@article{Eshankulov2026, 
author = {Khamza Eshankulov and Bahodir Muminov and Robiya Farmonova and Dilnavoz Sodikova and Bakhriddin Bozorov and Zavqiddin Temirov and Rashid Nasimov},
title = {Adaptive Pareto-Based Multi-Agent Decision Model for Resource Management in Cloud Business Intelligence Systems},
year = {2026},
journal = {Computers, Materials & Continua},
volume = {88},
number = {3},
pages = {94},
keywords = {Cloud resource allocation, pareto decision making, decentralized coordination, business intelligence microservices, adaptive multi-agent systems},
url = {https://www.sciopen.com/article/10.32604/cmc.2026.081803},
doi = {10.32604/cmc.2026.081803},
abstract = {Cloud-based Business Intelligence (BI) systems operate under highly dynamic analytical workloads, including bursty OLAP queries, concurrent aggregations, and real-time microservice interactions, where static resource allocation leads to latency spikes and inefficient resource utilization. This paper proposes a decentralized adaptive Pareto-based multi-agent decision model for real-time resource coordination in cloud BI microservice environments. The agent placement problem is formulated as a multi-criteria decision process that minimizes service response latency, improves computational resource utilization, and preserves Quality-of-Service (QoS) stability. Instead of constructing a centralized global optimization policy, the proposed framework relies on decentralized locally Pareto-efficient decisions combined with adaptive priority regulation driven by QoS deviation. The approach is evaluated through large-scale controlled simulation and validated in a Kubernetes-based pilot cloud environment. Experimental results demonstrate up to 54% latency reduction compared to static allocation and 22% improvement over GA-based optimization, with enhanced CPU utilization balance under dynamic workloads. Statistical analysis confirms the significance of improvements (p < 0.05). The proposed model ensures bounded monotonic decision transitions without centralized orchestration or predictive training, making it suitable for real-time cloud-native BI service ecosystems.}
}