The Ultimate Edge Computing Checklist: 10 Essentials for HPC Success

The Ultimate Edge Computing Checklist: 10 Essentials for HPC Success
7 min read

Edge is no longer a buzzword; it's a critical component of digital transformation strategies across every industry. By processing data where it's generated rather than routing it to centralized cloud data centers, Edge delivers unprecedented speed, latency, security, reliability and insights. 

However, with so many edge computing platforms and technologies available, it can be difficult to determine which features are truly essential for success. 

Let’s explore the 10 most important edge computing features that every organization should prioritize to achieve high performance, scalability and ROI.

1. Low Latency

The most important factor when it comes to edge computing is, arguably, latency. The whole point of deploying applications and workloads to the network edge is to minimize latency and enable real-time interactions and decisions. Edge platforms must be able to process data with minimal delay—typically under 20 milliseconds of latency—to support applications like industrial automation, autonomous vehicles, telemedicine, augmented and virtual reality, and more. 

Leading edge platforms like AWS Outposts, Microsoft Azure Edge Zones, and VMware Edge compute nodes are engineered from the ground up for ultra-low latency with local processing and 5G connectivity built-in.

2. Distributed Architecture

To support global deployments with thousands or millions of edge nodes,  high-performance computing platforms must have a distributed architecture that allows for decentralized management, updates and security. A centralized approach simply won't scale. Leaders like IBM's Edge Application Manager and HPE's Edgeline Converged Edge Systems support distributed deployment models with local compute and storage at each node, as well as centralized orchestration of the entire edge ecosystem. This distributed design is essential for scalability, reliability and future growth.

3. Hardware Agnostic

Edge environments will involve a wide variety of heterogeneous devices, from industrial gateways and embedded systems to microdata centers and cloud pods. A hardware-agnostic architecture is key to accommodating this diversity while maintaining standard APIs, security, updates and orchestration across all endpoints. VMware's Project Monterey, Azure Edge Zones and AWS Outposts all embrace a hardware-agnostic approach with software-based virtualization that works across many processor architectures and form factors.

4. Container Support

Containers have emerged as the dominant method for developing, deploying and managing applications in high-performance computing environments. For consistency between core data centers and edge locations, platforms must support containerized workloads and integrate with container orchestration tools like Kubernetes, Docker and OpenShift. Leaders offer native container support along with tools that simplify container deployment, security and management at a large scale across diverse edge infrastructure.

5. Automated Management

Fully automated management is crucial when dealing with edge infrastructure that can scale to thousands, millions or even billions of distributed devices globally. Manually configuring, updating, monitoring and repairing each individual node is completely infeasible in these types of massive IoT and edge computing environments.

  • This is where self-service management platforms and AIOps tools really shine. Centralized control planes with user-friendly web portals and comprehensive APIs allow administrators to easily provision new edge applications, workloads and services across entire fleets of devices. 
  • Self-service interfaces streamline operations by empowering delegated control and empowering individual business units or line-of-work teams to deploy and manage their own edge services.
  • Once deployed, AIOps capabilities take over to ensure everything runs smoothly with minimal human intervention required. Sophisticated monitoring uses automation to continuously collect telemetry from all nodes, proactively detect anomalies, and predict and prevent issues before they impact the business. 
  • When problems do occur, AIOps leverages machine learning to quickly diagnose root causes and initiate automatic repairs or rollbacks.
  • Advanced tools also automate routine maintenance tasks. Over-the-air updates push critical security patches, software and firmware upgrades to all nodes simultaneously without disrupting operations. 

Backup and restore are automatically handled for any node that fails. And compliance is simplified through automated configuration audits that ensure policy and regulatory requirements are met across dynamically scaling fleets of edge devices.

6. Security & Access Controls

With endpoints distributed across uncontrolled environments, edge security is paramount.  High-performance computing platforms offer role-based access controls, encryption of data in transit and at rest, identity and access management (IAM), intrusion detection, vulnerability management and more. For extra perimeter security and to micro-segment east-west traffic between workloads, leaders also incorporate SASE and zero-trust network access (ZTNA) solutions. Comprehensive security is a must for regulated industries.

7. Edge Application Enablement

Purpose-built platforms provide developer tools, SDKs, edge-native frameworks and edge-aware services to simplify writing, deploying and managing applications optimized for low-latency edge environments. Leaders offer fully managed edge services for common functions like data ingestion, streaming analytics, ML inference, computer vision and more. They also support edge-focused languages like C, C++, Java, Python, Go and Rust.

8. Data Processing

Given the volume of data generated at the edge, platforms require high-performance processing capabilities for tasks like data filtering, aggregation, transformation and analytics. Leaders offer in-built GPU/FPGA acceleration, distributed stream processing, SQL/NoSQL databases, edge-to-cloud integration and machine learning inference capabilities optimized for low-power embedded systems. Data processing tools are crucial for extracting real-time insights from edge data.

9. Mobility Support

For applications involving mobile assets, vehicles and field workers, platforms must support intermittent connectivity, mobility across subnets and dynamic changes in network topology. Leaders offer capabilities like mobile edge computing, follow-the-sun workload migration, adaptive VPNs, distributed ledger syncing and mobile device/endpoint management features optimized for unpredictable edge environments involving mobility.

10. Billing & Monetization

Given the complexity of billing thousands or millions of globally distributed edge nodes, platforms require subscription-based pricing models along with pay-as-you-go metering and charging by resource usage, data volume and other flexible metrics. Leaders offer marketplace business models alongside billing consoles that provide full visibility into edge infrastructure costs and the ability to pass on charges to line-of-business customers. Comprehensive billing and monetization tools are essential for large-scale commercial edge deployments.

Final Words

Edge computing is revolutionizing industries by enabling real-time automation, insights and interactions. However, to truly achieve the performance, scalability and ROI promised by Edge, organizations must prioritize platforms that offer the 10 essential features discussed in this post. Purpose-built for edge, these leaders deliver low latency, distributed management, hardware flexibility, security, automation, application enablement and billing capabilities required to power the next generation of latency-sensitive applications and use cases. 

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