Multi-Agent Systems for Autonomous Orchestration in AI-Driven Computing Networks
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V3I4P102Keywords:
Multi-Agent Systems, Autonomous Orchestration, Edge–Cloud Computing, Multi-Agent Reinforcement Learning, Distributed Optimization, Intent-Based Networking, SLA-Aware Scheduling, Digital Twins, Self-Healing, Federated Coordination, Zero-Trust Security, ExplainabilityAbstract
AI-driven computing networks spanning edge, fog, and cloud demand real-time coordination under volatile workloads, heterogeneous resources, and strict service-level objectives. This paper proposes a multi-agent systems (MAS) architecture for autonomous orchestration that couples decentralized decision-making with global policy compliance. Specialized agents scheduler, scaler, placement, data, and security sentinels negotiate via market-based mechanisms and cooperative game-theoretic protocols to allocate compute, memory, and bandwidth while respecting latency budgets and energy caps. Learning-enabled controllers combine model-predictive scheduling with multi-agent reinforcement learning to adapt to demand surges, drift, and failures; safety layers constrain exploration through formal guards and intent-based policies. To improve robustness, agents share compact state via a publish–subscribe control plane and use digital-twin simulations for counterfactual rollouts before enacting changes in production. The design supports privacy-preserving analytics with federated coordination at the edge and employs trust scoring and zero-trust enforcement to mitigate adversarial behavior and misconfigurations. We present a reference implementation with pluggable observability hooks and outline evaluation metrics for tail latency, SLA adherence, energy per inference, recovery time, and orchestration overhead. Results demonstrate that MAS-based orchestration can reduce p95 latency and policy-violation rates while improving resource utilization and fault tolerance, suggesting a practical path to self-optimizing, self-healing AI infrastructure across heterogeneous, multi-tenant environments
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