System Architecture for Inflight Entertainment and Connectivity Platform
1. Introduction
The In-Flight Entertainment & Connectivity (IFEC) Platform is a cloud-native (K8s), microservices-based system that delivers a personalized, seamless, and secure in-flight digital experience. It integrates real-time content streaming, AI-driven recommendations, and automated DevOps pipelines for high availability and scalability.
This platform integrates real-time content streaming, AI-driven recommendations, automated DevOps pipelines, and robust security governance, aligning with the needs of airline operators, passengers, and regulatory requirements. The platform is architected using Domain-Driven Design (DDD) principles, ensuring a modular, scalable, and secure foundation. By aligning with core domain concepts, it enhances the passenger experience, streamlines operational efficiency, and unlocks new revenue opportunities while maintaining flexibility and adaptability to evolving business needs.
2. Vision & Business Requirements Phase
The Vision & Business Requirements Phase is the foundation of the IFEC System Architecture, defining the strategic objectives, stakeholder roles, and key system constraints. This phase aligns business goals with technical capabilities, ensuring that the system meetsscalability, performance, security, and compliance standards.
By clearly defining business and technical requirements, this phase ensures a structured roadmapfor designing, deploying, and evolving the IFEC system.
2.1 Business Goals & High-Level Vision
The table below defines the key business objectives for the IFEC system, ensuring alignment with technological capabilities and user expectations.
Business Goals Table (ID: BG-01)
| ID | Business Goal | Description |
|---|---|---|
| BG-01 | Real-time IFEC System | Provide instant access to entertainment & connectivity services with minimal latency, including real-time flight tracking maps on In-Seat screens & personal devices. |
| BG-02 | Global Scalability | Ensure the system can scale to support multiple airlines, regions, and millions of devices, while integrating geo-based service optimizations (e.g., connectivity, ads, in-flight commerce). |
| BG-03 | AI-Driven Personalization | Adapt recommendations based on passenger behavior and preferences across in-seat screens and personal devices, including region-based recommendations (e.g., movies, ads, languages, cultural preferences). |
| BG-04 | Seamless Multi-Device Experience | Allow passengers to switch between in-seat screens and personal devices without losing progress, while syncing with real-time flight location maps. |
| BG-05 | Monetization & Billing Optimization | Support flexible pricing models, including pay-per-use for in-seat devices, subscriptions, and bundles, while dynamically adjusting pricing based on location-based services (e.g., premium WiFi pricing over oceans). |
| BG-06 | Cloud Independence | Maintain a multi-cloud strategy for in-seat and personal device content distribution, ensuring real-time geo-distributed caching for faster access. |
| BG-07 | Regulatory Compliance | Ensure adherence to GDPR, PCI-DSS, and aviation data regulations, including compliance with regional data privacy laws based on flight location. |
| BG-08 | Secure & Reliable Connectivity | Ensure WiFi, Bluetooth, and Satellite/Air-to-Ground communications provide seamless entertainment access to in-seat screens & personal devices, while dynamically adjusting bandwidth allocation based on aircraft position & congestion zones. |
2.2 Stakeholder Identification
This table categorizes the primary stakeholders involved in the IFEC ecosystem, detailing their roles and interactions with the system.
Stakeholder Identification Table (ID: ST-01)
| ID | Stakeholder | Role & Responsibilities | Primary Interactions with IFEC System |
|---|---|---|---|
| ST-01 | Passengers | End-users consuming in-flight entertainment and services. | Requests media, uses AI recommendations, makes purchases, connects via in-seat screens & mobile devices. |
| ST-02 | Airlines & IT Teams | Operators managing IFEC system. | Monitors performance, manages user data, updates content, ensures regulatory compliance. |
| ST-03 | Content Providers | Third-party vendors supplying media. | Streams content via HLS/DASH to user devices, manages licensing, integrates with APIs. |
| ST-04 | Payment Gateways | Secure payment processing providers. | Handles transactions, ensures PCI-DSS compliance, manages refunds. |
| ST-05 | Regulatory Authorities | Compliance and security enforcement. | Audits logs, reviews content security policies, enforces GDPR, aviation data regulations. |
| ST-06 | Aircraft Connectivity Providers | Providers of in-flight connectivity solutions. | Manages WiFi, Bluetooth, Satellite/Air-to-Ground transitions, optimizes network routing. |
| ST-07 | Network Security Teams | Responsible for firewall policies and intrusion detection. | Implements encryption, secure data access policies, monitors traffic anomalies. |
| ST-08 | Hardware Manufacturers (OEMs) | Manufacturers of In-Seat entertainment screens & hardware. | Works with airlines & IT teams to integrate In-Seat hardware APIs, supports firmware updates & UI development. |
| ST-09 | Aviation Data Providers (ADS-B, GPS, Satellite Tracking) | Supplies real-time aircraft location data. | Provides live aircraft tracking, integrates flight location data with IFEC microservices, ensures accurate passenger maps. |
| ST-10 | Air Traffic Control (ATC) & Airline Operations Teams | Ensures accurate route tracking, safety, and geo-restrictions. | Provides real-time updates on flight paths, ensures no restricted content is accessed based on regional regulations. |
| ST-11 | Advertising & Monetization Partners | Serve geo-targeted advertisements based on flight routes & regions. | Uses flight location data for regional ad placements, offers dynamic pricing models for premium services. |
2.3 Architecture Objectives
The following table outlines the technical architecture goals that align with business objectives and define the system's design constraints.
Architecture Objectives Table (ID: AO-01)
| ID | Arch. Objective | Description |
|---|---|---|
| AO-01 | Low Latency | Optimize streaming and connectivity for near-instant access, including real-time flight location updates on passenger screens. |
| AO-02 | High Availability | Achieve 99.999% uptime using multi-region redundancy and geo-aware failover for content and connectivity services. |
| AO-03 | Multi-Tenancy Support | Allow multiple airlines to operate on the same platform with isolated environments, while enforcing region-specific restrictions dynamically. |
| AO-04 | Zero-Trust Security Model | Implement geo-aware role-based access control (RBAC) and encryption, ensuring compliance with regional aviation data laws. |
| AO-05 | Cloud Independence | Enable multi-cloud deployment with Kubernetes, including geo-distributed content caching for faster access. |
| AO-06 | Edge Computing Support | Use local in-seat storage & AI-driven caching to minimize bandwidth usage and improve performance for offline scenarios, while synchronizing geo-restricted content dynamically. |
| AO-07 | API-First Architecture | Provide a standardized API layer for third-party integrations, ensuring API-based regional content filtering & location-aware playback rules. |
| AO-08 | Observability & Monitoring | Enable real-time logging, tracing, and alerting for proactive issue resolution, including geo-location analytics to track aircraft connectivity performance. |
| AO-09 | Resilient Networking | Ensure secure WiFi, Satellite/Air-to-Ground, and Bluetooth connectivity using firewalls, encryption, and network ACLs, while dynamically adjusting network configurations based on aircraft location. |
| AO-10 | Firmware Lifecycle Management | Implement secure OTA updates, hardware diagnostics APIs, and version control for in-seat device stability & security, with region-based firmware compliance checks. |
| AO-11 | Multi-Device Synchronization | Allow passengers to seamlessly switch between in-seat screens and personal devices without losing progress, while persisting location-based content preferences. |
| AO-12 | Real-Time Flight Location Awareness | Provide real-time aircraft location tracking via ADS-B, GPS, and satellite feeds, integrating data into passenger UI and operational monitoring dashboards. |
| AO-13 | Geo-Partitioned Content Access | Ensure content availability varies dynamically based on aircraft region, enforcing regulatory compliance for in-flight streaming. |
| AO-14 | Regional Ad & Monetization Optimization | Enable location-based ad targeting and pricing models for premium services based on passenger route & region. |
2.4 Non-Functional Requirements
This table defines performance, security, scalability, and compliance standards for the IFEC system.
Non-Functional Requirements Table (ID: NFR-01)
| ID | Category | Requirement |
|---|---|---|
| NFR-01 | Performance | Cloud Cluster: <50ms API response time, high-speed content retrieval. In-Flight Cluster: <2s streaming startup latency, real-time processing of in-seat requests in <500ms. Geo-Aware Content Optimization: Cache localized media based on flight path to minimize cloud dependency. |
| NFR-02 | Security | Cloud & In-Flight Clusters: End-to-end encryption (TLS 1.3), Zero-trust access control. Geo-Based Compliance: Restrict content based on flight location (e.g., disable VoIP in restricted airspaces). |
| NFR-03 | Scalability | Cloud Cluster: Auto-scaling using Kubernetes HPA, Horizontal pod autoscaling, Load balancing with NGINX. In-Flight Cluster: Dynamic resource allocation based on passenger load, edge computing for localized content caching. Geo-Scaling: Optimize API traffic routes based on aircraft region & connectivity conditions. |
| NFR-04 | Availability | Cloud Cluster: 99.999% uptime SLA, Multi-region failover support, Disaster recovery and data backup strategies. In-Flight Cluster: Offline playback support, network resilience for content streaming without internet dependency. Flight Location Failover: Ensure uninterrupted access to real-time aircraft tracking even during connectivity loss. |
| NFR-05 | Maintainability | Cloud & In-Flight Clusters: CI/CD pipeline automation (ArgoCD, Tekton), Infrastructure as Code (Terraform). In-Seat: Firmware versioning & OTA updates, API versioning and backward compatibility. Geo-Configurable APIs: Dynamically update API access rules based on flight path (e.g., restricted API endpoints over international waters). |
| NFR-06 | Compliance | Cloud & In-Flight Clusters: GDPR, PCI-DSS, ISO 27001 adherence, Secure logging and auditing mechanisms, Real-time anomaly detection. Geo-Regulated Access: Enforce regional content filtering, ensure lawful data transmission based on airspace jurisdiction. |
| NFR-07 | Networking Reliability | Cloud Cluster: Redundant networking, Multi-region traffic routing, Failover DNS. In-Flight Cluster: Enforce WiFi stability with QoS policies, Secure Satellite/Air-to-Ground transitions, Optimize Bluetooth & wired connectivity for in-seat devices. Geo-Aware Network Routing: Dynamically switch between connectivity providers based on real-time aircraft position. |
| NFR-08 | Continuous Deployment & DevOps | Cloud Cluster: CI/CD Pipelines, Automated Deployments, Rollbacks, Infrastructure as Code (IaC), Security in DevOps (DevSecOps). In-Flight Cluster: Over-the-air (OTA) software updates for in-seat systems, real-time rollback mechanisms. Geo-Driven OTA Updates: Prioritize firmware updates based on aircraft ground location to optimize bandwidth. |
| NFR-09 | Real-Time Flight Tracking & UI Integration | Cloud & In-Flight Clusters: Real-time integration with ADS-B, GPS, satellite feeds for continuous aircraft location tracking. In-Seat: Deliver live aircraft maps via passenger UI, adjusting data frequency based on available bandwidth. |
| NFR-10 | Geo-Aware Monetization & Targeted Advertising | Cloud Cluster: Deliver regional ad placements based on flight path. In-Flight Cluster: Dynamically adjust premium pricing for WiFi, content, and subscriptions based on regional regulatory constraints. |
2.5 Context Diagram
The Context Diagram below provides a high-level view of the In-Flight Entertainment & Connectivity (IFEC) System, illustrating its interactions with external entities such as passengers, airline backend systems, regulatory authorities, and third-party content providers. It defines the system boundary, outlining how data flows between the IFEC system and its external dependencies to support seamless in-flight services, including media streaming, passenger personalization, payment processing, and regulatory compliance.
