Optimizing Decision Systems through Predictive Analytics for Enterprise Intelligent Cloud Platforms and API Management
DOI:
https://doi.org/10.15680/IJCTECE.2024.0706031Keywords:
Decision Support Systems, Predictive Analytics, API Management, Cloud Computing, Intelligent Architecture, Traffic OrchestrationAbstract
Modern corporate environments rely on complex, cloud-native infrastructures where localized information delays can compromise competitive advantage. This paper introduces an optimized, multi-layered architectural model for Decision Support Systems (DSS) embedded within Enterprise Intelligent Cloud Platforms, coordinated through advanced Application Programming Interface (API) management layers. Traditional enterprise decision engines frequently operate on rigid, retrospective batch processing, failing to proactively respond to erratic demand spikes, localized service degradations, and backend hardware faults. By synthesizing real-time predictive analytics with elastic cloud resources, the proposed framework transitions enterprise decision systems into proactive operational systems. The intelligence layer leverages specialized gradient-boosted trees and deep sequence-to-sequence neural network architectures to continuous analyze multi-variant telemetry data, forecasting resource constraints and transaction volumes before they disrupt operations. Operating as the essential communication system, an optimized API management structure uses predictive inferences to adjust traffic routing, dynamically update web application firewall policies, and manage rate-limiting rules. The result is a highly adaptive, resilient cloud ecosystem that minimizes human operational overhead, ensures continuous system availability, and increases institutional decision-making velocities across highly distributed global enterprise networks.
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