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January 23, 2025 Technical Deep Dive

When Every Millisecond Matters: Solving Real-Time Network Traffic Quality Issues

How SRE teams are transforming video and voice quality management with AI

In today’s hybrid work environment, a 200ms delay in video conferencing isn’t just an inconvenience—it’s the difference between seamless collaboration and frustrated teams missing crucial conversations. For SRE teams managing real-time communications infrastructure, these quality issues create a perfect storm of complexity: they’re time-sensitive, impact-heavy, and notoriously difficult to diagnose.

The challenge isn’t just about watching network metrics. Modern video and voice applications generate massive amounts of telemetry data across multiple layers: network paths, codec behaviors, endpoint performance, and infrastructure health. When quality degrades, engineers face the daunting task of correlating data across these layers in real-time, often while users are actively reporting issues

The Hidden Complexity of Real-Time Traffic

Traditional network monitoring approaches fall short when dealing with real-time traffic issues. While your dashboard might show acceptable overall network performance, users still experience stuttering video and choppy audio. Why? Because real-time communications require a different class of network quality:

  • Jitter and latency variations that barely impact web browsing can destroy video quality
  • Packet loss patterns that standard monitoring might miss can create noticeable audio artifacts
  • Micro-bursts of congestion can cause quality degradation without triggering traditional thresholds
  • Quality metrics need to be analyzed end-to-end, across multiple network segments and providers

Adding to this complexity, modern video and voice applications dynamically adjust to network conditions, making it challenging to establish baseline performance metrics. An issue that causes severe quality problems in one session might have minimal impact in another, depending on codec adaptations and endpoint behaviors.

Enter Hawkeye: Your AI-Powered Network Quality Expert

Instead of manually correlating metrics across multiple tools and time ranges, imagine having an AI teammate that understands the nuanced relationships between network behavior and real-time traffic quality. Hawkeye transforms how teams handle these challenges:

  1. Proactive Detection: By analyzing patterns across network layers, Hawkeye identifies potential quality issues before users report problems. It understands the specific network characteristics that impact real-time traffic and can spot subtle degradations traditional monitoring might miss.
  2. Rapid Resolution: Instead of spending hours manually investigating, teams receive comprehensive analysis identifying the root cause and recommended actions. Hawkeye’s understanding of real-time traffic requirements means it can distinguish between general network issues and those specifically impacting video/voice quality.
  3. Contextual Analysis: When quality issues occur, Hawkeye automatically correlates relevant data points:
    • Network path performance metrics
    • Infrastructure health indicators
    • Application-level quality metrics
    • Historical baseline comparisons
    • Configuration changes that might impact performance

The Transformed Workflow

The impact on daily operations is immediate. Traditional troubleshooting workflows require engineers to:

  • Monitor multiple dashboards across network and application layers
  • Manually correlate quality metrics with network performance
  • Analyze historical trends to identify patterns
  • Investigate potential infrastructure or configuration changes
  • Coordinate with multiple teams to implement fixes

With Hawkeye, engineers start with a unified view that automatically brings together all relevant information. Routine issues are quickly resolved using recommended actions, while complex problems come with detailed investigation summaries that include data from across your environment.

Moving Beyond Reactive Monitoring

For organizations heavily dependent on real-time communications, the transformation Hawkeye brings extends beyond technical efficiency. It enables a fundamental shift from reactive quality management to proactive optimization:

  • Quality issues are identified and resolved before they impact users
  • Network capacity and configuration decisions are guided by AI-driven analysis
  • Engineering teams focus on strategic improvements rather than firefighting
  • Users experience consistently high-quality video and voice communications

Ready to Transform Your Real-Time Traffic Management?

Implementing Hawkeye alongside your existing tools is straightforward and begins paying dividends immediately. While this blog focuses on video and voice quality, Hawkeye’s flexible integration capabilities mean you can connect it to your entire observability stack, creating a unified intelligence layer across all your tools.

Take the next step toward transforming your real-time communications quality management. Contact us to see how Hawkeye can become your team’s AI-powered network quality expert.

Written by

Francois Martel
Field CTO

Francois Martel

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