Context and Problem Definition

Intermittent Network Symptoms

Inconsistencies typically manifest under moderate to heavy load conditions:

  • Random packet drops between internal VMs or Kubernetes pods
  • High ping latencies (>100ms) to local or inter-region endpoints
  • Inconsistent performance on managed load balancers
  • TCP connection resets or broken HTTP streams under pressure

Why It Matters

These behaviors degrade user experience, break SLA expectations, and cause retries, cascading latency, or service failures. They also complicate root cause attribution in multi-cloud observability pipelines.

Root Causes and Architectural Risks

1. Shared vSwitch Contention

Scaleway instances use shared virtual switches. Under network I/O spikes, virtual NICs may experience throughput degradation due to noisy neighbor effects.

2. MTU Mismatch and Jumbo Frame Fragmentation

Some instance types or VPC configurations default to an MTU of 1500, but overlay networks (e.g., Kubernetes with Calico or Flannel) may assume higher MTU values, leading to fragmentation.

# Check MTUip link show eth0

3. Inconsistent Routing Between AZs

Routing anomalies can occur in multi-AZ setups where inter-AZ traffic takes unexpected routes, adding latency or reducing reliability.

4. Overly Aggressive Security Group or Firewall Rules

Fine-grained but misconfigured network ACLs may intermittently drop packets depending on connection state, especially with short-lived microservice traffic.

Diagnostics and Debugging Steps

Step 1: Trace Latency Using MTR

Run MTR or traceroute to detect mid-path issues:

mtr -rw -c 100 internal-node.local

Step 2: Inspect Logs at the Kernel Level

Use dmesg and netstat to find network stack anomalies:

dmesg | grep eth0
netstat -s | grep retrans

Step 3: Check Load Balancer Logs and Health Probes

Analyze Scaleway load balancer logs to detect timeout thresholds being exceeded or health probes failing.

Step 4: MTU and Fragmentation Testing

Use ping with packet sizes to identify fragmentation thresholds:

ping -M do -s 1472

Step 5: Monitor Metrics with External APMs

Use Datadog, Prometheus, or Grafana with node_exporter and blackbox_exporter to correlate spikes in latency or packet loss to system events.

Mitigation and Long-Term Solutions

1. Align MTU Settings Across Layers

Explicitly configure MTU across OS, container runtime, and CNI layers. Common value: 1400 to account for encapsulation overhead.

2. Enable QoS and Rate Limiting for Traffic Isolation

Implement traffic shaping on critical services using Linux TC or Scaleway's bandwidth control options (where available).

3. Spread Workloads Across AZs and Monitor Paths

Use multi-AZ deployments strategically and avoid cross-AZ chatter where low latency is required.

4. Audit and Simplify Security Rules

Minimize overlapping firewall rules and use connection tracking features to avoid stateless packet drops.

5. Consider Bare Metal for Critical Latency Paths

Scaleway offers bare-metal instances that eliminate virtual NIC bottlenecks entirely.

Best Practices

  • Deploy internal health checks between pods or VMs to catch early degradation
  • Use dedicated VLANs for latency-sensitive workloads
  • Enable flow logging for all VPC endpoints
  • Tag latency-critical services for preferential monitoring and alerting
  • Automate MTU validation during provisioning pipelines

Conclusion

Network latency and packet loss on Scaleway virtual instances stem from a mix of architectural constraints, misaligned configurations, and workload contention. While the symptoms are subtle, they can erode service reliability over time. Teams must implement layered diagnostics—from MTU and routing to APM correlations—and adopt configuration best practices to achieve reliable, scalable deployments on Scaleway. Where latency is business-critical, consider isolating paths with QoS or switching to bare-metal nodes entirely.

FAQs

1. Why do I see packet loss only under high CPU usage?

Shared vNICs may compete for host resources under CPU saturation, leading to degraded packet forwarding performance.

2. Does Scaleway support Jumbo Frames across all services?

No. Jumbo frame support depends on instance type, image kernel, and network stack. Validate MTU compatibility per layer.

3. How do I isolate noisy neighbor impact?

Use iPerf3 across internal nodes to benchmark network throughput and correlate dips with other tenants on shared infrastructure.

4. Can I replicate this issue in staging?

Yes. Simulate high network throughput, enable packet capture, and use synthetic tests across AZs to surface routing or MTU issues.

5. Are Scaleway Load Balancers stateful?

No. They operate at L4 or L7 in a stateless fashion. Ensure your app handles rebalancing and retries without relying on sticky sessions.