Paper Title
Optimizing Latency and Intelligence Trade-Offs in AI-Driven Games: Edge-Cloud Architectures, Scheduling Policies, and Observability Frameworks
Keywords
- Latency–Intelligence Trade-Offs
- AI-Driven Games
- Latency Fundamentals
- Edge–Cloud Intelligence Continuum (ECIC)
- AI Complexity Metrics
- Model Optimization Techniques
- Latency Intelligence Trade-Off Framework (LITF)
- Scheduling & Orchestration Policies
- Network Optimization
- Observability & QoE Management
- Game AI Security
- Sustainability in Edge AI
Article Type
Review Article
Research Impact Tools
Publication Info
Volume: 4 | Issue: 2 | Pages: 647-671
Published On
April, 2024
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Abstract
A unified framework for optimizing latency and intelligence tradeoffs in AI driven live games is introduced, addressing the challenge of delivering sub 50 ms responsiveness alongside ever increasing AI sophistication. The discussion begins by characterizing latency fundamentals, including human motion to photon thresholds, network jitter, and server tick rate dynamics, and by defining intelligence metrics such as model size, inference accuracy, and engagement uplift. Building on these foundations, the Edge and Cloud Intelligence Continuum (ECIC) Framework dynamically assigns inference tasks to device, edge POP, or regional cloud tiers based on real time latency and cost signals. Contemporary edge computing architectures, federated inference meshes, and peer to peer offload strategies are surveyed, followed by model optimization techniques such as quantization, pruning, cascaded inference, and dynamic fidelity scaling that enable tight latency budgets without sacrificing AI fidelity. A novel Latency and Intelligence Trade Off Framework (LITF) employs Pareto frontier analysis, utility functions, and genre specific sensitivity studies to guide optimal resource allocation. These insights are operationalized through scheduling and orchestration policies that include reinforcement learning controllers, QoS aware load balancing, and fail fast rollback modes, together with complementary network optimizations such as QUIC tuning and edge assisted compression. Observability and QoE management integrate end to end latency tracing, real time scorecards, and feedback loops into the LITF scheduler. Security, fairness, and sustainability analyses complete the blueprint. Empirical evaluations across battle royale shooters, augmented reality mobile titles, and cloud native MMOs validate the proposed approach, and practitioner guidelines distill actionable best practices for scalable, responsive, and sustainable game AI infrastructure.
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