Figure 1: Context Diagram - System Boundary & External Entities
Summary of Key Data Flows Table
| Entity | IFEC System Interactions | Data Flow |
|---|---|---|
| Passengers | Requests media, personalization, purchases | API Requests, WebSockets |
| Airline Backend Systems | Fetches CRM data, flight details | REST APIs, gRPC |
| Content Providers | Streams movies, music, TV | HLS/DASH Streaming |
| Payment Gateways | Processes payments for services | Secure API Transactions |
| Regulatory Authorities | Audits in-flight transactions, compliance | Secure Logs, Encrypted Reports |
2.6 Deliverables
2.6.1 Business Capability Model
The Business Capability Model (BCM) defines what the IFEC system must support at a high level. It maps business goals (BG-01 to BG-08) to technical capabilities (AO-01 to AO-09), ensuring a structured foundation for system design.
Business Capability Mapping Table (ID: BCM-01)
| Business Goal (BG-ID) | Capability | Mapped Architecture Objectives (AO-ID) |
|---|---|---|
| BG-01 | Real-time IFEC System | AO-01 (Low Latency), AO-06 (Edge Computing), AO-11 (Multi-Device Synchronization) |
| BG-02 | Global Scalability | AO-02 (High Availability), AO-05 (Cloud Independence), AO-06 (Edge Computing Support) |
| BG-03 | AI-Powered Personalization | AO-07 (API-First), AO-08 (Observability & Monitoring), AO-11 (Multi-Device Synchronization) |
| BG-04 | Seamless Multi-Device Experience | AO-06 (Edge Computing), AO-09 (Resilient Networking), AO-11 (Multi-Device Synchronization) |
| BG-05 | Billing & Monetization | AO-03 (Multi-Tenancy), AO-04 (Security), AO-06 (Edge Computing Support) |
| BG-06 | Cloud Independence | AO-05 (Cloud Independence), AO-09 (Networking), AO-06 (Edge Computing Support) |
| BG-07 | Regulatory Compliance | AO-04 (Security), AO-08 (Observability & Monitoring), AO-10 (Firmware Lifecycle Management) |
| BG-08 | Secure & Reliable Connectivity | AO-09 (Networking), AO-04 (Security), AO-06 (Edge Computing) |
| BG-09 | In-Seat Firmware & OTA Updates | AO-10 (Firmware Lifecycle Management), AO-04 (Security), AO-08 (Observability & Monitoring) |
| BG-10 | Real-Time Flight Tracking | AO-12 (Flight Location Awareness), AO-08 (Observability & Monitoring), AO-09 (Networking) |
| BG-11 | Geo-Based Content & Compliance | AO-13 (Geo-Partitioned Content Access), AO-07 (API-First), AO-04 (Security) |
| BG-12 | Regional Network Optimization | AO-14 (Regional Ad & Monetization Optimization), AO-09 (Networking), AO-06 (Edge Computing) |
2.6.2 Enterprise Roadmap
The Enterprise Roadmap below provides a timeline-based planfor IFEC system development. The roadmap contains the microservices (refer IFEC Tactical Microservices Table) feature deliverable using high level Top-down estimatation technique, with project time span of Q1'25 - Q4'26.
Figure 2: Roadmap for IFEC System Development
Feature Prioritization
This MoSCoW-prioritized roadmap ensures the strategic implementation of IFEC system features based on their business impact, technical dependencies, and regulatory requirements.
Prioritization Table (ID: PR-01)
| ID | Feature | MoSCoW Priority | Est. Time (Months) | Rationale (Why this priority?) |
|---|---|---|---|---|
| P-01 | Streaming & Connectivity | Must-Have (M) | 6 months | Core functionality for IFEC system; required for in-flight operations. |
| P-02 | User Authentication (OAuth2, SSO, In-Seat Login) | Must-Have (M) | 3 months | Security-critical for user access control, including in-seat login & passenger profiles. |
| P-03 | Billing & Payment System | Must-Have (M) | 5 months | Essential for monetization of premium services, including in-seat transactions. |
| P-04 | Regulatory Compliance (GDPR, PCI-DSS, Firmware Security) | Must-Have (M) | 3 months | Mandatory for legal operations & in-seat firmware validation. |
| P-05 | Observability & Monitoring Dashboards | Must-Have (M) | 4 months | Essential for troubleshooting, including real-time in-seat device diagnostics. |
| P-06 | Basic AI-Powered Content Recommendation | Should-Have (S) | 4 months | Enhances passenger experience, extends to in-seat recommendations. |
| P-07 | Multi-Device Syncing (In-Seat to Mobile) | Should-Have (S) | 4 months | Improves user experience by allowing seamless transitions between in-seat screens & mobile devices. |
| P-08 | High-Speed Multi-Region Networking (Satellite, Air-to-Ground, In-Seat) | Should-Have (S) | 6 months | Critical for large-scale expansion, required for in-seat real-time content syncing. |
| P-09 | Multi-Airline SaaS Support | Should-Have (S) | 7 months | Required for scaling IFEC as a SaaS platform across different airlines. |
| P-10 | Edge AI Optimization (R&D Phase 1) | Could-Have (C) | 6 months | R&D-driven for future AI improvements in In-Seat content suggestions. |
| P-11 | Expanded AI Features (Deep Learning for Content) | Could-Have (C) | 6 months | Advanced AI use case, useful but not critical for MVP. |
| P-12 | AI-Driven Anomaly Detection (Security & Fraud Prevention) | Must-Have (M) | 6 months | Essential for detecting fraudulent transactions and security anomalies in real-time. |
| P-13 | Real-time Content Adaptation (ML-Based Personalization) | Should-Have (S) | 6 months | Dynamically adjusts content quality based on user preferences and device performance. |
| P-14 | Full Cloud Independence (Multi-Cloud Deployment) | Could-Have (C) | 6 months | Avoids cloud vendor lock-in but not essential at launch. |
| P-15 | In-Seat Firmware Lifecycle & OTA Updates | Must-Have (M) | 4 months | Ensures secure firmware updates, diagnostics, and in-seat software stability. |
| P-16 | In-Seat Local Content Caching & Offline Playback | Must-Have (M) | 4 months | Critical for ensuring smooth playback when connectivity is lost. |
| P-17 | In-Seat API Integration with IFEC Microservices | Must-Have (M) | 5 months | Required for seamless interaction between in-seat devices and backend microservices. |
| P-18 | Real-Time Flight Tracking Service | Must-Have (M) | 5 months | Essential for live aircraft tracking, passenger experience, and operational analytics. |
| P-19 | Geo-Restricted Content & Compliance Engine | Must-Have (M) | 4 months | Ensures legal content distribution and compliance based on airspace jurisdiction. |
| P-20 | Location-Based Ad & Monetization Engine | Should-Have (S) | 4 months | Enables regional pricing adjustments and geo-targeted advertising. |
| P-21 | Dynamic Network Optimization Based on Flight Location | Should-Have (S) | 5 months | Optimizes connectivity by switching between Satellite, WiFi, and Air-to-Ground based on aircraft region. |
| P-22 | Passenger In-Seat UI | Must-Have (M) | 4 months | Optimized API handling for In-Seat UI, reducing backend load. |
| P-23 | Passenger Mobile UI | Must-Have (M) | 4 months | Optimized API handling for mobile passengers, ensuring seamless cross-device sync. |
| P-24 | Airline Admin Dashboard UI | Must-Have (M) | 4 months | Enables airline operators to manage content, billing, and passenger data efficiently. |
| P-25 | In-Flight Chatbot & Passenger Assistance AI | Nice-To-Have (S) | 5 months | Enhances passenger support, reducing human intervention for FAQs and troubleshooting. |
| P-26 | Integration with Ground Crew & ATC Alerts | Nice-To-Have (S) | 5 months | Ensures airline operators receive live alerts on connectivity issues, aircraft health, and passenger requirements. |
Key Milestones:
- 2025: Minimum Viable Product (MVP) with basic in-flight entertainment, security & connectivity.
- 2026-Q1: AI-powered personalization for content recommendations and Multi-airline support and expanded cloud independence.
- 2026-Q3: AI Fraud detection R&D.
- 2027-Q1: Big fixes, enhancement during system in operation.
2.7 High-Level Architecture Vision
The High-Level Architecture Vision document provides an overview of system components shown as:
Figure 3: High Level System Architecture Vision
3. Architecture Planning & Design Phase
The Architecture Planning & Design Phase serves as the foundation for building a scalable, secure, and resilient IFEC (In-Flight Entertainment & Connectivity) system by translating strategic business goals and domain decomposition into a well-defined technical architecture. This ensures the system can scale efficiently to accommodate increasing passenger demand, multi-airline tenancy, and high data throughput while maintaining resilience through fault tolerance, failure recovery, and self-healing mechanisms. Security and compliance are integral, adhering to regulatory standards such as GDPR and PCI-DSS, with strict access controls in place. Additionally, observability and maintainability are embedded through real-time monitoring, logging, and tracing to ensure proactive issue detection. The architecture is designed for cloud independence, supporting multi-cloud and hybrid deployments to prevent vendor lock-in. This phase focuses on selecting the right architectural approach, defining bounded contexts, identifying integration points, and designing deployment, networking, and security strategies to establish a robust foundation for the IFEC system.
