Multi-Agent AI Systems for Autonomous Resource Allocation in Cloud Automation
Abstract
Resource allocation in cloud environments is a complex and dynamic task requiring real-time decision-making. This paper proposes a multi-agent AI system that autonomously manages resource provisioning, load balancing, and cost optimization in cloud infrastructure. Using deep learning and distributed agent communication protocols, the system ensures efficient allocation based on workload patterns and service-level agreements (SLAs). Experimental results showcase a reduction in operational costs by 18% and improved resource utilization compared to static automation models.
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