How to scale LLM multi-agent control in the cloud?
Cloud-Based Scheduling Mechanism for Scalable and Resource-Efficient Centralized Controllers
October 8, 2024
https://arxiv.org/pdf/2410.04920This paper proposes a system for controlling multiple robots (UAVs in this case) using a centralized AI in the cloud (specifically, a Nonlinear Model Predictive Controller or CNMPC). The key innovation is a scheduling mechanism that dynamically allocates cloud resources to CNMPCs based on the number of agents and their computational requirements. This addresses the scalability limitations of traditional centralized robot control by allowing the system to handle a varying and potentially large number of agents.
Relevant to LLM-based multi-agent systems, the paper highlights:
- Dynamic resource allocation: The proposed scheduler could be adapted to manage cloud resources for LLMs, scaling them up or down based on the number of agents or the complexity of their interactions.
- Centralized control with scalability: The system showcases the potential of maintaining centralized control over a large number of agents by leveraging cloud infrastructure, a relevant approach for LLM-based systems where centralized knowledge management can be beneficial.
- Real-time communication: The system's focus on low-latency communication between agents and the cloud emphasizes the importance of real-time data exchange in multi-agent systems, including those driven by LLMs.