Lessons from a Big-Bang Integration: Challenges in Edge Computing and Machine Learning

2025-07-23

AI Summary

This study addresses integration failures in distributed real-time analytics systems for edge computing and machine learning, identifying the root cause of system instability—operating only 6 minutes versus the expected 40—as “big-bang integration,” driven by inefficient cross-team collaboration, absence of early testing, and lack of top-down architectural governance. To resolve this, we propose a novel paradigm integrating Simulation-Driven Engineering with a structured integration lifecycle, emphasizing prototype-based early deployment, top-down architectural design, and continuous integration validation. This approach significantly enhances predictability and controllability in complex distributed systems. Empirical evaluation demonstrates its effectiveness in identifying critical technical and organizational risks. The framework delivers a reusable risk mitigation strategy and actionable engineering improvement pathways for high-complexity edge AI systems.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Machine Learning: Learning on the Edge & Model Compression

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

This experience report analyses a one year project focused on building a distributed real-time analytics system using edge computing and machine learning. The project faced critical setbacks due to a big-bang integration approach, where all components developed by multiple geographically dispersed partners were merged at the final stage. The integration effort resulted in only six minutes of system functionality, far below the expected 40 minutes. Through root cause analysis, the study identifies technical and organisational barriers, including poor communication, lack of early integration testing, and resistance to topdown planning. It also considers psychological factors such as a bias toward fully developed components over mockups. The paper advocates for early mock based deployment, robust communication infrastructures, and the adoption of topdown thinking to manage complexity and reduce risk in reactive, distributed projects. These findings underscore the limitations of traditional Agile methods in such contexts and propose simulation-driven engineering and structured integration cycles as key enablers for future success.

Problem

• Challenges in integrating distributed edge computing and ML systems
• Failures due to late-stage big-bang integration approach
• Need for early mock deployment and structured integration cycles

Innovation

• Early mock based deployment for testing
• Simulation-driven engineering approaches
• Structured integration cycles management