This article is based on industry analysis and predictions originally published around early 2020, drawing on Forrester research and reporting from sources including analyticsinsight.net and TechCrunch.
Why Edge Computing Gained Momentum
Edge computing emerged as a direct response to the limitations of traditional centralized cloud architectures. By moving computation closer to where data is generated — on devices, in factories, at retail locations, or along transportation routes — edge computing reduces network latency, lowers bandwidth costs, and enables faster real-time decision-making.
Industry analysts projected significant growth: one report cited in early 2020 forecast the global edge computing market would expand from approximately US$2.8 billion in 2019 to US$9.0 billion by 2024, representing a compound annual growth rate (CAGR) of around 26.5 percent.
Key 2020 Predictions from Forrester
Forrester’s edge computing predictions for 2020 highlighted several concrete trends that infrastructure and DevOps teams needed to watch. Around 57 percent of mobility decision-makers surveyed in the Forrester Analytics Global Business Technographics Mobility Survey (2019) indicated they had edge computing on their roadmap for the following 12 months.
1. Custom Form Factors Will Be Driven by Fit, Form, and Function
Edge computing is not a one-size-fits-all technology. Different use cases — from autonomous vehicles to industrial IoT sensors to smart retail shelves — impose unique requirements on compute power, storage capacity, and networking capabilities. Forrester predicted that these unique demands would push hardware development toward custom form factors, right down to the processor level.
One concrete example from that period: HPE led a Series C investment of up to US$145 million in Pensando Systems, a startup co-founded by former Cisco CEO John Chambers, which was developing a custom programmable processor optimized specifically for edge workloads.
2. The Edge Cloud Service Market Would Grow by at Least 50%
Public cloud hyperscalers, telecommunications companies, content delivery networks (CDNs), platform software vendors, and colocation data center providers were all moving to offer infrastructure-as-a-service (IaaS) and platform-as-a-service (PaaS) capabilities on distributed edge infrastructure.
The defining characteristic of these offerings was the ability to run workloads independently of — or with only intermittent connectivity to — central cloud or data center assets. This was a critical requirement for use cases in manufacturing plants, remote locations, or environments where consistent WAN connectivity could not be guaranteed.
3. Multi-Vendor Packaged Solutions Over Single-Vendor Lock-In
Managing edge computing infrastructure at scale is operationally complex. Unlike a centralized data center, edge deployments span many geographically distributed sites, often with limited on-site IT expertise. Forrester predicted that organizations across retail, manufacturing, and logistics would increasingly prefer integrated multi-vendor solutions — working with systems integrators rather than attempting to build and operate bespoke edge stacks themselves.
For DevOps teams, this signaled a shift toward standardized tooling, managed edge platforms, and partner ecosystems rather than purely DIY infrastructure-as-code approaches.
Primary Benefits Organizations Sought from Edge Computing
According to the Forrester survey, the top drivers for enterprise adoption of edge computing were:
- AI flexibility: The ability to handle current and future artificial intelligence workloads at or near the data source, without round-tripping data to a central cloud.
- Reduced latency: Processing data locally avoids the network round-trip to a cloud region, enabling faster responses — critical for applications such as real-time quality control, autonomous systems, and patient monitoring.
- Bandwidth efficiency: Filtering and processing data at the edge reduces the volume of raw data that needs to be transmitted over expensive or constrained WAN links.
The Broader Vision: 50 Billion Connected Devices by 2030
Microsoft CEO Satya Nadella, as reported by TechCrunch around this time, argued that the future of computing would increasingly happen at the edge — with local processing performed first, and data then forwarded to the cloud for AI model training and deeper analytics. This hybrid approach treats edge and cloud as complementary layers rather than competing paradigms.
To put the scale in context, Nadella referenced research projecting 50 billion connected devices worldwide by 2030. Compared to roughly one billion Windows machines or a few billion smartphones, that figure underscored just how fundamentally different the edge computing era would be — and why centralized cloud-only architectures would struggle to scale to meet it.
Implications for DevOps and Infrastructure Teams
For engineering teams, the edge computing trends visible in 2020 pointed toward several practical considerations:
- Distributed deployment pipelines: CI/CD tooling needs to support deployments to hundreds or thousands of geographically dispersed edge nodes, not just a handful of cloud regions.
- Lightweight container runtimes: Constrained edge hardware requires optimized container and orchestration stacks — tools like K3s (lightweight Kubernetes) emerged to address this.
- Observability at scale: Monitoring, logging, and alerting across distributed edge sites requires rethinking traditional centralized observability approaches.
- Offline-first and intermittent connectivity design: Applications deployed at the edge must be architected to handle loss of connectivity to central systems gracefully.
- Security at the perimeter: Edge nodes outside the data center expand the attack surface and require robust device identity, encrypted communication, and automated patching strategies.
Summary
Edge computing in 2020 moved from a niche concept to a mainstream infrastructure priority. The combination of AI workload growth, IoT device proliferation, latency-sensitive applications, and maturing edge cloud services created a compelling case for investment. For DevOps professionals, edge computing represented both a new operational challenge and an opportunity to extend modern delivery and operations practices to an entirely new class of compute environment.