Edge Computing

Overview

Edge computing refers to the deployment of compute, storage, and networking infrastructure at or near the physical location where data is generated and consumed, rather than routing all processing to centralized cloud data centers. The core rationale is latency reduction, bandwidth efficiency, and data sovereignty: for applications like real-time AI inference, industrial automation, autonomous vehicles, and video analytics, round-trip latency to a central cloud is prohibitive.

The edge computing market is experiencing accelerating growth driven by the intersection of AI and IoT. Edge enterprise infrastructure workloads are forecast to grow at 19.1% CAGR from 2024 to 2028 (IDC, 2024), outpacing central data center growth. This growth is attributed primarily to the requirement to run AI inference models against data streams generated at the edge — factory floor sensors, cameras, medical devices — without the latency or bandwidth costs of cloud transmission.

From an infrastructure design perspective, edge deployments present distinct challenges compared to hyperscale: more constrained physical environments, less controlled power and thermal conditions, limited on-site staffing, and requirements for ruggedized or compact form factors (3U/4U or smaller). The DC-MHS modular architecture and remote management capabilities are increasingly relevant at the edge for the same reasons they matter in the data center — multiple silicon architectures, need for remote manageability, and compressed engineering team capacity.