Edge Computing

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…

While 5G deployments are still ongoing, research into 6G is actively underway, with a focus on even higher data rates, lower latencies, and greater network intelligence. The anticipated integration of…

In the rapidly evolving world of modern computing, the concept of the “edge” has become increasingly prominent, with organizations across various industries exploring its potential to drive innovation, enhance operational…

Overcoming latency in 5G platforms is crucial for unlocking its full potential in real-time applications like autonomous vehicles, remote surgery, and AR/VR experiences. While 5G promises significant latency reduction compared to previous generations, several technical challenges remain. This blog discusses some ways to tackle these challenges. 

the need for efficient bare metal provisioning has become increasingly crucial. Bare metal servers, with their dedicated hardware resources and customizable configurations, offer a powerful solution for a wide range of workloads. In this blog post, we’ll explore the key questions to address before embarking on your bare metal provisioning journey.

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.

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

I would like to round-out 2021 with a final post on a reference architecture that I have been driving towards all year. This post brings together several themes we have covered since 2019 – on the suitability of Kubernetes as a foundation for Telco 5G workloads.