The Business Case For Edge Computing: Key Components, Deployment Strategies and Security Best Practices
Key Highlights
- Edge deployments bring processing and storage closer to devices, reducing latency and supporting real-time applications like AR/VR, autonomous vehicles, and smart cities.
- Key hardware includes network gateways, edge nodes, and ruggedized servers, while software tools like SD-WAN and open orchestration platforms enable efficient management.
- Security is paramount; implementing zero trust, data encryption, firewalls, and intrusion prevention helps protect distributed edge environments from cyber threats.
- Organizations should develop comprehensive strategies considering operational, security, and scalability factors, starting with small proof-of-concept projects.
- Open ecosystems and flexible deployment options, such as colocation or managed services, facilitate cost-effective and adaptable edge infrastructure growth.
Edge deployments are becoming an essential part of today’s digital infrastructure. Organizations with edge networks gain advantages in terms of seamless data management, operations automation and real-time IT service delivery. According to recent IT research, 68% of respondents reported increases in edge-related spending.
In contrast to centralized data centers, the edge provides close physical proximity to distributed devices and end users through the use of specialized networks, modular servers and remote connected devices. This infrastructure enables nanosecond resource delivery to remote events and substantially reduces latencies, a critical requirement for growing IoT, 5G and AI deployments.
A key benefit is the ability of edge devices to automatically perform actions without IT intervention, significantly reducing the IT burden. However, as enterprise leaders and IT administrators consider adoptions, strategic planning and airtight security remain essential prerequisites. We explore the architecture of edge-first deployments as well as adoption strategies and the components necessary for building a scalable operations model.
Understanding key elements of edge architecture
Edge deployments comprise a distributed computing approach that brings processing and storage physically closer to remote devices and end users. For example, reducing latencies is essential for immersive reality technologies (e.g., AR/VR, smart glasses, etc.), remote AI deployments, autonomous vehicles (AV), intelligent cities, Industry 4.0 and smart home automation. Organizations not only gain the advantages of high-speed data access for mission-critical deployments, but they can also eliminate the overhead related to traditional data centers in terms of hardware expenses, HVAC controls, energy costs, security and infrastructure maintenance.
Primary hardware components include network gateways and aggregators, as well as edge nodes, which can include ruggedized servers for use in harsh environments or compact modular data centers for remote locations. Software-defined wide area networks (SD-WAN) are crucial for controlling edge data traffic, while wireless protocols (e.g., 5G, Wi-Fi 6, etc.) enable real-time connectivity to cloud or local edge servers. Internal IT teams and administrators also rely on either open source tools or proprietary platforms to orchestrate and centralize edge management.
“Open orchestration tools make it easier to move workloads between cloud and edge environments. And open source projects support a wider range of hardware out of the box,” says Ilya Tabakh, vice president of Innovation at TensorWave, a startup focused on building high-performance AI computing clusters. “As open ecosystems mature, they are driving more cost-effective and performant solutions,” he adds.
In terms of implementing edge initiatives, C-suite and IT leaders have different management and deployment options: A hosted edge colocation provider can offer the physical infrastructure and virtualization layer, while the client maintains control over applications, operating systems, and data. Alternatively, a managed service provider (MSP) can handle and manage the entire edge stack for a company, including hardware, OS, security, monitoring and updates.
Finally, an enterprise IT team can bring together specialized software and open-source tools (e.g., Jenkins, Cloud Build) within a unified platform to manage, deploy, and update remote-site workloads on their own. According to a recent survey, more than half of respondents (56%) relied on internally operated, on-premises data centers in their edge strategies, making them the most common edge deployment strategy.
“However, for many, a dedicated cloud GPU instance is a practical first step towards edge, offering workload isolation, predictable performance and dedicated resources without the operational complexity of a full edge deployment,” says Tabakh.
Ensuring secure edge deployments
Distributed edge environments increase the cyber attack surface with numerous opportunities for malicious actors, and so organizations must implement airtight security to protect all edge endpoints. A zero trust approach provides the necessary foundation for verifying every request and securing remote hardware. This protocol assumes threats exist both inside and outside the network and requires continuous authentication for every user, device and application. It represents a centralized security solution that can be monitored and extended to all edge sites.