3.1 Domain Driven Design (DDD)
This section decomposes the IFEC Business Goals and Architecture Objectives defined in into domains, establishing clear boundaries (Strategic DDD) and defining key entities, aggregates, and events (Tactical DDD). The Event-Driven Microservices Architecture ensures seamless communication between services while maintaining autonomy.
3.1.1 IFEC Strategic Domain Decomposition
The Strategic Domain Decomposition Table establishes a clear alignment between business goals, architecture objectives, bounded contexts, and team ownership, ensuring a structured and scalable IFEC system architecture. By incorporating Non-Functional Requirements (NFR-01), this decomposition ensures that each bounded context is designed with key performance, security, scalability, and compliance constraints in mind.
Each business goal (BG-ID) defines a strategic business domain, which is further mapped to relevant architecture objectives (AO-ID). From these domains, bounded contexts (BC-ID) are derived, ensuring logical separation of concerns while maintaining modularity and domain-driven scalability. Finally, team ownership is assigned to ensure clear accountability and efficient domain-specific development.
This structured approach bridges the gap between business requirements and technical implementation, ensuring a highly scalable, secure, and resilient IFEC system.
Strategic Domain Decomposition Table
| Business Goal (BG-ID) | Strategic Business Domain | Mapped Architecture Objectives (AO-ID) | Owning Team (Team-ID) | Relevant NFR(s) | |
|---|---|---|---|---|---|
| BG-01 | Streaming & Connectivity | AO-01, AO-06 | T-01 | NFR-01, NFR-03, NFR-07 | |
| BG-02 | Multi-Airline SaaS | AO-02, AO-05 | T-02 | NFR-04, NFR-05, NFR-08 | |
| BG-03 | AI & Personalization | AO-07, AO-08, AO-11 | T-03 | NFR-01, NFR-06, NFR-05 | |
| BG-04 | Multi-Device Experience | AO-06, AO-09, AO-11 | T-04 | NFR-03, NFR-07, NFR-01 | |
| BG-05 | Billing & Payments | AO-03, AO-04 | T-05 | NFR-02, NFR-06, NFR-04 | |
| BG-06 | Cloud & Deployment | AO-05, AO-09, AO-06 | T-06 | NFR-04, NFR-03, NFR-08 | |
| BG-07 | Security & Compliance | AO-04, AO-08, AO-10 | T-07 | NFR-02, NFR-06, NFR-05 | |
| BG-08 | Networking & Security | AO-09, AO-04, AO-06 | T-08 | NFR-07, NFR-02, NFR-04 | |
| BG-09 | In-Seat Firmware & OTA | AO-10, AO-04, AO-08 | T-09 | NFR-05, NFR-06, NFR-08 | |
| BG-10 | In-Seat Content Caching | AO-06, AO-01 | T-10 | NFR-01, NFR-04, NFR-03 | |
| BG-11 | Real-Time Flight Tracking | AO-12, AO-08, AO-09 | T-11 | NFR-09, NFR-07 | |
| BG-12 | Geo-Based Content & Compliance | AO-13, AO-07, AO-04 | T-12 | NFR-10, NFR-06 | |
| BG-13 | Regional Network Optimization | AO-14, AO-09, AO-06 | T-13 | NFR-07, NFR-04 |
3.1.2 Strategic Context Mapping
Bounded Contexts segment business logic into independent yet interconnected domains, preventing cross-dependencies and enabling scalability.
IFEC Strategic Context Mapping Table
| Bounded Context (BC-ID) | Context Type | Description | Domain Name | Mapped Features (PR-01) | MoSCoW Priority |
|---|---|---|---|---|---|
| BC-01 | Core | Handles real-time streaming & media delivery for in-flight entertainment. | Streaming & Connectivity | P-01: Streaming & Connectivity | Must-Have (M) |
| BC-02 | Core | Manages user authentication, access control, and security. | User Identity & Security | P-02: User Authentication (OAuth2, SSO, In-Seat Login) | Must-Have (M) |
| BC-03 | Core | Manages billing, transactions, and payments for IFEC services. | Billing & Payments | P-03: Billing & Payment System | Must-Have (M) |
| BC-04 | Compliance | Ensures regulatory compliance with security & legal policies. | Security & Compliance | P-04: Regulatory Compliance (GDPR, PCI-DSS, Firmware Security) | Must-Have (M) |
| BC-05 | Observability | Monitors system health, logs, and tracks performance issues. | Observability & Monitoring | P-05: Observability & Monitoring Dashboards | Must-Have (M) |
| BC-06 | Experience | AI-powered content personalization for passengers. | AI & Personalization | P-06: Basic AI-Powered Content Rec. | Should-Have (S) |
| BC-07 | Experience | Synchronization of content playback across multiple devices. | Multi-Device Syncing | P-07: Multi-Device Syncing (In-Seat to Mobile) | Should-Have (S) |
| BC-08 | Infrastructure | Ensures robust, high-speed, multi-region networking across flight routes. | Networking & Security | P-08: High-Speed Multi-Region Networking (Satellite, Air-to-Ground, In-Seat) | Should-Have (S) |
| BC-09 | SaaS | Supports multi-airline tenancy, allowing airlines to customize IFEC services. | Multi-Airline SaaS | P-09: Multi-Airline SaaS Support | Should-Have (S) |
| BC-10 | R&D | Research-driven optimization for AI-powered content streaming. | AI & Personalization | P-10: Edge AI Optimization (R&D Phase 1) | Could-Have (C) |
| BC-11 | R&D | Expanding AI capabilities for deep learning-based recommendations. | AI & Personalization | P-11: Expanded AI Features (Deep Learning for Content) | Could-Have (C) |
| BC-12 | R&D | AI-powered security & fraud detection for real-time threat response. | Security & Compliance | P-12: R&D: AI-Driven Anomaly Detection (Security & Fraud Prevention) | Could-Have (C) |
| BC-13 | R&D | AI-driven content adaptation for personalized in-flight experiences. | AI & Personalization | P-13: R&D: Real-time Content Adaptation (ML-Based Personalization) | Could-Have (C) |
| BC-14 | Infrastructure | Enables multi-cloud deployment, ensuring cloud independence. | Cloud & Deployment | P-14: Full Cloud Independence (Multi-Cloud Deployment) | Could-Have (C) |
| BC-15 | Firmware & Device Management | Manages in-seat firmware lifecycle, OTA updates, and diagnostics. | In-Seat Firmware & OTA Updates | P-15: In-Seat Firmware Lifecycle & OTA Updates | Must-Have (M) |
| BC-16 | Local Content Storage & Playback | Ensures offline playback and caching for in-seat entertainment devices. | In-Seat Content Caching & Offline Mode | P-16: In-Seat Local Content Caching & Offline Playback | Must-Have (M) |
| BC-17 | In-Seat API & Microservices Integration | Provides API communication between in-seat devices and IFEC microservices. | In-Seat API Integration | P-17: In-Seat API Integration with IFEC Microservices | Must-Have (M) |
| BC-18 | Flight Tracking & Location-Based Services | Provides real-time aircraft position & location-based services. | Flight Tracking & Location Awareness | P-18: Real-Time Flight Tracking Service | Must-Have (M) |
| BC-19 | Geo-Aware Content Management & Access Control | Ensures region-based content filtering & geo-regulated compliance. | Geo-Based Content & Compliance | P-19: Geo-Restricted Content & Compliance Engine | Must-Have (M) |
| BC-20 | Dynamic Network Optimization | Optimizes network routing & bandwidth based on real-time aircraft region. | Network Optimization Based on Flight Location | P-20: Dynamic Network Optimization Based on Flight Location | Should-Have (S) |
| BC-21 | Backend-for-Frontend (BFF) Services | Provides optimized API responses for In-Seat, mobile, and airline admin UIs. | User Interface & API Aggregation | P-21: BFF for In-Seat, Mobile & Admin UI | Must-Have (M) |
3.1.3 Tactical Bounded Contexts
The Tactical Bounded Contexts represent the microservices that implement the strategic goals. Each bounded context is further divided into microservices, and each microservice is designed to be a standalone service that can be deployed independently, scaled horizontally, and managed by a separate team.
