A system for real-time predictive cloud orchestration with generative AI and microservices

Abstract

A system for real-time predictive cloud orchestration using generative AI and microservices, comprising: a data ingestion and monitoring module configured to collect and process real-time and historical data from the cloud infrastructure, services, and application layers; a generative AI-based prediction engine configured to analyze the data to predict workload requirements, resource usage trends, and performance bottlenecks; an intelligent orchestration planner configured to generate and optimize orchestration strategies based on predictive insights, system constraints, and policy requirements; a microservices-based execution and control layer configured to autonomously provision, scale, and manage cloud resources in real time; a feedback and optimization loop module configured to evaluate the performance of executed strategies and provide continuous learning updates to the prediction engine; and a policy management and governance interface configured to define, enforce, and audit orchestration rules and compliance parameters across multi-cloud environments.

Description

[0001] The present invention relates to cloud computing, in particular to systems for real-time orchestration of cloud resources. It utilizes generative AI models and a microservices architecture to predict workload demand and optimize cloud resource allocation. This invention enables intelligent, autonomous, and scalable cloud management.

[0002] In modern cloud computing environments, resource orchestration remains a complex and dynamic challenge. Traditional cloud orchestration tools are often reactive and rule-based, lacking the flexibility to respond in real time to fluctuating workloads or changing user demands. As a result, cloud systems frequently suffer from underutilization, overprovisioning, or latency issues, ultimately leading to increased operational costs and impaired user experience.

[0003] Furthermore, existing orchestration solutions typically lack predictive intelligence. They are unable to effectively anticipate future system states or dynamically adjust workloads before bottlenecks occur. This limitation becomes particularly critical in large, distributed, and multi-cloud environments, where latency, resiliency, and cost-effectiveness must be balanced across diverse services and infrastructure components. Without predictive insights, cloud operators struggle to maintain optimal system performance and reliability, especially during peak loads or unexpected workloads.

[0004] To address these challenges, there is a growing need for an intelligent and autonomous system that can predict future resource requirements and orchestrate cloud services in real time. By integrating generative AI with a microservices-based architecture, the proposed invention provides a novel solution that continuously learns from historical and real-time data to optimize resource allocation. This approach not only increases scalability and responsiveness but also reduces manual intervention, improves fault tolerance, and supports more sustainable and cost-effective cloud operations.

[0005] One goal of this disclosure is to enable proactive cloud resource allocation through AI-driven workload prediction.

[0006] Another goal of this disclosure is to leverage the microservices architecture for modularity, scalability, and fault isolation.

[0007] Another objective of this disclosure is to reduce latency and improve responsiveness through real-time orchestration.

[0008] Another goal of this disclosure is continuous adaptation through self-learning feedback loops.

[0009] Another objective of this disclosure is to ensure compliance with policies through integrated governance controls.

[0010] Another goal of this disclosure is to support hybrid and multi-cloud environments for flexible deployments.

[0011] Another objective of this disclosure is to minimize manual intervention and operational effort through automation.

[0012] Another objective of this disclosure is to improve the overall efficiency, reliability and cost-effectiveness of the cloud.

[0013] Further objects and advantages of the present disclosure will become apparent from the following description, which is not intended to limit the scope of the present disclosure.

[0014] The present invention relates to the system that collects real-time and historical data from cloud environments, applications and infrastructures to monitor performance and usage patterns.

[0015] Another embodiment of the present invention is that the generative AI engine predicts future load peaks, outages, and performance issues by analyzing data trends using advanced deep learning models.

[0016] Another embodiment of the present invention is that the intelligent orchestration planner formulates optimal resource deployment strategies based on AI predictions and organizational policies.

[0017] Another embodiment of the present invention is the microservices-based execution layer, which ensures agile, modular and event-driven control of the cloud infrastructure and enables rapid response to dynamic requirements.

[0018] Another embodiment of the present invention is that the feedback loop module continuously evaluates the orchestration results and refines the AI models, creating a self-learning and adaptive system.

[0019] Another embodiment of the present invention is the policy and governance interface, which allows administrators to set compliance rules, cost constraints, and manual override controls for orchestration plans.

[0020] Another embodiment of the present invention is that the system supports hybrid and multi-cloud architectures and ensures interoperability and resiliency across different cloud platforms.

[0021] Another embodiment of the present invention is that the invention enables autonomous, predictive, and policy-compliant cloud orchestration that reduces operational costs and increases reliability.

