Module 1: Introduction to Low-Latency Computing and OpenShift

Module Overview

This module introduces the fundamentals of low-latency computing and demonstrates why OpenShift is an ideal platform for high-performance, time-sensitive workloads. You will learn about real-world use cases, performance characteristics, and the role of Kubernetes Operators in managing complex performance configurations.

Learning Objectives

  • Understand what low-latency means and why it matters for modern applications

  • Learn about real-world use cases requiring microsecond response times

  • Explore OpenShift’s role in high-performance computing environments

  • Identify latency challenges in containerized environments

  • Introduction to Kubernetes Operators for performance management

Workshop Prerequisites

This workshop requires:

  • Access to a Single Node OpenShift (SNO) cluster (covered in Module 2)

  • SSH access to bastion host with pre-configured tools

  • Basic understanding of OpenShift and Kubernetes concepts

What is Low-Latency and Why Does it Matter?

Definition

Latency, or response time, is the time between an event and the system’s response, typically measured in microseconds (µs). Low-latency computing focuses on achieving predictable, minimal response times for time-sensitive applications.

Importance

Many industries and organizations, especially in finance and telecommunications, require extremely high performance computing with low and predictable latency. Even small delays can result in:

  • Lost trading opportunities worth millions of dollars

  • Degraded user experience in real-time applications

  • Violation of Service Level Agreements (SLAs)

  • Competitive disadvantage in time-sensitive markets

Key Use Cases

  • Financial Trading: High-frequency trading platforms requiring microsecond response times

  • Telecommunications: Real-time voice and video processing with strict latency budgets

  • Gaming: Online gaming platforms where milliseconds determine user experience

  • Industrial IoT: Real-time control systems and automation requiring deterministic responses

  • Live Streaming: Media processing and delivery with minimal delay

  • Autonomous Vehicles: Safety-critical systems requiring immediate response to sensor data

OpenShift’s Role in High-Performance Computing

Capability Overview

Exchanges, banks, hedge funds, and financial intermediaries can run high-throughput/low-latency trading on OpenShift with performance characteristics similar to Red Hat Enterprise Linux (RHEL). This enables organizations to containerize their most demanding workloads without sacrificing performance.

Performance Metrics

Real-world benchmarks demonstrate OpenShift’s capability:

  • At 100,000 messages/second: RHEL and OpenShift exhibit nearly identical 99th percentile latency (2 µsec vs. 2.3 µsec)

  • At 1,400,000 messages/second: OpenShift’s 99th percentile latency was 2.8 µsec

  • Production deployments: Tier 1 banks achieving 16x transaction capacity increase over legacy platforms

Enterprise Benefits

OpenShift enables organizations to achieve high performance while providing:

  • Improved Manageability: Kubernetes-native orchestration and automation

  • Lower Total Cost of Ownership (TCO): Reduced operational overhead compared to traditional infrastructure

  • Improved Developer Productivity: Container-based development and deployment workflows

  • Reduced Application Downtime: Built-in high availability and automated recovery

  • More Efficient DevOps: GitOps workflows and automated operations

  • Improved Security Posture: Immutable CoreOS foundation and integrated security controls

Latency Challenges in Containerized Environments

Understanding the sources of latency is crucial for effective optimization. OpenShift helps mitigate several key challenges:

Common Sources of Latency

  • Control-plane Overhead: Kubernetes API server and scheduler delays

  • Kernel Jitter: Context switches, interrupts, and memory management overhead

  • Container Runtime Path: CRI-O, overlay filesystems, and namespace overhead

  • Network CNI Overhead: Software-defined networking latency

  • Resource Contention: CPU, memory, and I/O competition between workloads

OpenShift Solutions

  • Node Tuning Operator: Automated system-level optimizations via TuneD profiles

  • Performance Profile Controller: Comprehensive CPU isolation and memory tuning

  • Real-time Kernel Support: Deterministic scheduling and reduced jitter

  • SR-IOV Network Operator: Hardware-accelerated networking with direct device access

  • NUMA Awareness: Memory locality optimization for multi-socket systems

  • Machine Config Operator: Declarative node-level configuration management

Introduction to Kubernetes Operators

What Are Kubernetes Operators?

A Kubernetes Operator is a method of packaging, deploying, and managing a Kubernetes-native application. They are essentially custom controllers that continuously reconcile the desired state and the current state of an object.