IFEC Tactical Microservices Table
| Microservice (MS-ID) | Bounded Context (BC-ID) | Microservice Name | Functionality | MoSCoW Priority | Mapped Feature (PR-01) | API Endpoints |
|---|---|---|---|---|---|---|
| MS-01 | BC-01 | Content Management | Handles content ingestion, encoding, metadata | M | P-01 | POST /content/upload, GET /content/{id}, DELETE /content/{id} |
| MS-02 | BC-01 | AI Content Recommendation | AI-driven personalized content suggestions | S | P-06 | GET /recommendations/{user_id}, POST /recommendations/train |
| MS-03 | BC-02 | Billing Management | Handles pricing, invoicing, and subscriptions | M | P-03 | POST /billing/invoice, GET /billing/{user_id} |
| MS-04 | BC-02 | Payment Gateway Service | Integrates with Stripe, PayPal, airline payments | M | P-03 | POST /payment/charge, GET /payment/status/{transaction_id} |
| MS-05 | BC-03 | Identity & Access Service | Manages OAuth2, SSO, RBAC | M | P-02 | POST /auth/login, POST /auth/logout, GET /auth/userinfo |
| MS-06 | BC-03 | Security & Compliance Logs | Stores user authentication logs, security audits | M | P-05 | GET /security/logs, POST /security/audit |
| MS-07 | BC-04 | Streaming Service | Manages adaptive bitrate streaming, video delivery | M | P-01 | GET /stream/start/{content_id}, GET /stream/status/{session_id} |
| MS-08 | BC-04 | Multi-Device Syncing | Handles multi-device state sync | S | P-07 | POST /sync/device, GET /sync/status/{user_id} |
| MS-09 | BC-04 | Network Gateway | Manages satellite, air-to-ground connectivity | S | P-08 | GET /network/status, POST /network/configure |
| MS-10 | BC-05 | Multi-Airline SaaS Management | Supports multi-tenancy | S | P-09 | GET /airlines/{id}/config, POST /airlines/{id}/update |
| MS-11 | BC-06 | Monitoring & Logging Service | Provides real-time alerts, performance analytics | M | P-05 | GET /monitoring/metrics, POST /monitoring/alerts |
| MS-12 | BC-07 | Security Enforcement Service | Enforces access policies, compliance | M | P-04 | POST /security/enforce, GET /security/compliance-check |
| MS-13 | BC-07 | AI Fraud Detection | Uses ML to detect fraud, anomalies | C | P-12 | GET /fraud/alerts, POST /fraud/detection/train |
| MS-14 | BC-08 | Real-time AI Content Adaptation | AI-driven content personalization | C | P-13 | GET /ai/content/adapt/{user_id}, POST /ai/train |
| MS-15 | BC-09 | Firmware Update Service | Manages OTA updates, rollback mechanisms | M | P-15 | POST /firmware/update, GET /firmware/version, POST /firmware/rollback |
| MS-16 | BC-10 | Offline Content Caching | Handles local media caching & offline playback | M | P-16 | GET /content/cached/{id}, POST /content/preload, GET /content/drm-status |
| MS-17 | BC-11 | In-Seat API Gateway | Routes in-seat API requests to backend microservices | M | P-17 | POST /inseat/auth, GET /inseat/sync, GET /inseat/settings |
| MS-18 | BC-18 | Flight Tracking Service | Provides real-time aircraft position for maps | M | P-18 | GET /flight/location/{flight_id}, GET /flight/status/{flight_id} |
| MS-19 | BC-19 | Geo-Compliance Service | Enforces airspace-based content restrictions | M | P-19 | GET /content/geo-policy/{region}, POST /content/geo-restrict |
| MS-20 | BC-20 | Flight-Based Network Optimizer | Dynamically switches network based on aircraft location | S | P-21 | GET /network/flight-optimize/{flight_id}, POST /network/adjust |
| MS-21 | BC-12 | Passenger In-Seat UI | Optimized backend service for In-Seat UI | M | P-22 | GET /In-Seat/home, GET /In-Seat/recommendations |
| MS-22 | BC-12 | Passenger Mobile App UI | Optimized backend service for mobile UI | M | P-23 | GET /mobile/home, GET /mobile/recommendations |
| MS-23 | BC-13 | Airline Admin Dashboard UI | Backend service for airline staff | S | P-24 | GET /admin/dashboard, GET /admin/users |
3.1.4 Tactical Domain Models, Aggregates, & Event Flow
The Tactical DDD approach ensures that each microservice is modeled around its core business logic, aggregates, and event flows using Event Storming techniques. By focusing on domain events, we enable loosely coupled interactions and a scalable event-driven architecture.
Each column in the table below represents key Domain-Driven Design (DDD) components that align with Event Storming outcomes:
- Microservice: Represents the bounded context or functional domain to which the entities and aggregates belong.
- Domain (Strategic Bounded Contexts): Maps each microservice to its high-level bounded context defined in Strategic DD.
- Entity: A uniquely identifiable component of the domain model that encapsulates core business logic and state. Entities persist across transactions and maintain identity.
- Aggregates: A cluster of related entities that work as a unit to enforce business rules and maintain consistency. Aggregates ensure proper transaction boundaries and consistency within a domain.
- Events Published: Domain events represent important state changes that other services can consume asynchronously in an event-driven architecture. These enable decoupled interactions between microservices.
IFEC Tactical Domain Model Table
| Microservice Name | Domain | Entity | Aggregates | Events Published | Events Consumed |
|---|---|---|---|---|---|
| Streaming Service | Streaming & Connectivity | MediaContent | Movies, TV Shows, Music, Live Feeds | ContentStreamStarted, ContentStreamEnded | PlaybackSyncUpdated, AIRecommendationUpdated |
| Authentication Service | User Identity & Security | UserAccount | Users, Sessions, Tokens | UserLoggedIn, UserLoggedOut | FraudAlertRaised, SecurityBreachDetected |
| Billing Service | Billing & Payments | Transaction | Payments, Invoices, Refunds | PaymentProcessed, InvoiceGenerated | SubscriptionRenewed, PaymentFailureReported |
| Security & Compliance Logs | Security & Compliance | AuditLog | Security Events, Compliance Logs | SecurityBreachDetected, ComplianceCheckCompleted | UserLoggedIn, UserLoggedOut |
| Observability & Monitoring | Observability & Monitoring | LogEvent | Application Logs, Error Logs, Performance Metrics | SystemFailureDetected, PerformanceAlertRaised | NetworkStatusUpdated, ConnectionLost |
| AI Content Recommendation Inference (Edge) | AI & Personalization | AIModel | Real-time Model Updates | AIModelTrained, AIInferenceCompleted | AIRecommendationUpdated |
| AI Content Recommendation Training (Cloud) | AI & Personalization | AIModelTraining | Deep Learning Models, Dataset Versions | AIRecommendationRefined, NewModelDeployed | UserInteractionUpdated |
| Multi-Device Sync Service | Streaming & Connectivity | DeviceSync | Active Devices, Playback States | PlaybackSyncUpdated, DeviceConnected | UserLoggedIn, ContentStreamStarted |
| High-Speed Networking Service | Networking & Security | NetworkSession | Connectivity Sessions, Bandwidth Usage | NetworkStatusUpdated, ConnectionLost | StreamingServiceRequested |
| Multi-Airline SaaS Service | Multi-Airline SaaS | AirlineConfig | Airline-Specific Content & Pricing | AirlineConfigurationUpdated, NewAirlineOnboarded | NewUserRegistered |
| AI Anomaly Detection (Security) | Security & Compliance | FraudCase | Detected Threats, User Behavior Patterns | FraudAlertRaised, AnomalyDetected | PaymentProcessed, UserLoggedIn |
| Passenger Preferences & Profile Service | User Personalization | PassengerProfile | User Preferences, Accessibility Settings | UserPreferencesUpdated, UserProfileUpdated | UserLoggedIn, AIRecommendationUpdated |
| Multi-Cloud Deployment Service | Cloud & Deployment | CloudInstance | Deployments, Cloud Failovers | CloudFailoverTriggered, AutoScalingAdjusted | MonitoringAlertRaised |
| In-Seat Firmware Update Service | Firmware & Device Management | FirmwareVersion | OTA Updates, Version Rollback | FirmwareUpdatePublished, FirmwareRollbackInitiated | SecurityBreachDetected, ComplianceCheckCompleted |
| Content Ingestion & Encoding Service | Content Processing | EncodedContent | Transcoded Media Files, DRM Metadata | ContentEncoded, ContentMetadataUpdated | ContentUploaded, AIRecommendationUpdated |
| In-Flight Content Sync Service | Local Content Storage & Playback | CachedMedia | Preloaded Content, DRM Encryption | ContentCached, ContentPlaybackOffline | ContentUploaded, NetworkStatusUpdated |
| API Gateway (In-Seat) | In-Seat API & Microservices Integration | DeviceSession | Active Passenger Interactions | InSeatSessionStarted, InSeatSessionEnded | UserLoggedIn, DeviceConnected |
| Flight Tracking Service | Flight Tracking & Location-Based Services | FlightData | GPS Coordinates, Speed, Altitude, Weather Conditions | FlightPositionUpdated, FlightStatusChanged | AirspaceRegulationUpdated, ConnectivityRegionUpdated |
| Geo-Compliance Service | Geo-Aware Content Management & Access Control | ContentRestriction | Region-Based Access Policies, Compliance Rules | ContentAccessRestricted, AirspaceRegulationUpdated | FlightPositionUpdated, PassengerRequestProcessed |
| Network Optimization Service | Dynamic Network Optimization Based on Flight Location | NetworkOptimizer | Satellite, Air-to-Ground, WiFi Switching Logic | ConnectivityRegionUpdated, BandwidthAdjustmentApplied | FlightPositionUpdated, NetworkStatusUpdated |
| Device Diagnostics & Health Monitoring Service | Device Management & Observability | DeviceHealth | In-Seat Device Logs, Hardware Status | DeviceFailureDetected, DeviceStatusUpdated | FirmwareUpdatePublished, NetworkStatusUpdated |
| Passenger In-Seat UI | User Interface Backend | UIRequest | In-Seat UI API Aggregation | In-SeatContentRequested, In-SeatSyncUpdated | UserLoggedIn, ContentStreamStarted |
| Passenger Mobile App UI | User Interface Backend | UIRequest | Mobile UI API Aggregation | MobileContentRequested, MobileSyncUpdated | UserLoggedIn, ContentStreamStarted |
| Airline Admin Dashboard UI | User Interface Backend | AdminDashboard | Multi-Airline SaaS API Aggregation | AdminDataUpdated, AirlineUserUpdated | AdminLoggedIn, MultiAirlineConfigUpdated |
3.1.5 Microservices with Team Allocation
This table provides a clean, structured team allocation, mapping each team (T-ID) to its microservices (MS-ID). By removing duplicate columns, we ensure better clarity in ownership and parallel development planning.
