【摘要】 近年来，人工智能(AI)计算服务的规模和复杂性迅速增长要求算力资源能够被灵活访问和高效使用。作为用户与算力资源间访问和交互的重要通道，网络的能力和性能也亟需进行提升以支持AI计算服务的应用需求，如低时延、高并发等。然而，传统的域名系统(DNS)和基于IP的调度机制在满足这些需求方面面临适应性不足和智能化缺失的问题。因此，计算与网络资源的一体化(即算网融合)成为了解决上述问题的关键途径。鉴于此，该文引入了一种面向AI计算的语义化服务标识(AISID)，用于对服务进行编码，AISID的引入实现了服务请求与资源位置的解耦，从而支持更灵活精确的服务调度。在此基础上，提出一种算网融合的轻量化增量部署方案，通过将智能路由与资源调度相结合以优化服务请求的路由及资源分配。通过对核心设备实施轻量化的增量部署，可在最小改动现有网络的情况下优化网络性能，并增强系统可扩展性。实验结果表明，在500个并发请求的负载条件下，相较于传统的DNS调度和网络架构，AISID机制将请求响应时间降低了61.3%；轻量化部署方案使链路带宽使用率方差和算力使用率方差分别降低32.8%和12.3%。这些结果验证了所提方法在提升AI计算服务性能和资源利用效率方面的有效性，表明该方法为实现算网融合提供了一种有效途径。更多还原

【Abstract】 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 a64-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 leastconnections 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.更多还原