[0022] The present invention relates to a system for real-time predictive cloud orchestration using generative AI and microservices. It comprises a modular, scalable architecture that autonomously predicts, plans, and executes cloud orchestration strategies. The system consists of the following interconnected modules: Data acquisition and monitoring module:

[0023] This module continuously collects real-time operational data from various cloud infrastructure components, including compute instances, containers, storage, networks, and application performance metrics. It also collects metadata from deployment logs, telemetry systems, and API traffic. Through seamless integration with public and private cloud environments, the module ensures comprehensive visibility into system health and usage patterns. It leverages streaming technologies and APIs to support low-latency data ingestion while archiving historical data to support long-term pattern recognition. Generative AI-based prediction engine:

[0024] This module is the heart of the system and leverages extensive generative AI models trained on extensive datasets of cloud activity and orchestration logs. Using transformer-based architectures or other deep learning techniques, it generates predictive insights into utilization spikes, infrastructure bottlenecks, outage probabilities, and risks of performance degradation. The engine continuously refines its models using reinforcement learning and real-time feedback, enabling it to improve the accuracy of predictions of future states and proactively optimize orchestration decisions. Intelligent Orchestration Planner:

[0025] This module translates the prediction results of the KL engine into actionable orchestration strategies. It creates optimal deployment plans by analyzing service dependencies, available resources, and workload priorities. The planner uses constraint-resolving algorithms and rule-based logic to decide whether scaling up / down, migrating services, or reconfiguring resource allocations is necessary. It supports hybrid and multi-cloud environments and ensures that orchestration decisions align with policies related to cost, latency, compliance, and redundancy. Microservices execution and control layer:

[0026] This layer executes orchestration actions by interfacing with cloud management platforms and container orchestrators (e.g., Kubernetes, OpenShift). It comprises stateless, event-driven microservices responsible for resource provisioning, container scaling, service failover, and traffic routing. Each microservice is independently deployable, ensuring modularity, fault isolation, and easy updates. The layer uses secure APIs to control the cloud infrastructure and supports rollback mechanisms in case of failure or changing requirements. Feedback and optimization loop module:

[0027] This module continuously evaluates the impact of the executed orchestration strategies by analyzing updated telemetry data and user experience metrics. It provides real-time feedback to the generative AI-based prediction engine, enabling continuous learning and dynamic model recalibration. Furthermore, it identifies inefficiencies or misalignments in orchestration decisions and recommends optimizations. The feedback loop ensures the system's adaptability, resilience, and long-term performance improvement through self-tuning mechanisms. Policy management and governance interface:

[0028] This module provides a user-friendly dashboard for cloud administrators to define orchestration policies, compliance rules, cost constraints, and service-level objectives (SLOs). It includes access control, audit logging, and customizable orchestration scenario templates. Administrators can override or refine AI-generated plans as needed, and the system ensures that all actions comply with corporate policies and regulatory requirements. This module strengthens human oversight without compromising the efficiency of automation.

[0029] System operation begins with the data collection and monitoring module, which continuously collects real-time and historical data from the cloud infrastructure, application performance metrics, and deployment logs across multiple environments. This data is fed into the generative AI-based prediction engine, which analyzes patterns and makes predictions about future workload demands, system failures, and performance bottlenecks. These predictions are forwarded to the Intelligent Orchestration Planner, which formulates dynamic orchestration strategies such as scaling resources, load balancing, or initiating service migrations based on real-time requirements, policy constraints, and optimal resource utilization.The microservices execution and control layer implements these strategies by executing commands to provision or deprovision resources, redirect traffic, or reconfigure services through cloud-native APIs and orchestrators. At the same time, the feedback and optimization loop module evaluates the results of these actions and feeds the performance results back into the KL engine to enable continuous learning and model refinement, creating a closed-loop system of intelligent adaptation. Throughout the process, the policy management and governance interface ensures that all orchestration activities comply with corporate policies, security protocols, and compliance requirements, providing administrators with visibility, control, and override capabilities.This coherent operation enables the system to manage cloud resources autonomously and proactively in real time, improving efficiency, scalability, and resilience.

Claims

[1] A system for real-time predictive cloud orchestration using generative AI and microservices, comprising: a data ingestion and monitoring module configured to collect and process real-time and historical data from the cloud infrastructure, services, and application layers; a generative AI-based prediction engine configured to analyze the data to predict workload requirements, resource usage trends, and performance bottlenecks; an intelligent orchestration planner configured to generate and optimize orchestration strategies based on predictive insights, system constraints, and policy requirements; a microservices-based execution and control layer configured to autonomously provision, scale, and manage cloud resources in real time; a feedback and optimization loop module configured to evaluate the performance of executed strategies and provide continuous learning updates to the prediction engine; and a policy management and governance interface configured to define, enforce, and audit orchestration rules and compliance parameters across multi-cloud environments. [2] The system of claim 1, wherein the prediction engine uses transformer-based deep learning models trained on cloud workload telemetry and operational logs. [3] The system of claim 1, wherein the data entry module is integrated into hybrid and multi-cloud environments via secure APIs to ensure unified observability. [4] The system of claim 1, wherein the orchestration planner uses constraint-solving algorithms to optimize decisions for cost efficiency, latency reduction, and high availability. [5] The system of claim 1, wherein the execution layer consists of containerized, event-driven microservices that perform resource provisioning, load balancing, and service migration. [6] The system of claim 1, wherein the feedback loop module uses reinforcement learning techniques to dynamically improve the performance of the AI model and the accuracy of the orchestration. [7] The system of claim 1, wherein the policy interface includes a graphical dashboard for defining service level objectives, access roles, and compliance enforcement rules. [8] The system of claim 1, wherein the system automatically triggers rollback mechanisms when performance degradation, policy violations, or failed orchestration tasks are detected.