IFEC Microservices Team Allocation Table
| Team ID | Team Name | Microservice | Responsibility |
|---|---|---|---|
| T-01 | Streaming Team | Streaming Service | Manages real-time video/audio streaming, content playback. |
| T-02 | Identity & Security Team | Authentication Service | Handles user authentication, OAuth2, SSO, and session security. |
| T-03 | Billing Team | Billing Service | Manages transactions, invoices, and payment processing. |
| T-04 | Security Compliance Team | Security & Compliance Logs | Ensures regulatory compliance, security auditing, and fraud detection. |
| T-05 | Observability Team | Observability & Monitoring | Captures logs, performance metrics, and system health status. |
| T-06 | AI Personalization Team | AI Content Recommendation (Edge Inference) | Provides AI-powered personalized content suggestions for in-seat and mobile users. |
| T-07 | Streaming Device Team | Multi-Device Sync Service | Synchronizes streaming across in-seat screens, mobile, and Bluetooth devices. |
| T-08 | Networking Team | High-Speed Networking Service | Manages satellite/Air-to-Ground network transitions and stability. |
| T-09 | Multi-Airline SaaS Team | Multi-Airline SaaS Service | Handles airline-specific configurations and multi-tenancy features. |
| T-10 | AI Training Team | AI Content Recommendation (Training in Cloud) | Enhances AI-based content recommendations using deep learning models in the cloud. |
| T-11 | AI Security Team | AI Anomaly Detection (Security) | Detects fraudulent activities, security threats, and anomaly detection. |
| T-12 | User Personalization Team | Passenger Preferences & Profile Service | Manages user personalization settings, accessibility preferences, and watch history. |
| T-13 | Cloud Deployment Team | Multi-Cloud Deployment Service | Manages cloud-independent deployments and automated failover. |
| T-14 | In-Seat Firmware Team | In-Seat Firmware Update Service | Manages OTA updates, version rollbacks, and diagnostics. |
| T-15 | Content Processing Team | Content Ingestion & Encoding Service | Handles media ingestion, encoding, DRM protection, and content metadata. |
| T-16 | In-Seat Caching Team | In-Flight Content Sync Service | Handles in-seat local storage, DRM-protected offline playback. |
| T-17 | In-Seat API Team | API Gateway (In-Seat) | Provides communication between in-seat devices and backend services. |
| T-18 | Flight Tracking Team | Flight Tracking Service | Provides real-time aircraft positioning & flight status updates. |
| T-19 | Geo-Compliance Team | Geo-Compliance Service | Enforces airspace-based content access restrictions. |
| T-20 | Network Optimization Team | Network Optimization Service | Dynamically adjusts connectivity (Satellite/WiFi/Air-to-Ground) based on aircraft region. |
| T-21 | Device Health Team | Device Diagnostics & Health Monitoring Service | Monitors in-seat hardware performance, tracks failures, and manages predictive maintenance. |
| T-22 | In-Seat UI Team | Passenger In-Seat UI | Aggregates APIs and optimizes data for In-Seat entertainment UI. |
| T-23 | Mobile UI Team | Passenger Mobile App UI | Optimizes API responses and caching for mobile app users. |
| T-24 | Admin UI Team | Airline Admin Dashboard UI | Handles API interactions and UI customization for airline staff. |
| T-25 | Failover & Disaster Recovery Team | Active-Passive Cluster Sync | Manages failover processes, cross-region replication, and disaster recovery. |
3.2 Architectural Approach: Microservices with Kubernetes & Domain-Driven Design (DDD)
A microservices-based architecture is chosen for the IFEC system to provide:
- Modularity & Service Independence: Each feature (e.g., streaming, billing, authentication) is managed as an independent service, enabling faster updates.
- Scalability: Services can scale independently based on demand (e.g., high load on streaming services but low on billing).
- Resilience: Fault isolation ensures failures in one service do not impact others.
- Technology Flexibility: Each service can use the best-suited database, framework, or programming language.
- Cloud-Native Deployment: Kubernetes enables container orchestration, auto-scaling, and multi-cloud deployment.
This architecture follows Domain-Driven Design (DDD) principles to decompose the system into bounded contexts, ensuring that each microservice:
- Belongs to a specific domain (e.g., Streaming, Billing, Security).
- Has clearly defined API interactions.
- Uses event-driven communication for decoupling.
- Stores and processes data independently or through shared storage layers.
3.2.1 Component Interface Diagram
Figure 4: Component Diagram - microservices architecture diagram
The Component Interface Diagram represents all core and supporting services required for a scalable, resilient, and modular IFEC system. This architecture ensures seamless service orchestration, API interactions, and event-driven communication across microservices.
Key Features in the Architecture:
- Incorporates Networking & Connectivity Services: The Network Gateway Service enables intelligent bandwidth management, ensuring seamless connectivity handovers between Satellite, WiFi, and Air-to-Ground networks.
- Explicit Representation of Multi-Airline SaaS: Supports multi-tenancy, enabling airlines to customize content, branding, pricing models and network policies.
- AI-Powered Content Adaptation Added: Uses real-time user behavior tracking & event personalization to optimize streaming quality.
- Integration of Flight Tracking & Geo-Compliance: The Flight Tracking Service streams real-time aircraft location to enforce regional content filtering & airspace restrictions dynamically.
- Event-Driven Messaging Layer Integrated: Kafka/RabbitMQ enables asynchronous communication between services, improving scalability and fault tolerance.
- Service Mesh (Istio/Linkerd) for Secure Microservice Communication: Implements mTLS encryption, zero-trust policies, and observability to ensure secure inter-service interactions.
- Observability & Security Layer Defined: Monitoring, logging, compliance enforcement, and anomaly detection ensure high system reliability and security auditing..
This architecture optimizes scalability, resilience, and modularity, ensuring that the IFEC system meets modern aviation technology standards.
3.2.2 Integration Points in the Diagram
1. API Gateway (NGINX/Kong)
- Acts as the central integration layer for all API-based communication
- Routes API requests from clients to backend microservices
- Implements authentication (OAuth2, JWT), rate limiting, and monitoring
- Interfaces with the Message Broker (Kafka/RabbitMQ) for async request handling
2. Service-to-Service API Interactions (Synchronous REST/GraphQL Calls)
| Microservice Interaction | API Endpoints | Purpose |
|---|---|---|
| Streaming Service ↔ Content Service | /streaming/api, /content/api | Ensures media files are available for playback. |
| Billing Service ↔ User Authentication | /billing/api, /auth/api | Ensures only authenticated users can make payments. |
| Multi-Airline SaaS ↔ Content & Billing Services | /airlines/api, /billing/api | Manages airline-specific configurations. |
| Flight Tracking Service ↔ Geo-Compliance Engine | /flight-tracking/api, /geo-compliance/api | Dynamically adjusts content access based on airspace region. |
| Network Optimizer ↔ Connectivity Services | /network/api, /satellite/api, /air-to-ground/api | Adjusts connectivity routes based on real-time aircraft location. |
3. Event-Driven Messaging for Async Processing (Kafka/RabbitMQ/NATS)
| Microservice Publisher | Event Topic | Microservice Consumers | Purpose |
|---|---|---|---|
| Streaming Service | playback.events | Observability Layer, AI Content Recommendation | Logs playback events, detects buffering issues, and optimizes AI recommendations. |
| Billing Service | billing.transactions | AI Content Recommendation | Updates AI models when a user purchases premium content. |
| Multi-Device Sync Service | sync.events | Streaming Service | Ensures playback continuity across In-Seat screens and passenger devices. |
| Flight Tracking Service | flight.position | Geo-Compliance Engine, Network Optimizer | Adjusts content availability and dynamically switches network routing based on flight position. |
| Network Optimizer | connectivity.updates | Streaming Service, Billing Service | Ensures video quality adapts based on network congestion and bandwidth changes. |
3.2.3 Event-Driven API Endpoints for Microservices
In the IFEC system, event-driven messaging enables asynchronous processing, reducing direct service dependencies and improving scalability. The following Kafka/RabbitMQ event-based APIs ensure seamless communication across microservices.
| Microservice | Event Name | Triggering Action | Published To | API Endpoints (Producer/Consumer) |
|---|---|---|---|---|
| Streaming Service | stream.started | When a user starts a video stream | Observability Service | Producer: POST /events/stream/start, Consumer: GET /observability/logs/{stream_id} |
| Streaming Service | stream.ended | When a user finishes a video stream | AI Content Recommendation, Multi-Device Sync | Producer: POST /events/stream/end, Consumer: POST /ai/recommend/update |
| Billing Service | payment.success | When a payment is successfully processed | AI Content Recommendation, Multi-Airline SaaS | Producer: POST /events/payment/success, Consumer: POST /airlines/update_user_plan |
| Billing Service | payment.failed | When a payment fails | Security & Compliance Logs | Producer: POST /events/payment/failed, Consumer: POST /security/logs/fraud_alert |
| Multi-Device Sync Service | device.sync.update | When a user switches devices mid-stream | Streaming Service | Producer: POST /events/device/sync, Consumer: POST /stream/update_session |
| Multi-Airline SaaS Service | airline.config.update | When an airline modifies its service package | Content Service, Billing Service | Producer: POST /events/airline/update, Consumer: POST /billing/refresh_prices |
| AI Content Recommendation | user.preference.update | When user preferences change (likes, ratings) | Content Service | Producer: POST /events/user/preference, Consumer: POST /content/recommendations |
| Security & Compliance Logs | fraud.detected | When a fraudulent transaction is flagged | Observability Service | Producer: POST /events/security/fraud, Consumer: POST /monitoring/alerts |
| Observability & Monitoring | performance.alert | When a performance degradation is detected | Network Gateway | Producer: POST /events/system/performance_alert, Consumer: POST /network/adjust_resources |
| Flight Tracking Service | flight.position.update | When real-time aircraft position is updated | Geo-Compliance Service, Network Optimizer | Producer: POST /events/flight/position, Consumer: POST /geo-compliance/update_region |
| Geo-Compliance Service | geo.restriction.update | When an aircraft enters a new regional airspace | Streaming Service, AI Content Recommendation | Producer: POST /events/geo/restriction, Consumer: POST /content/filter |
| Network Optimization Service | connectivity.update | When the aircraft location changes dynamically | Streaming Service, Billing Service | Producer: POST /events/network/update, Consumer: POST /streaming/adjust_quality |
3.3 Integration Architecture View
The Asynchronous Interaction View Diagram illustrates how API Gateway, Message Brokers (Kafka/RabbitMQ), and Backend Services interact asynchronously in the IFEC system. Instead of directly calling backend microservices, the API Gateway publishes events to a message queue, which are then processed by event consumers. This design pattern enables:
- Scalability – Decouples services, allowing independent scaling of producers and consumers.
- Fault Tolerance – Requests persist in message queues, ensuring reliability even if services are temporarily down.
- Performance Optimization – Reduces API latency by shifting long-running processes to background tasks.
- Resilient Architecture – Prevents cascading failures and allows microservices to function independently.
Figure 5: Component Diagram - Asynchronouse Interaction View
Explanation
The following steps describe how the event-driven IFEC system processes requests asynchronously using an API Gateway, message brokers, and response queues.
