be effective. Edge computing provides the ideal platform for running AI models at the edge of the network, where the data is generated.

Edge computing is a distributed computing paradigm that brings computation and data storage closer to the sources of data, rather than relying on a centralized cloud data center. This allows for faster response times, reduced latency, and improved bandwidth efficiency

Edge computing is reshaping the way we process and leverage data. By bringing computation closer to the data source, it delivers real-time responsiveness, reduces latency, enhances privacy and security, and offers scalability—all while optimizing bandwidth and reducing costs.

Edge computing is all about implementing compute, storage & networking to enable processing closer to data-generating connected devices. There are a few definitions that emerged with the adopters of edge in different architectures.

Enterprises and telecom operators are focusing on achieving a great amount of flexibility, observability, and dynamic orchestration by deploying and testing Kubernetes at the edge.

Small-scale enterprises and organizations have started evaluating and implementing edge architectures due to the need to support innovative use cases to increase efficiency and/or enhance their customer experience. But there is still a long way to go for enterprise edge deployments as there are more challenges in large-scale distributed computing

This post will consider the top four intertwined system architecture considerations in deploying edge applications successfully on a hardware platform.