Key Characteristics

  • Custom Resource Definitions (CRDs): Extend Kubernetes API with domain-specific objects

  • Controllers: Monitor and maintain desired state automatically

  • Domain-Specific Knowledge: Encode operational knowledge for specific applications

  • Lifecycle Management: Handle installation, updates, configuration changes, and scaling

Relevance to Performance Tuning

OpenShift performance tuning features are managed through various Operators. Important: Starting with OpenShift 4.11, the performance operator architecture has been significantly updated. This workshop targets OpenShift 4.20, which fully implements the modernized architecture:

Operator Purpose Status (4.20)

Node Tuning Operator (NTO)

Manages TuneD daemon AND Performance Profiles. Since OpenShift 4.11+, this operator has absorbed the functionality of the deprecated Performance Addon Operator

Built-in ✅

Performance Addon Operator (PAO)

Previously managed Performance Profiles for CPU isolation, real-time kernels, and HugePages

DEPRECATED ❌

Machine Config Operator (MCO)

Manages configuration changes and orchestrates updates across the cluster, crucial for performance-related configurations

Built-in ✅

SR-IOV Network Operator

Manages Single-Root I/O Virtualization for high-performance networking with direct hardware access

Requires Installation 📦

OpenShift Virtualization Operator

Provides KubeVirt virtualization capabilities for running VMs with low-latency characteristics

Requires Installation 📦
Critical Architecture Changes in OpenShift 4.11+ (Including 4.20)

Performance Addon Operator Deprecation: As of OpenShift 4.11, the Performance Addon Operator has been deprecated and its functionality has been integrated into the Node Tuning Operator. OpenShift 4.20 (our target version) fully implements this modernized architecture:

  • Performance Profiles are now managed directly by the Node Tuning Operator

  • No separate installation required for performance profile functionality

  • Simplified deployment with fewer operators to manage

  • Better integration between system tuning and performance profiles

  • Mature implementation with enhanced stability and features

Reference: OpenShift 4.20 Low Latency Tuning Documentation
Benefits of Operator-Based Management

  • Declarative Configuration: Define desired state, operator ensures implementation

  • Automated Operations: Operators handle complex configuration and lifecycle tasks

  • Consistency: Standardized deployment and management across environments

  • Version Control: Configuration as code for auditability and reproducibility

Workshop Environment

Pre-Configured SNO Cluster

A major financial institution successfully modernized their trading platform using OpenShift:

  • Time to Market: Reduced functional enhancement delivery from 2-3 months to a few days

  • Operational Efficiency: Significant reduction in OpEx for colocation versus legacy "snowflake" servers

  • Performance Gains: 16x increase in daily transaction capacity

  • Infrastructure Optimization: Achieved high-density deployment with up to 10x less rack space

This demonstrates that with proper tuning, OpenShift can deliver bare-metal performance characteristics while providing the benefits of container orchestration.

Module Summary

In this module, you have:

✅ Learned the fundamentals of low-latency computing and its critical importance
✅ Understood real-world use cases requiring microsecond response times
✅ Explored OpenShift’s capabilities for high-performance workloads
✅ Identified the sources of latency in containerized environments
✅ Discovered how Kubernetes Operators automate performance management
✅ Updated Knowledge of OpenShift 4.20 performance operator architecture changes

Key Takeaways

  • Low-latency computing is essential for financial, telecom, and real-time applications

  • OpenShift can achieve bare-metal performance characteristics with proper tuning

  • OpenShift 4.20 has a mature, simplified performance operator architecture

  • Node Tuning Operator now handles both TuneD profiles and Performance Profiles

  • Performance Addon Operator is deprecated - its functionality is now built-in

  • Kubernetes Operators enable declarative, automated performance management

External References

OpenShift 4.20 Performance Documentation
Performance Operator Architecture (4.20)
Real-World Case Studies
Technical Deep Dives

Next Steps

In Module 2, you will verify your pre-configured Single Node OpenShift (SNO) cluster and confirm that all required operators are installed and ready. Your environment comes pre-configured with all necessary components for the workshop exercises.

Ready to start? Begin with Module 1 to understand the fundamentals, then progress through hands-on exercises that build upon each other.

Learning Path:

  1. Start Here: Module 1 - Low-Latency Fundamentals

  2. Environment Setup: Module 2 - Environment Setup and Verification

  3. Performance Baseline: Module 3 - Baseline Testing

  4. Core Optimization: Module 4 - Performance Tuning

Additional Resources: