Minkube Design Concept

Minkube is a distributed task management system. This document outlines its core design principles.

Manager

The Manager is responsible for distributing tasks to available Worker nodes.

• Worker Selection: It selects a Worker to receive a task using a round-robin strategy.
• Task Assignment: It sends an HTTP request to the chosen Worker's API to assign the task and awaits a response.
• Failure Considerations: The Manager is designed with the understanding that Workers might fail due to various reasons, such as:
  • Failure to pull or use a specified Docker image.
  • Bugs within the Docker container causing it to fail on startup.
  • Insufficient disk space on the Worker node.

Worker

Workers execute the tasks assigned by the Manager.

• State Management: Each Worker stores its state, including the IDs of tasks assigned to it, in memory (primarily using a map data structure).
• Fault Tolerance: This in-memory storage of task IDs aims to provide fault tolerance. If the Manager node fails, a Worker retains information about the tasks it is expected to perform.
• Performance: Local storage of task IDs allows for faster access, as the Worker does not need to make a network round trip to the Manager to retrieve this information.
• Data Reconciliation: Periodically, each Worker reconciles its local task database with the Manager's task database to ensure consistency.

Planned Enhancements & System Details

Distributed Key-Value Store for Tasks

• TODO: Implement a distributed key-value store for managing task data. This store will be shared between the Manager and Workers, aiming to improve data consistency, fault tolerance, and overall system scalability.

CPU Utilization Measurement

• For measuring CPU utilization, the initial approach considered using Performance Monitoring Counters. However, due to unavailability in the target cloud VM environment, the implementation was switched to reading CPU statistics from the /proc/stat file.

Task Definition and Lifecycle

• (Placeholder for Task Details)
  • This section will detail the structure of a task, its possible states, and how its lifecycle is managed within the system.

Service Discovery

• Current Approach: Workers currently register themselves with the Manager upon startup. This is achieved by the Worker sending an HTTP request to a registration endpoint on the Manager, which then adds the Worker to its list of available nodes.
• Alternative Approaches Considered:
  • Utilizing a dedicated service discovery tool (e.g., Consul).
  • Implementing DNS-based service discovery, similar to how Kubernetes resolves service IP addresses.
• TODO: Implement DNS-based service discovery as the preferred method for a more robust and scalable solution.

1. Security & Authentication API Authentication API Keys: Simple token-based auth for service-to-service communication JWT Tokens: For user sessions with expiration and refresh mechanisms mTLS (Mutual TLS): For secure worker-to-manager communication RBAC (Role-Based Access Control): Different permissions for admins, developers, operators Network Security TLS Everywhere: Encrypt all HTTP traffic (manager ↔ workers, client ↔ manager) Network Segmentation: Workers in private subnets, manager in DMZ Firewall Rules: Restrict port access between components VPN/Bastion: Secure access to management interfaces
2. Observability & Monitoring Metrics & Monitoring Prometheus/Grafana: System metrics (CPU, memory, disk, network) Application Metrics: Task queue size, success/failure rates, response times Worker Health: Track worker availability, resource usage, container counts Alerting: PagerDuty/Slack integration for critical issues Logging Structured Logging: JSON logs with correlation IDs Centralized Logging: ELK Stack (Elasticsearch, Logstash, Kibana) or similar Log Levels: DEBUG/INFO/WARN/ERROR with configurable levels Audit Logging: Track all API calls, task creations, configuration changes Distributed Tracing Jaeger/Zipkin: Track requests across manager → worker → container lifecycle Request Correlation: Unique IDs to follow a task through the entire system
3. High Availability & Resilience Manager High Availability Multiple Manager Instances: Active-passive or active-active clustering Load Balancer: HAProxy/NGINX for manager instances Shared State: External database (PostgreSQL) or distributed cache (Redis Cluster) Leader Election: Consensus algorithm for active manager selection Data Persistence Database: Move from in-memory maps to PostgreSQL/MySQL Redis: For caching and session storage Backup Strategy: Automated database backups with point-in-time recovery Circuit Breakers & Retry Logic Graceful Degradation: Handle worker failures without cascading Exponential Backoff: Smart retry strategies for failed operations Bulkhead Pattern: Isolate failures (one worker failure doesn't affect others)
4. Scalability & Performance Horizontal Scaling Manager Clustering: Multiple manager instances behind load balancer Worker Auto-scaling: Dynamic worker registration/deregistration Database Sharding: Partition task data across multiple databases Message Queues: Redis/RabbitMQ for task distribution at scale Resource Management Resource Quotas: Limit CPU/memory per tenant or project Priority Queues: High/medium/low priority task scheduling Resource Monitoring: Track and alert on resource exhaustion Auto-scaling Workers: Spin up/down workers based on queue depth
5. Configuration & Deployment Configuration Management Environment-based Configs: Dev/staging/prod configurations Secret Management: HashiCorp Vault or Kubernetes Secrets Feature Flags: Toggle features without deployments Hot Reloading: Update configurations without restarts Deployment Strategy Blue-Green Deployments: Zero-downtime deployments Rolling Updates: Gradual rollout with health checks Canary Releases: Test new versions with small traffic percentage Database Migrations: Version-controlled schema changes
6. API & Interface Design API Design Versioning: /v1/, /v2/ API versioning strategy Rate Limiting: Prevent API abuse with token bucket algorithms Pagination: Handle large result sets efficiently OpenAPI/Swagger: API documentation and client generation User Interfaces Web Dashboard: Task monitoring, worker status, system metrics CLI Tool: Command-line interface for operators API Documentation: Interactive API explorer Status Pages: Public status page for system health
7. Data & State Management Database Design Task History: Persistent storage of completed tasks Worker Registry: Dynamic worker discovery and health tracking User Management: Store user accounts, permissions, quotas Audit Trail: Track all system changes and API calls State Consistency ACID Transactions: Ensure data consistency across operations Event Sourcing: Store events rather than current state CQRS: Separate read/write models for performance Data Retention: Automatic cleanup of old task data
8. Compliance & Governance Security Compliance SOC 2: Security controls and auditing GDPR: Data privacy and user rights Encryption: Data at rest and in transit Access Logs: Track who accessed what and when Operational Compliance Change Management: Approval processes for production changes Disaster Recovery: Backup and restore procedures SLA Monitoring: Track and report on service level agreements Incident Response: Playbooks for handling outages
9. Cost & Resource Optimization Resource Efficiency Auto-scaling: Scale resources based on demand Spot Instances: Use cheaper cloud instances for workers Resource Pooling: Share resources across tenants efficiently Usage Analytics: Track and optimize resource consumption Cost Monitoring Cost Allocation: Track costs per tenant/project Resource Quotas: Prevent runaway resource usage Budget Alerts: Notify when costs exceed thresholds Implementation Priority Phase 1 (Foundation):

TLS encryption, basic authentication, structured logging, database persistence Phase 2 (Reliability):

High availability, monitoring/alerting, circuit breakers, backup strategy Phase 3 (Scale):

Horizontal scaling, advanced monitoring, performance optimization Phase 4 (Enterprise):

Advanced security, compliance, multi-tenancy, governance Each of these areas represents a significant engineering effort, but they're what separates a proof-of-concept from a production-ready system that can handle real workloads reliably and securely.