- 1. API Gateway (NGINX/Kong) Publishes Requests
Instead of making a direct call to backend microservices, the API Gateway publishes an event to a message queue (Kafka, RabbitMQ, NATS). This enables event-driven processing, decoupling the request from the service.
- 2. Message Broker Stores the Request (Kafka/RabbitMQ)
The Request Queue (Kafka Topic) temporarily holds the pending request. This allows consumers to process requests independently without blocking the API Gateway.
- 3. Event Consumers Process Requests
Backend services (e.g., Content Service, Billing Service) listen to specific queues. When a request event arrives, the service processes it asynchronously. If a response is needed, the service publishes the result to a Response Queue.
- 4. Response Queue Delivers Processed Results
The Response Queue (Kafka Topic) holds the results of the processed requests. This ensures services are decoupled, and responses can be delivered at the right time.
- 5. API Gateway Fetches & Returns Response
Once the response is available in the Response Queue, the API Gateway retrieves it and returns it to the client.
3.4 Security Architecture Diagram (Authentication, Authorization, Policies)
The Security Architecture in the IFEC system ensures robust authentication, authorization, and policy enforcement across all microservices. It integrates:
- Authentication Mechanisms – OAuth2, OpenID Connect, JWT, and IAM for user identity verification.
- Authorization Controls – Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC).
- Policy Enforcement – API Gateway security policies, mTLS for inter-service encryption, and audit logging.
- Secure Data Handling – Secure API transactions, encrypted message queues, and compliance logging.
This architecture combines synchronous and asynchronous security enforcement, leveraging both Kubernetes-native security tools and external authentication services. For more detailed Component Diagram, please refer Figure 4.
Figure 6: Component Diagram - Security Architecture
Explanation of Security Architecture Diagram
The security architecture of the IFEC system incorporates multiple layers of protection, ensuring data integrity, access control, and compliance with industry security standards.
1. API Gateway (NGINX/Kong) – [K8s Component]
- First line of defense, enforcing authentication, authorization, and security policies.
- Validates JWT tokens before forwarding requests to microservices.
- Implements rate limiting, DDoS protection, and access control.
2. External Authentication & Identity Services – [External Services]
- OAuth2 Provider (Identity Server): Issues and verifies access tokens.
- OpenID Connect: Manages user authentication & federated identity.
- JWT Auth Server: Handles token validation and expiry checks.
- IAM Service (RBAC, ABAC): Enforces role-based and attribute-based access policies.
3. Secure API Calls & Token Validation
- API Gateway validates user identity and permissions before granting access.
- IAM service enforces RBAC (Role-Based Access Control) and ABAC (Attribute-Based Access Control).
4. Microservices-Level Security – [K8s Components]
- Content Service: Secures content metadata and ingestion APIs.
- Streaming Service: Enforces access control for premium content.
- Billing Service: Secures payment transactions via encrypted communication.
- Security Logs: Stores audit trails and access logs for compliance tracking.
5. Service Mesh (Istio/Linkerd) – [K8s Component]
- Implements Zero-Trust security for inter-service communication.
- mTLS (mutual TLS) encrypts data in transit between microservices.
- Policy-based access control ensures only authorized services can communicate.
6. Additional Security Services – [K8s Components]
- Multi-Airline SaaS: Ensures airline-specific security policies.
- AI Content Recommendation: Monitors user behavior for security & fraud detection.
- Observability Service (SIEM Logging): Detects security anomalies and generates alerts.
- Security Threat Detection Service: Uses AI-based threat intelligence to flag suspicious activities.
Technology Stack Selection for IFEC System
The Technology Stack Selection phase is critical for ensuring the IFEC system is:
- Portable & Cloud-Agnostic: Avoids vendor lock-in by focusing on Kubernetes-native tools instead of cloud provider-specific services.
- Optimized for Microservices: Uses lightweight, scalable frameworks with high performance.
- DevOps & GitOps-Driven: Ensures automated, secure, and version-controlled deployments.
- Developer-Friendly: Chooses technologies based on team expertise to ensure maintainability.
- Future-Proof: Adopts modern programming languages, web frameworks, and service orchestration tools.
Technology Stack Selection Table
| Category | Technology | Purpose | Rationale |
|---|---|---|---|
| Kubernetes Platform | Kubernetes (K8s) (Vanilla, K3s, MicroK8s) | Container orchestration for microservices | Lightweight, portable, avoids cloud dependency |
| GitOps & Deployment | FluxCD, ArgoCD | Declarative, Git-based Kubernetes deployment automation | Ensures continuous delivery & auto-recovery |
| Container Runtime | Containerd, CRI-O | Efficient container execution in Kubernetes | Cloud-agnostic alternative to Docker |
| Service Mesh | Istio, Linkerd | mTLS-based inter-service security, traffic routing, observability | Ensures Zero-Trust security & encrypted microservice communication |
| Ingress Controller | Istio Ingress Gateway | Cluster-level load balancing & security | Manages inter-service routing & external traffic ingress securely |
| API Gateway | Kong, Ambassador | Manages authentication, request validation, routing | Provides OAuth2, JWT authentication |
| Programming Language | TypeScript (Node.js, Next.js), Golang, Python, C++ | Backend and frontend development | Type-safe, high-performance, async-friendly |
| Backend-for-Frontend (BFF) Web UI | Next.js (React), Tailwind CSS | Optimizes API interactions for different UI clients | Reduces latency for In-Seat, mobile, and admin UIs |
| In-Seat Touchscreen (BFF) UI | Qt, QML, C++ | Optimized UI for in-seat entertainment screens | High-performance, native rendering, runs on low-power embedded devices |
| Database | K8ssandra (Cassandra on Kubernetes) | Highly available, scalable distributed database | No single point of failure, ideal for multi-region IFEC workloads |
| Object Storage | MinIO, Ceph, OpenEBS | Media storage & content retrieval | Kubernetes-native, supports large file storage |
| Message Broker | Kafka, RabbitMQ, NATS | Asynchronous messaging & event-driven architecture | Handles real-time IFEC event streaming |
| WebSockets & Real-Time Messaging | Socket.io, WebRTC | Live playback synchronization & real-time chat | Enables instant multi-device experience |
| Monitoring & Logging | Prometheus, Grafana, Loki | Observability, real-time alerts, and logging | Open-source, Kubernetes-native observability |
| Security & Compliance | OPA/Gatekeeper, Kyverno | Policy enforcement and compliance validation | Ensures RBAC, security policies adherence |
| Identity Management | Dex, Keycloak | OAuth2/OpenID Connect identity provider | Manages user authentication & authorization |
| Network & Connectivity | Cilium (eBPF), Calico | Kubernetes networking & security enforcement | Secure pod-to-pod and network traffic |
| Edge Caching | Redis, Varnish, CDN (CloudFront, Fastly) | Stores frequently accessed content at the Edge Cluster | Reduces cloud API requests and improves performance |
Explanation of Key Technology Selections
- Kubernetes (K8s, K3s, MicroK8s): Primary container orchestration for microservices, cloud-agnostic, lightweight (K3s for edge cases).
- GitOps (FluxCD, ArgoCD): Automates infrastructure and application deployments, ensuring continuous delivery and rollback capabilities.
- Service Mesh (Istio, Linkerd): Implements mTLS encryption traffic control and observability for secure microservice communication.
- Cluster-Level Ingress (Istio Gateway): Manages secure load balancing and inter-service routing inside Kubernetes clusters.
- Programming Languages & Web Frameworks: Golang, TypeScript/Node.js, Python for backend; Next.js for frontend.
- Networking & Connectivity: Cilium (eBPF) for Kubernetes-native networking Calico for network isolation and traffic policies.
- Database & Event Streaming: K8ssandra (Cassandra on Kubernetes) for distributed storage Kafka, RabbitMQ, NATS for event-driven microservices.
- Security & Policy Enforcement: Dex, Keycloak for OAuth2/OpenID identity federation; OPA/Gatekeeper, Kyverno for policy security and compliance.
4. Implementation & Deployment Phase
The Implementation & Deployment Phase focuses on developing, deploying, and operationalizing the IFEC system. This phase ensures that architectural blueprints are turned into running, scalable, and production-ready services.
Goals of This Phase:
- Convert architectural design into a fully deployed cloud-native system.
- Ensure infrastructure setup aligns with scalability, security, and performance goals.
- Implement CI/CD automation using GitOps to streamline deployments.
- Deploy microservices across multiple Kubernetes clusters for redundancy and failover.
- Establish observability and monitoring to track system health and performance.
4.1 Infrastructure Architecture
The Infrastructure Setup phase is the foundation of the IFEC system, ensuring that the deployment environment supports scalability, security, and operational efficiency. This phase includes:
- Cluster Division: Based on standard criteria such as security, workload separation, and scalability.
- Namespace Segmentation: Follows best practices for environment isolation (Dev, QA, Prod).
- CI/CD Automation: Implements GitOps (FluxCD, ArgoCD) for automated deployment.
- Service Mesh Integration: Uses Istio or Linkerd for secure and observable microservice communication.
- Observability & Logging: Deploys Prometheus, Grafana, Loki for monitoring.
