Lightweight Incremental Deployment for Computing-Network Converged AI Services

Objective The rapid expansion of Artificial Intelligence (AI) computing services has heightened the demand for flexible access and efficient utilization of computing resources. Traditional Domain Name System (DNS) and IP-based scheduling mechanisms are constrained in addressing the stringent requirements of low latency and high concurrency, highlighting the need for integrated computing-network resource management. To address these challenges, this study proposes a lightweight deployment framework that enhances network adaptability and resource scheduling efficiency for AI services.Methods The AI-oriented Service IDentifier (AISID) is designed to encode service attributes into four dimensions: Object, Function, Method, and Performance. Service requests are decoupled from physical resource locations, enabling dynamic resource matching. AISID is embedded within IPv6 packets (Fig. 5), consisting of a 64-bit prefix for identification and a 64-bit service-specific suffix (Fig. 4). A lightweight incremental deployment scheme is implemented through hierarchical routing, in which stable wide-area routing is managed by ingress gateways, and fine-grained local scheduling is handled by egress gateways (Fig. 6). Ingress and egress gateways are incrementally deployed under the coordination of an intelligent control system to optimize resource allocation. AISID-based paths are encapsulated at ingress gateways using Segment Routing over IPv6 (SRv6), whereas egress gateways select optimal service nodes according to real-time load data using a weighted least-connections strategy (Fig. 8). AISID lifecycle management includes registration, query, migration, and decommissioning phases (Table 2), with global synchronization maintained by the control system. Resource scheduling is dynamically adjusted according to real-time network topology and node utilization metrics (Fig. 7).Results and Discussions Experimental results show marked improvements over traditional DNS/IP architectures. The AISID mechanism reduces service request initiation latency by 61.3% compared to DNS resolution (Fig. 9), as it eliminates the need for round-trip DNS queries. Under 500 concurrent requests, network bandwidth utilization variance decreases by 32.8% (Fig. 10), reflecting the ability of AISID-enabled scheduling to alleviate congestion hotspots. Computing resource variance improves by 12.3% (Fig. 11), demonstrating more balanced workload distribution across service nodes. These improvements arise from AISID’s precise semantic matching in combination with the hierarchical routing strategy, which together enhance resource allocation efficiency while maintaining compatibility with existing IPv6/DNS infrastructure (Fig. 2, Fig. 3). The incremental deployment approach further reduces disruption to legacy networks, confirming the framework’s practicality and viability for real-world deployment.Conclusions This study establishes a computing-network convergence framework for AI services based on semantic-driven AISID and lightweight deployment. The key innovations include AISID’s semantic encoding, which enables dynamic resource scheduling and decoupled service access, together with incremental gateway deployment that optimizes routing without requiring major modifications to legacy networks. Experimental validation demonstrates significant improvements in latency reduction, bandwidth efficiency, and balanced resource utilization. Future research will explore AISID’s scalability across heterogeneous domains and its robustness under dynamic network conditions.