面向移动边缘计算中流处理的、基于大语言模型辅助合同网协商的多智能体调度
Multi-Agent Scheduling with LLM-Assisted Contract Net Negotiation for Stream Processing in Mobile Edge Computing
AI总结：
针对移动边缘计算流处理的分散调度难题，提出MAS-DecStream模型，其核心为LLM-MR-CNP协议，实验表明该模型可降低延迟违规、提升冲突解决率与效用，多轮CNP优化及LLM辅助均具价值。
AI中文摘要：
流处理系统日益在异构移动边缘-云基础设施中运行，工作负载波动、资源争用及严格的服务质量（QoS）要求使分散式调度变得复杂。本文提出MAS-DecStream，其主要贡献为LLM-MR-CNP：对经典合同网协议（Contract Net Protocol）的扩展，具备语义CFP（调用提案）制定、渐进式上下文披露、多轮提案修订、协商记忆及确定性验证功能。边缘集群智能体基于局部观测、预测资源状态及定性运行时上下文优化自然语言卸载提案，而硬资源和QoS约束保持确定性。基于阿里巴巴ASI Trace的实验从三个层面评估该扩展：单轮与多轮CNP、基于规则与LLM辅助优化、固定模型单轮与多轮协商。在评估配置下，MAS-DecStream将延迟违规降至3%，消除资源过度承诺，20个智能体下冲突解决率达0.91，相比多轮基于规则基线的效用提升最高22%。另一项25案例评估显示模型与提示依赖的精度-成本权衡。结果提供初步证据：多轮CNP优化是协议层面的主要增益，LLM辅助为定性及不确定运行时上下文增添价值。
英文摘要：
Stream-processing systems increasingly operate across heterogeneous mobile edge--cloud infrastructures, where workload volatility, resource contention, and stringent quality-of-service (QoS) requirements complicate decentralized scheduling. This paper proposes \emph{MAS-DecStream}, whose main contribution is \emph{LLM-MR-CNP}: an extension of the classical Contract Net Protocol with semantic CFP formulation, progressive context disclosure, multi-round proposal revision, negotiation memory, and deterministic validation. Edge-cluster agents refine natural-language offloading proposals from local observations, predicted resource states, and qualitative runtime context, while hard resource and QoS constraints remain deterministic. Experiments derived from the Alibaba ASI Trace evaluate the extension at three levels: single- versus multi-round CNP, rule-based versus LLM-assisted refinement, and fixed-model single- versus multi-round negotiation. Under the evaluated configurations, MAS-DecStream reduces latency violations to 3\%, eliminates resource overcommitment, reaches a conflict-resolution rate of 0.91 with 20 agents, and improves utility by up to 22\% over the multi-round rule-based baseline. A separate 25-case evaluation shows model- and prompt-dependent accuracy--cost trade-offs. The results provide initial evidence that multi-round CNP refinement is the principal protocol-level gain, with LLM assistance adding value for qualitative and uncertain runtime context.