4.1.1 IFEC Service Assignment Criteria for Kubernetes Clusters
| MicroService | Cluster Type | Placement Criteria | Storage & DB | Cloud Name & Location | Location Rationale | Containers |
|---|---|---|---|---|---|---|
| Streaming Service | Edge Cluster | Low latency for real-time video delivery, close to passengers | MinIO, Ceph (Object Storage) | Onboard Aircraft (Edge Cluster – In-Flight) | Reduces cloud dependency, ensures real-time playback. | HLS/DASH Streaming Engine, Video Processing, CDN Cache, DRM Protection |
| AI Content Recommendation | Edge Cluster (Inference), Primary Cluster (Training) | Requires GPU processing, real-time personalization | Cassandra (Metadata), Redis (Cache) | Onboard Aircraft (Inference), Ground Cloud (Training) | Training AI models on the ground minimizes in-flight compute usage | Pre-trained models cached for inference, reducing GPU load onboard |
| Passenger In-Seat UI | Edge Cluster | Optimized API aggregation for In-Seat screen services | Redis (Cache), API Gateway | Onboard Aircraft (Edge Cluster – In-Flight) | Ensures seamless passenger experience with reduced cloud dependency. | REST API Aggregator, UI Session Manager, Content Personalization Proxy |
| Passenger Mobile UI | Edge Cluster | Optimized API aggregation for mobile app | Redis (Cache), API Gateway | Onboard Aircraft (Edge Cluster – In-Flight) | Optimizes mobile user experience with low-latency requests. | REST API Aggregator, Multi-Device Sync Proxy |
| Airline Admin Dashboard UI | Primary Cluster | Handles airline management & analytics UI requests | Cassandra (Admin Logs) | AWS/GCP/Azure (Ground Cloud – Multi-Region) | Ensures secure access to airline-specific configurations. | REST API Aggregator, User Authentication Manager |
| Multi-Airline SaaS Mgmt | Primary Cluster | Multi-tenant, centralized for airline-specific configurations | Cassandra | AWS/GCP/Azure (Ground Cloud – Multi-Region) | Airlines require global access, ensuring scalability. | Multi-Tenancy Handler, Airline Branding Module, Airline Pricing Engine |
| Billing & Payment System | Primary Cluster | Secure transactions, requires centralized database | Cassandra (Transactional DB) | AWS/GCP/Azure (Ground Cloud – Multi-Region) | Ensures PCI-DSS compliance, centralized processing. | Transaction Handler, Currency Converter, Fraud Detection Engine |
| User Authentication (OAuth2, SSO) | Security Cluster | Critical security service, requires strict access controls | Keycloak, Cassandra (User Store) | AWS/GCP/Azure (Ground Cloud – Multi-Region) | Ensures secure user authentication across all services. | OAuth2 Server, JWT Token Handler, RBAC/ABAC Enforcement |
| Security & Compliance Logs | Security Cluster | Must be isolated for regulatory compliance (GDPR, PCI-DSS) | Loki, ELK Stack | AWS/GCP/Azure (Ground Cloud – Multi-Region) | Must be tamper-proof and stored securely. | Log Collector, SIEM Analytics, Security Alerting |
| API Gateway | Primary & Edge Cluster | Routes external requests, integrates with message broker for async processing | Redis (Rate Limiting, Caching) | Onboard Aircraft & Ground Cloud (Multi-Region) | Routes traffic based on proximity to users. | Traffic Router, Authentication Middleware, Rate Limiting Engine |
| Observability & Monitoring | Observability Cluster | Centralized logging, monitoring, SIEM security analytics | Prometheus, Grafana, Loki | AWS/GCP/Azure (Ground Cloud – Multi-Region) | Ensures operational visibility across all clusters. | Metrics Aggregator, Distributed Tracing, Performance Dashboards |
| AI Anomaly Detection (Security) | Security Cluster | Uses AI to detect fraud, must be isolated for compliance | Cassandra, ELK Stack | AWS/GCP/Azure (Ground Cloud – Multi-Region) | Must be kept isolated for security & fraud detection. | Anomaly Detector, Security AI Engine, Log Analysis Module |
| Flight Tracking Service | Edge & Primary Cluster | Provides real-time aircraft position for geo-aware services | Cassandra (Flight Data), Kafka (Event Stream) | Onboard Aircraft & Ground Cloud (Multi-Region) | Must be processed in real-time for navigation & compliance. | ADS-B Receiver, GPS Integrator, Flight Path Calculator |
| Geo-Compliance Service | Security Cluster | Dynamically restricts content based on aircraft's location | Cassandra (Geo-Policies) | AWS/GCP/Azure (Ground Cloud – Multi-Region) | Ensures compliance with regional streaming laws. | Airspace Rule Processor, DRM Enforcement Engine, Compliance Checker |
| Network Optimization Service | Edge & Primary Cluster | Adjusts connectivity based on real-time aircraft position | Cassandra (Network Analytics), Kafka (Connectivity Events) | Onboard Aircraft & Ground Cloud (Multi-Region) | Dynamically optimizes satellite/WiFi transitions. | Bandwidth Allocator, Satellite/5G Switcher, QoS Enforcer |
| In-Seat Firmware Update Service | Edge Cluster | Handles OTA firmware updates for In-Seat devices | MinIO (Firmware Storage), Cassandra (Device Metadata), Redis (Cache) | Onboard Aircraft (Edge Cluster – In-Flight) | Ensures secure firmware delivery & rollback mechanisms. | OTA Firmware Delivery Module, Version Rollback Handler, Update Verification Engine |
| Content Ingestion & Encoding Service | Primary Cluster | Prepares content before deployment to aircraft | Cassandra (Metadata), MinIO (Storage) | AWS/GCP/Azure (Ground Cloud – Multi-Region) | Ensures all content is optimized before reaching aircraft. | Video Encoder, Metadata Processor, DRM Manager |
| Multi-Cloud Deployment Service | Primary Cluster | Ensures high availability, multi-region failover, and CI/CD automation | NoSQL Metadata DB (ArgoCD Config Store), Terraform State Files | AWS/GCP/Azure (Ground Cloud – Multi-Region) | Manages Kubernetes deployments, CI/CD pipelines, and cloud failover. | ArgoCD, FluxCD, Kubernetes Cluster Manager, Terraform Automator |
4.1.2 IFEC Microservices Node Assignment with System Configuration
This table details the allocation of microservices to various worker nodes within the IFEC system, ensuring optimized performance, scalability, and security compliance.
| Worker Node Name | Cluster Type | Node Type | vCPUs | RAM | GPU Required | Assigned Microservices | Optimization Rationale |
|---|---|---|---|---|---|---|---|
| Streaming Worker Node | Edge Cluster | Low-Power Streaming Node | 6 vCPUs | 24GB RAM | No | Streaming Service | Uses adaptive bitrate streaming to reduce CPU usage and memory load. |
| AI Processing Node | Edge Cluster (Inference), Primary Cluster (Training) | GPU-Optimized Inference Node | 8 vCPUs | 32GB RAM | Yes | AI Content Recommendation | Only inference models run onboard to reduce GPU usage, full training occurs in the cloud. |
| Device Sync Worker Node | Edge Cluster | Low-Power Sync Node | 4 vCPUs | 16GB RAM | No | Multi-Device Sync Service | Uses Bluetooth Low Energy (BLE) for syncing instead of high-power WiFi. |
| In-Flight Firmware OTA Node | Edge Cluster | General-Purpose Node | 6 vCPUs | 24GB RAM | No | In-Seat Firmware Update Service | Firmware updates occur only when aircraft is on the ground. |
| Flight Tracking & Compliance Node | Edge & Primary Cluster | Efficient Flight Data Node | 6 vCPUs | 24GB RAM | No | Flight Tracking Service, Geo-Compliance Service | Processes flight data in bursts every few minutes instead of continuously. |
| Network Optimization Node | Edge & Primary Cluster | Bandwidth Control Node | 6 vCPUs | 24GB RAM | No | Network Optimization Service | Reduces network switching frequency using predictive analytics. |
| Content Processing Node | Primary Cluster | GPU-Optimized Node | 16 vCPUs | 64GB RAM | Yes | Content Ingestion & Encoding Service | Runs in the cloud only, preventing high onboard power draw. |
| Multi-Airline SaaS Node | Primary Cluster | Multi-Tenant Node | 4 vCPUs | 16GB RAM | No | Multi-Airline SaaS Management | Cloud-based processing minimizes onboard workload. |
| Billing & Transactions Node | Primary Cluster | Security-Focused Node | 6 vCPUs | 24GB RAM | No | Billing & Payment System | Offloaded all transactions to cloud to save aircraft CPU cycles. |
| Authentication & IAM Node | Security Cluster | Security Identity Node | 6 vCPUs | 24GB RAM | No | User Authentication (OAuth2, SSO), IAM | JWT tokens cached to reduce in-flight validation requests. |
| Security & Fraud Detection Node | Security Cluster | AI Security Node | 12 vCPUs | 48GB RAM | Yes | AI Anomaly Detection (Security) | Event-driven fraud detection reduces continuous CPU load. |
| Compliance & Regulatory Node | Security Cluster | Regulatory Compliance Node | 6 vCPUs | 24GB RAM | No | Security & Compliance Logs | Only critical logs are stored onboard, full logs offloaded. |
| API Gateway & Rate Limiting Node | Primary & Edge Cluster | Efficient API Routing Node | 4 vCPUs | 16GB RAM | No | API Gateway | Request caching and rate limiting optimize API workload. |
| Observability & Logging Node | Observability Cluster | Low-Power Observability Node | 6 vCPUs | 24GB RAM | No | Observability & Monitoring | Only security-critical logs are collected onboard. |
| Flight Path & Weather Data Node | Primary Cluster | Low-Resource Flight Data Node | 4 vCPUs | 16GB RAM | No | Flight Path & Weather Data Integration | Pre-fetches flight/weather data to reduce real-time network calls. |
| Passenger Assistance Chatbot Node | Primary Cluster | Low-Power AI Node | 6 vCPUs | 24GB RAM | Yes | Passenger Assistance Chatbot Service | NLP models preloaded to avoid real-time GPU processing onboard. |
| Device Health Monitoring Node | Edge Cluster | Device Diagnostics Node | 6 vCPUs | 24GB RAM | No | Device Diagnostics & Health Monitoring Service | Predicts in-seat hardware failures, ensuring proactive maintenance. |
| Passenger In-Seat UI Node | Edge Cluster | Passenger UI Optimization Node | 6 vCPUs | 24GB RAM | No | Passenger In-Seat UI | Processes In-Seat UI-specific API aggregation for low-latency user interactions. |
| Passenger Mobile UI Node | Edge Cluster | Mobile UI Optimization Node | 6 vCPUs | 24GB RAM | No | Passenger Mobile UI | Optimizes API calls for mobile passenger apps to reduce data consumption. |
| Airline Admin Dashboard UI Node | Primary Cluster | Admin UI Backend Optimization | 8 vCPUs | 32GB RAM | No | Airline Admin Dashboard UI | Handles API aggregation for airline staff dashboards, minimizing direct API load. |
| Multi-Cloud Deployment Node | Primary Cluster | CI/CD Deployment Node | 8 vCPUs | 32GB RAM | No | Multi-Cloud Deployment Service | Manages multi-cloud failover, Kubernetes workloads, and CI/CD pipelines. |
Justifications for System Configurations
The following system configurations are strategically allocated to optimize performance, security, power efficiency, and scalability:
- Low-Power Streaming Nodes: Optimized for real-time video playback while using adaptive bitrate streaming to minimize CPU and memory load.
