MUMBAI, India, Sept. 14 -- Intellectual Property India has published a patent application (202641105554 A) filed by Lavan Kumar Muppalla; and Dileep Kumar Somajohassula on September 02, 2026, for Ai-Driven Workload Forecasting And Api-Led Infrastructure Orchestration System For Sustainable Cloud Resource Management, And Method Thereof.
Inventors include Lavan Kumar Muppalla; and Dileep Kumar Somajohassula.
The application for the patent was published on September 11, 2026, under issue no. 37/2026.
Abstract: Disclosed is an AI-driven cloud orchestration system (300) comprising a telemetry acquisition unit (302) configured to continuously capture resource utilization telemetry, including CPU, memory, and storage I/O metrics, together with API invocation metrics comprising latency, throughput, call volume, and error rate, across distributed cloud and edge computing nodes. The system further includes an orchestration controller (304) communicatively coupled with the telemetry acquisition unit (302) and configured to forecast future workload demand using a trained temporal neural network operating on the captured telemetry, generate resource provisioning decisions for virtual machine and container instances based on the forecasted demand, manage service-to-service integration and data transformation across heterogeneous backend systems via a layered, reusable application programming interface architecture, generate and apply infrastructure-as-code templates to provision computing resources in accordance with the provisioning decisions, and coordinate deployment of the provisioned resources through an automated build-test- deployment pipeline synchronized with the provisioning decisions. The orchestration controller (304) further computes an energy-cost-aware placement score for each provisioning decision and consolidates or migrates workloads to reduce power consumption while maintaining a defined service-level threshold. An output unit (306) generates dashboards presenting forecasted demand, provisioning decisions, deployment status, and energy optimization outcomes. The present disclosure also relates to a corresponding method (400) for AI-driven, API-led, sustainability-optimized cloud workload orchestration.
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