Posted on June 24, 2025June 24, 2025 | by priteshgeek

1. Introduction & Overview

What is Load Shedding?

Load Shedding in software systems refers to the intentional dropping of lower-priority requests or workloads to protect the overall system from overload or failure. This approach ensures that critical operations remain functional, even when system resources are heavily constrained.

In the context of DevSecOps, load shedding helps ensure system resilience, security under stress, and compliance with SLAs (Service Level Agreements), particularly during high load or attacks such as DDoS.

History or Background

Origin: Originally a power grid concept, “load shedding” was adapted into distributed computing for gracefully degrading service during load spikes.
Adopted by cloud platforms (e.g., Netflix OSS, Google SRE) to prevent system crashes during scaling events or cyberattacks.
Became a key technique in modern reliability engineering and DevSecOps resilience strategies.

Why Is It Relevant in DevSecOps?

Prevents resource starvation attacks
Ensures continuous security checks even under pressure
Maintains compliance SLAs for high-priority users
Avoids data corruption by dropping unsafe requests under pressure

2. Core Concepts & Terminology

Key Terms and Definitions

Term	Definition
Load Shedding	Intentionally rejecting requests to protect system health
Circuit Breaker	A pattern that stops traffic flow to failing components
Rate Limiting	Controls how many requests a client can send
Graceful Degradation	Maintaining core functionality while limiting others
Backpressure	Technique to control data flow to prevent overload

How it Fits into the DevSecOps Lifecycle

Phase	Role of Load Shedding
Plan	Design for failure
Develop	Code fallback logic
Build	Automate stress tests
Test	Include performance + chaos scenarios
Release	Deploy with feature flags
Deploy	Configure load-shedding thresholds
Operate	Monitor real-time health
Secure	Prevent overload-based denial-of-service

3. Architecture & How It Works

Components

Load Monitor: Monitors CPU, memory, latency
Load Shedding Policy: Defines when and what to shed
Priority Queue Manager: Decides which requests to drop
Fallback Services: Optional degraded services

Internal Workflow

flowchart LR
A[Incoming Requests] --> B{Check System Load}
B -->|Healthy| C[Process Request]
B -->|Overloaded| D{Request Priority}
D -->|Low| E[Drop Request]
D -->|High| F[Route to Fallback or Retry]

Integration Points with CI/CD or Cloud Tools

Tool	Integration
Kubernetes	HPA + Istio + Retry Budget with Load Shedding filters
Istio / Envoy	Built-in load shedding via outlier detection
AWS / GCP / Azure	Auto-scaling, throttling policies, App Gateway
GitHub Actions	Can trigger load tests during CI
Prometheus + Alertmanager	Monitoring CPU/mem to trigger actions

4. Installation & Getting Started

Basic Setup or Prerequisites

Kubernetes cluster or microservice architecture
Istio or Envoy proxy setup
Observability: Prometheus, Grafana
CI/CD pipeline for test/deploy automation

Step-by-Step: Load Shedding with Envoy Proxy (Basic)

Install Envoy
Configure a basic filter in your Envoy YAML:

overload_manager:
  refresh_interval: 0.25s
  resource_monitors:
    - name: "envoy.resource_monitors.fixed_heap"
      typed_config:
        "@type": type.googleapis.com/envoy.extensions.resource_monitors.fixed_heap.v3.FixedHeapConfig
        max_heap_size_bytes: 2147483648
  actions:
    - name: "envoy.overload_actions.shed_load"
      triggers:
        - name: "envoy.resource_monitors.fixed_heap"
          threshold:
            value: 0.95

Test with Load Generator like hey or wrk:

hey -n 100000 -c 100 http://your-service-url

Observe behavior in logs and dashboards.

5. Real-World Use Cases

Use Case 1: Security Under Load

During a penetration test, load spikes occur. Load shedding ensures authentication and logging services remain live while rate-limiting low-priority scan traffic.

Use Case 2: Multi-Tenant SaaS

SaaS app for enterprise users gives SLAs to premium customers. Load shedding deprioritizes free-tier users during resource contention.

Use Case 3: Healthcare System

A hospital management system during a pandemic sees traffic spikes. Non-critical features like report download are temporarily shed to maintain EMR updates.

Use Case 4: E-commerce DDoS Mitigation

During a flash sale, bot traffic causes overload. System uses load shedding + CAPTCHA + rate limiting to ensure genuine user access.

6. Benefits & Limitations

Key Advantages

Maintains system stability under stress
Supports zero-downtime availability goals
Improves QoS for premium users
Easy to integrate with SRE, DevSecOps, and Zero Trust

Common Challenges

Risk of unintended service denial to legit users
Requires accurate priority definition
Needs careful testing in staging/stress environments

7. Best Practices & Recommendations

Security & Performance

Always log shed requests for audit
Add fallback services where possible
Integrate with WAF or API gateway rules

Maintenance

Use feature flags to enable/disable policies
Regularly update thresholds based on metrics

Compliance

Avoid shedding audit, encryption, or PII access services
Ensure logs are preserved for compliance audits

Automation Ideas

Auto-enable shedding when latency > 500ms
Send alerts if load shedding exceeds 5% of total traffic

8. Comparison with Alternatives

Feature	Load Shedding	Rate Limiting	Circuit Breaker
Focus	System health	Per-client fairness	Service isolation
Granularity	Request-level	IP/user-level	Service-level
Priority Support	✅	❌	❌
Stateful Decisions	✅	❌	✅

When to Choose Load Shedding

You need smart shedding based on system health
You want to preserve core security services
You’re dealing with burst attacks or Black Friday events

9. Conclusion

Final Thoughts

Load shedding is a critical reliability and security feature in DevSecOps for ensuring that your systems remain available, secure, and compliant even under extreme load.

Incorporating load shedding into your pipeline and runtime can save your application from downtime, protect your users, and preserve trust.

Future Trends

AI-driven shedding policies
Dynamic SLA-aware routing
Integration with service mesh security contexts

Load Shedding in DevSecOps: A Complete Tutorial