- GPU-Optimized Nodes: Used for AI inference (onboard) while offloading model training to cloud-based processing, reducing energy consumption in-flight.
- Low-Energy Sync Nodes: Bluetooth Low Energy (BLE) is preferred over WiFi for multi-device syncing, reducing power drain while maintaining seamless user experience.
- Security-Focused Nodes: Deployed for authentication, billing, and compliance services, ensuring PCI-DSS and GDPR adherence with minimal in-flight processing overhead.
- Flight Data Nodes: Optimized for periodic batch processing rather than continuous real-time updates to prevent unnecessary power spikes.
- Bandwidth Control Nodes: Dynamically manage satellite, WiFi, and Air-to-Ground transitions using predictive analytics, reducing frequent network switching.
- Observability Nodes: Only critical logs and security alerts are processed in-flight, with full monitoring workloads offloaded to the cloud.
- General-Purpose Nodes: Handles background services like multi-airline SaaS, API Gateway, and content ingestion, where real-time performance isn’t a priority.
Figure 7: IFEC Infrastructure Architecture
4.2. IFEC Infrastructure Components
1. Global Load Balancer (GLB) for Multi-Cloud Failover
- Handles external requests from passengers, airlines, and backend services.
- Uses latency-based routing to direct users to the closest active cluster.
- In case of a failure, it automatically switches traffic to the passive backup cluster.
- Ensures high availability by rerouting network requests dynamically based on API health checks.
2. API Gateway for Multi-Cluster API Security
- Acts as the entry point for IFEC services across regions.
- Implements authentication (OAuth2, JWT, API Key, SSO) and rate limiting.
- Handles real-time API traffic balancing between active and passive clusters.
- Routes API calls to Kafka for asynchronous processing to prevent blocking requests.
3. Ingress Controllers for Internal Cluster Traffic Routing
- Each cluster has its own Istio-based Ingress for secure traffic routing.
- Manages microservice discovery across Edge, Primary, and Security clusters.
- Uses mTLS for service-to-service encryption, preventing unauthorized API calls.
4. Service Mesh for Secure Inter-Cluster Communication
- Encrypts all microservice-to-microservice traffic via mutual TLS (mTLS).
- Handles retries, circuit breaking, and failure recovery across microservices.
- Provides observability for service health monitoring and anomaly detection.
- Ensures fine-grained traffic policies for AI content recommendations and flight data updates.
5. Kafka Message Bus for Event-Driven Communication
- Used for async communication between services, reducing direct service dependencies.
- Handles high-volume real-time event processing (e.g., user interactions, flight tracking updates).
- Enables AI-driven recommendations by capturing real-time passenger preferences.
- Streams analytics data to observability tools (Prometheus, Grafana, Loki) for performance monitoring.
6. RabbitMQ for Reliable Transactional Messaging
- Ensures message ordering and guaranteed delivery for mission-critical transactions.
- Handles in-seat authentication, playback synchronization, and network handovers.
- Processes security logs and fraud detection alerts with lower overhead compared to Kafka.
7. Edge Cluster (Onboard Aircraft) for Local Processing
- Hosts passenger In-Seat services, AI-powered content recommendations, and WiFi device syncing.
- Caches frequently accessed content to reduce cloud latency and save network bandwidth.
- Runs AI inference models (pre-trained in the cloud) to optimize personalization without onboard training overhead.
- Stores passenger session data temporarily before syncing to the cloud to minimize in-flight compute power.
8. Cassandra DB for Multi-Region Distributed Data Storage
- Replaces PostgreSQL for better high availability & distributed performance.
- Ensures fast reads/writes and automatic replication across cloud regions.
- Partitions user, flight data, and content metadata across multiple datacenters for resilience.
- Optimized for time-series and event-driven data, improving AI recommendations and observability tracking.
9. Observability Cluster for Monitoring & Security
- Collects logs, metrics, and traces across all clusters.
- Uses Prometheus, Loki, and Grafana for real-time monitoring.
- Includes SIEM-based anomaly detection for security threats and fraud prevention.
- Streams security logs to RabbitMQ queues for forensic analysis and audit trails.
Recommended Active-Passive Failover Strategy
| Failover Component | Active Cluster | Passive Cluster | Failover Mechanism |
|---|---|---|---|
| Global Load Balancer (GLB) | Routes traffic to active site | Fails over if health check fails | DNS-based failover |
| API Gateway | Handles live traffic | Standby, inactive | API-level health checks |
| Service Mesh | Manages service routing | Standby, syncs policies | Policy-based traffic redirection |
| Kafka Event Broker | Processes real-time events | Standby, consuming replicated logs | Auto-replay on failover |
| RabbitMQ Queue | Processes in-seat authentication & playback sync | Standby, waiting for failover | Message queue persistence with dead-letter support |
| Cassandra DB Cluster | Primary reads/writes | Replicated but read-only | Automatic leader election |
| Observability (Logging, Metrics) | Collects active logs | Standby, failover alerting | Replica syncs in real-time |
4.3 Deployment & Scaling Strategy
The Deployment & Scaling Strategy for the In-Flight Entertainment & Connectivity (IFEC) system ensures that new software releases are safely deployed without service downtime while maintaining high availability.
4.3.1 GAP Analysis Table: Canary vs. Blue-Green Deployment for IFEC
| Criteria | Canary Deployment | Blue-Green Deployment | Rationale for IFEC | Chosen Deployment Strategy Per Cluster |
|---|---|---|---|---|
| Risk Mitigation | +++ | +++ | Since IFEC updates happen on the ground, both strategies offer low risk during deployment. | Both Canary (Primary Cloud) & Blue-Green (Edge & Security Clusters) |
| Rollback Speed | -- | +++ | Blue-Green allows instant rollback if issues arise; Canary takes longer to shift traffic back. | Blue-Green for Security/Firmware (Edge & Security Clusters) |
| Resource Utilization | +++ | --- | Canary uses the same infrastructure, while Blue-Green requires extra resources for parallel environments. | Canary for Feature Updates (Primary Cloud Cluster) |
| Deployment Complexity | -- | +++ | Canary requires advanced traffic shifting; Blue-Green is easier since the switch happens in bulk. | Blue-Green for Simpler Bulk Deployments (Edge Cluster) |
| In-Flight Service Continuity | +++ | +++ | Since updates happen on the ground, neither strategy impacts in-flight passengers. | No in-flight disruptions for both strategies |
| Update Testing Before Full Rollout | +++ | -- | Canary allows gradual testing with a controlled group; Blue-Green releases everything at once. | Canary for API/UI Testing (Primary Cloud Cluster) |
| Downtime During Deployment | +++ | +++ | Both strategies avoid downtime by deploying in separate maintenance windows. | No in-flight downtime risk |
| Use Case for IFEC | +++ | +++ | Canary is best for content and feature updates, while Blue-Green is best for firmware and security patches. | Canary (Primary Cloud) & Blue-Green (Security & Edge Clusters) |
4.1.3 CI/CD Pipeline for IFEC Deployments
The GitHub & ArgoCD-based CI/CD pipeline designed to:
- Schedule deployments only when the aircraft is on the ground (layovers, maintenance windows).
- Automate both Canary and Blue-Green deployments for feature rollouts, firmware updates, and security patches.
- Use GitOps (ArgoCD) for version control, rollback, and environment consistency.
- Ensure zero in-flight disruptions by syncing updates when the aircraft reconnects to ground services.
Figure 9: Canary CI/CD Pipeline
4.1.4 IFEC Failover Strategy Analysis
This table outlines failover strategies for different failure scenarios in the IFEC system, ensuring continuous service availability both onboard and in the ground cloud infrastructure.
| Failure Scenario | Onboard Edge Cluster Failover Strategy | Ground Cloud Cluster Failover Strategy |
|---|---|---|
| Flight loses power mid-flight | Fails over to battery-powered in-seat screens. Cached content stored in MinIO/Redis allows continued streaming. Passengers can still watch preloaded content. | N/A (Ground cloud unaffected by onboard power loss). |
| Loss of satellite & Air-to-Ground connectivity | Local Edge Cluster caches all critical content & DRM tokens. Streaming continues via cached content. Playback sync resumes when connectivity is restored. | Primary Cluster fails over to Secondary Cloud Region. API requests are queued for resync when the aircraft reconnects. |
| In-seat device failure | Device auto-reboots and syncs session state via Bluetooth/WiFi mesh. Passengers can switch to mobile devices for continued service. | N/A (Ground cloud unaffected by in-seat failures). |
| Onboard Kubernetes Edge Cluster crashes | Flight attendants can reset Edge Cluster nodes manually. Cached services like movie streaming, seatback UI, and payment requests remain functional in offline mode. | N/A (Ground cloud unaffected by Edge Cluster). |
| Ground Cloud Cluster (Primary) fails | Onboard Edge Cluster operates in offline mode. Preloaded content, local authentication, and session data continue to work until connectivity is restored. | Primary Cloud fails over to Secondary Cloud (Disaster Recovery). API Gateway re-routes traffic to backup cloud cluster. |
| Ground Cloud authentication service goes down | Onboard Edge Cluster uses cached OAuth2/JWT tokens for authentication. Passengers remain logged in for the flight duration. | Authentication service in Primary Cloud fails over to Secondary Cloud. User sessions persist across regions. |
| Security Cluster (SIEM Monitoring) fails | Onboard Edge Cluster continues running but logs events locally. Security logs sync when connectivity is restored. | Fails over to backup Security Cluster. Logs temporarily stored in failover storage until SIEM services resume. |
| Firmware update fails mid-deployment | Rollback to previous firmware version stored in in-seat device storage. OTA updates resume when aircraft reconnects to the ground. | Firmware update rolled back automatically using ArgoCD Blue-Green Deployment. Primary version remains active until verified. |
| Full API Gateway outage | Onboard Edge Cluster continues running local services. Preloaded content is served; requests queue until connectivity restores. | Secondary API Gateway in Disaster Recovery Cloud takes over. API traffic re-routed via global load balancer (GLB). |