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Computer Science
DevOps and SRE
Performance Engineering and Optimization
1. Introduction to Performance Engineering
2. Fundamental Concepts and Metrics
3. Performance Engineering in the Software Development Lifecycle
4. Performance Modeling and Forecasting
5. Performance Testing Methodologies
6. System Monitoring and Observability
7. Performance Analysis and Tuning
8. Performance in Modern Architectures
9. Advanced Performance Topics
10. Tools and Technologies
11. Performance Engineering Best Practices
8.
Performance in Modern Architectures
8.1.
Microservices Performance
8.1.1.
Service Discovery Performance
8.1.1.1.
Registry Latency
8.1.1.2.
Health Check Overhead
8.1.1.3.
Service Mesh Impact
8.1.2.
Inter-service Communication
8.1.2.1.
RPC Performance
8.1.2.2.
REST API Optimization
8.1.2.3.
GraphQL Performance
8.1.2.4.
Messaging System Latency
8.1.3.
Distributed Tracing Challenges
8.1.3.1.
Trace Propagation
8.1.3.2.
Sampling Strategies
8.1.3.3.
Trace Storage and Analysis
8.1.4.
Service Mesh Performance
8.1.4.1.
Proxy Overhead
8.1.4.2.
Policy Enforcement Impact
8.1.4.3.
Circuit Breaker Performance
8.1.5.
Data Consistency and Performance
8.1.6.
Microservices Testing Strategies
8.2.
Cloud Computing Performance
8.2.1.
Cloud Service Performance Characteristics
8.2.1.1.
IaaS Performance
8.2.1.2.
PaaS Performance
8.2.1.3.
SaaS Performance
8.2.2.
Auto-scaling Performance
8.2.2.1.
Reactive Scaling
8.2.2.2.
Predictive Scaling
8.2.2.3.
Scaling Latency
8.2.2.4.
Cost Optimization
8.2.3.
Multi-region Performance
8.2.3.1.
Latency Across Regions
8.2.3.2.
Data Consistency and Replication
8.2.3.3.
Global Load Balancing
8.2.4.
Cloud Storage Performance
8.2.5.
Cloud Database Performance
8.2.6.
Hybrid Cloud Performance
8.3.
Serverless and Function-as-a-Service
8.3.1.
Cold Start vs Warm Start Performance
8.3.1.1.
Impact on Latency
8.3.1.2.
Mitigation Strategies
8.3.1.3.
Runtime Optimization
8.3.2.
Concurrency Management
8.3.2.1.
Platform-specific Limits
8.3.2.2.
Throttling Behavior
8.3.2.3.
Concurrent Execution Patterns
8.3.3.
Function-level Optimization
8.3.3.1.
Code Size Reduction
8.3.3.2.
Dependency Management
8.3.3.3.
Memory Allocation
8.3.4.
Event-driven Performance
8.3.5.
Serverless Monitoring and Observability
8.4.
Containerization and Orchestration
8.4.1.
Docker Performance
8.4.1.1.
Image Size Optimization
8.4.1.2.
Container Startup Time
8.4.1.3.
Layer Caching
8.4.2.
Kubernetes Performance
8.4.2.1.
Pod Scheduling
8.4.2.1.1.
Scheduling Latency
8.4.2.1.2.
Node Affinity and Taints
8.4.2.1.3.
Resource Requests and Limits
8.4.2.2.
Cluster Performance
8.4.2.2.1.
etcd Performance
8.4.2.2.2.
API Server Performance
8.4.2.2.3.
Network Plugin Performance
8.4.2.3.
Storage Performance
8.4.2.3.1.
Persistent Volume Performance
8.4.2.3.2.
Storage Classes
8.4.2.3.3.
Volume Mounting
8.4.3.
Container Networking Performance
8.4.4.
Service Mesh in Kubernetes
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9. Advanced Performance Topics