1. Architectures
1.1. Service Discovery
1.1.1. client-side
1.1.2. server-side
1.1.3. Decision Guidance Models for Microservices – Service Discovery and Fault Tolerance
1.1.4. Circuit Breakers, Discovery, and API Gateways in Microservices
1.2. Load Balancing
1.2.1. client-side
1.2.2. server-side
1.3. Architectural Patterns for Microservices: A Systematic Mapping Study
1.4. Microservices Patterns (book)
2. Modeling
3. Microservices: yesterday, today, and tomorrow
4. Terminology
4.1. Types of Faults
4.1.1. IEEE Standard Classification for Software Anomalies
4.1.2. Basic Concepts and Taxonomy of Dependable and Secure Computing
5. Técnicas
5.1. Prevention
5.1.1. Fault Injection
5.1.1.1. Chaos Engineering
5.1.1.1.1. A Platform for Automating Chaos Experiments (ChAP)
5.1.1.1.2. Gremlin
5.1.1.1.3. Failure as a Service (FaaS): A Cloud Service for LargeScale, Online Failure Drills
5.1.1.1.4. Lineage-driven fault injection
5.1.1.1.5. Chaos Engineering
5.1.1.2. Lineage-driven fault injection
5.1.1.3. System
5.1.1.3.1. Fate and Destini
5.1.1.4. Application
5.1.2. Fault Density Analysis
5.1.2.1. Reexamining the fault density component size connection
5.1.2.2. The optimal class size for object-oriented software
5.1.3. Fault-tree Analysis
5.1.4. Cloud Provider
5.1.4.1. Reliability evaluation of cloud computing systems using hybrid methods
5.1.4.2. Failure Management for Reliable Cloud Computing: A Taxonomy, Model and Future Directions
5.1.4.3. Building Fault-Tolerant Applications on AWS
5.1.5. Formal Methods
5.1.5.1. TLA+
5.1.5.1.1. how amazon uses formal methods
5.1.6. Unbanlanced Capacities
5.2. Detection
5.2.1. Monitoring
5.2.1.1. An architecture for self-managing microservices
5.2.1.2. Decision Guidance Models for Microservice Monitoring
5.2.2. Chaos Engineering
5.3. Handling
5.3.1. Replication
5.3.1.1. Service Orchestration
5.3.1.1.1. An architecture for self-managing microservices
5.3.1.2. Load Balancing
5.3.1.2.1. An architecture for self-managing microservices
5.3.1.2.2. Decision Guidance Models for Microservices – Service Discovery and Fault Tolerance
5.3.1.3. Auto-scaling
5.3.1.3.1. AGILE: elastic distributed resource scaling for Infrastructure-as-a-Service
5.3.1.3.2. Building Fault-Tolerant Applications on AWS
5.3.1.4. disk
5.3.1.4.1. Building Fault-Tolerant Applications on AWS
5.3.1.5. node
5.3.1.5.1. Building Fault-Tolerant Applications on AWS
5.3.1.6. nodes health check
5.3.1.7. multi-az
5.3.2. Communication
5.3.2.1. Retry with exponential backoff
5.3.2.2. Fallback
5.3.2.3. Synchronous
5.3.2.3.1. Circuit Breakers
5.3.2.3.2. Bulkheads
5.3.2.3.3. Timeouts
5.3.2.3.4. Throttling
5.3.2.3.5. Circuit Breakers, Discovery, and API Gateways in Microservices
5.3.2.3.6. Decision Guidance Models for Microservices – Service Discovery and Fault Tolerance
5.3.2.3.7. Release it! (book)
5.3.2.3.8. Cloud Design Patterns PRESCRIPTIVE ARCHITECTURE GUIDANCE FOR CLOUD APPLICATIONS
5.3.2.4. Asynchronous
5.3.3. Restart
5.3.4. Saga
5.3.5. Canary
5.3.5.1. App–Bisect: Autonomous Healing for Microservice-based Apps
5.4. Diagnosis
5.4.1. Automatic diagnosis
5.4.1.1. Automatic Failure Diagnosis Support in Distributed Large-Scale Software Systems based on Timing Behavior Anomaly Correlation
5.4.1.2. Automated fault localization using potential invariants
5.4.1.3. Using likely invariants for automated software fault localization