Data encryption is also essential for information transfers between edge devices and the cloud or data center. Moreover, by implementing firewalls and intrusion prevention systems, IT teams can guard against cyber intrusions. This approach is crucial to prevent compromised connections in other private networks (e.g., supply chains, third-party services, etc.), which may be less secure and pose vulnerabilities. In certain respects, on-site processing at the edge reduces information exposure since processing is executed locally, both protecting data and complementing safeguards designed to meet data privacy compliance (e.g., GDPR, HIPAA, etc.).
“Where data actually lives is increasingly non-negotiable, particularly for regulated industries,” according to Roger Cummings, CEO of PEAK:AIO, a software-defined storage platform for AI initiatives. "Encryptions at rest and in transit are fundamental requirements. At the edge, devices are often physically accessible in ways a data center rack is not, so encryption at rest takes on additional importance,” he says.
Taking a strategic approach
For edge deployments to remain competitive and highly functional, C-suite executives and IT leaders must develop a comprehensive edge strategy that addresses the complexities of managing multiple edge sites, ensures efficient connectivity is in place, and accurately assesses security deficiencies. A key planning goal is to gain a thorough understanding of the edge strategy landscape and develop a comprehensive approach that considers the business implications of operational malfunctions. These impacts can include revenue losses and operational downtimes due to equipment failures, such as dysfunctional point-of-sale (POS) systems in retail, or assembly-line sensor malfunctions in manufacturing.
“Many organizations accept supplier connections without formally requiring the other party to meet defined security standards, provide audit evidence, or notify promptly in the event of a breach,” says Cummings. “But to detect it, you have to be watching for it,” he states.
One of the goals in undertaking an edge deployment might be to augment existing IT operations or adopt new capabilities to achieve desired business outcomes. Edge use cases offer different advantages, depending on the economic sector. For example, companies in manufacturing employ the edge to automate assembly lines, where each robot or machine sends data to a recording server for operational oversight and troubleshooting. In the energy sector, wind and solar farms gather and transmit data on weather conditions via sensors, radios and gateways.
It’s a similar scenario for oil and gas companies that use edge computing to monitor thresholds, inline pressure, temperatures and flow rates. In healthcare, the edge enables remote medical procedures via point-to-point connections that instantaneously relay data. And edge deployments in finance ensure the lowest latencies to facilitate trades, execute split-second transactions, and assess volatile market conditions.
“Organizations should start small by selecting a specific use case, running a proof of concept, and benchmarking performance across cloud and edge environments to validate assumptions before scaling,” cautions Tabakh.
Steps to implementation
As enterprise and IT leaders take the first steps in assessing readiness, they need to address diverse process and infrastructure questions. For example, regarding technical competency, it’s important to establish whether the necessary IT skill sets are in place to support edge deployment and management. For example, handling distributed automation and scaling numerous endpoints requires advanced technical skill using Kubernetes for orchestration and related tools (e.g., K3S, MicroK8s, etc.) to manage containerized workloads.
In addition to extensive networking and connectivity expertise, edge deployments require IT and operations technology (IT/OT) integration and knowledge of security protocols. When considering an MSP, colocation or unified platform approach, administrators must assess whether the solution meets the organization’s budget and technical requirements. They also need to determine if the solution is flexible enough to integrate with current legacy systems and future infrastructure, as well as evaluate the terms of service for ongoing support and maintenance.
“Locking into a proprietary ecosystem or application today could mean a painful and expensive migration when requirements change — and they will change,” says Cummings. “Start open where you can,” he adds.
And from the outset, C-suite leaders must identify the primary goals and value proposition for an organization’s edge adoption. They should be able to answer whether a deployment will be used to enhance existing enterprise applications, streamline current IT operations or develop new, unique end-user experiences. Once an edge foundation is established, organizations will be prepared to take advantage of the rise in AI, machine learning (ML) and computer vision deployments, integrating and refining them over time to improve edge processing and data quality.
“Leaders should also prioritize partners aligned with open ecosystems and silicon diversity, treating infrastructure strategy as a living plan that evolves alongside business needs,” states Tabakh.
About the Author
Kerry Doyle
Contributor
Kerry Doyle focuses primarily on issues relevant to both C-suite and enterprise leaders through technology articles, white papers and analyses. He covers a diverse range of topics, from nanotech to the cloud, open source to AI. Passionate about both the written word and communicating the value of technology, his experience stems from senior editorial positions at PCWeek, PCComputing, ZDNet, and CNet.com. He's a graduate of Boston University with a bachelor's degree in comparative literature.
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