Generative AI-supported prescriptive analysis system for optimizing data warehouse cluster nodes in cloud computing

Abstract

An AI-enhanced system for prescriptive analytics in cloud-based data warehouse cluster optimization, which includes the following: o a data input module(101) configured to collect real-time operational metrics, including resource utilization, query execution times and workload patterns, from a cloud-based data warehouse environment; ◯ a generative AI model layer(102) comprising at least one generative model wherein the generative model generates and simulates a variety of workload scenarios and corresponding cluster node configurations based on operational metrics; ◯ a prescriptive analysis module(103) that is functionally coupled with the generative AI model layer, wherein the prescriptive analysis module evaluates the simulated configurations to prescribe optimal resource allocation strategies based on predefined constraints including cost, performance benchmarks and resource availability; ◯ a control and execution module (104) configured to automatically implement the prescribed resource allocation strategies in the cloud environment using cloud service provider interfaces; and ◯ a performance monitoring module(105) configured to track the results of the implemented resource allocation strategies, compare them with expected benchmarks and feed the performance results back into the generative AI model layer for continuous improvement, with the system dynamically optimizing resource allocation, node scaling and workload distribution in real time to improve data repository performance and reduce operating costs.

Description

Field of the invention:

[0001] This invention relates to cloud computing and data warehouse management. More specifically, it relates to a system that uses generative AI for prescriptive analytics to optimize cluster node allocation, load balancing, and resource utilization in data warehouse clusters. Background of the invention

[0002] The rapid adoption of cloud-based infrastructures has revolutionized the way companies manage and process their data. Cloud data warehouses offer a scalable and flexible solution for storing, analyzing, and retrieving large amounts of data, making them a critical tool for data-driven decision-making. However, managing and optimizing these data warehouses presents significant challenges due to dynamic and unpredictable workloads, increasing query complexity, and varying resource requirements.

[0003] Current systems often rely on static rules and predictive analytics to allocate resources and manage cluster nodes. While predictive analytics can predict future trends, they often fail to adapt to sudden changes or unforeseen load spikes. Static heuristics and predefined thresholds used for cluster node allocation are inherently rigid, leading to either underprovisioning (causing performance bottlenecks) or overprovisioning (causing unnecessary costs).

[0004] Modern data warehouses face a variety of workload patterns, including real-time streaming data, periodic batch processing, and ad hoc analytical queries. Each of these workloads requires different compute resources, making it difficult for traditional optimization methods to ensure consistent performance. Furthermore, in cloud environments, resources are billed on a pay-as-you-go basis, so inefficient resource utilization directly impacts operating costs.

[0005] Generative AI has demonstrated remarkable capabilities in developing novel solutions to complex problems. Unlike traditional machine learning models, which rely on deterministic outcomes, generative AI models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) are capable of simulating various scenarios and deriving actionable insights. Applying these models to data warehouse optimization makes it possible to go beyond predictions and dynamically prescribe optimal configurations tailored to specific workloads.

[0006] Although prescriptive analytics has gained traction as a decision support tool, most existing systems are limited to simple decision-making based on static models or rule-based frameworks. These systems lack the ability to generate complex, adaptive solutions in real time. Integrating generative AI with prescriptive analytics represents a transformative approach that enables systems to learn from historical data, adapt to real-time changes, and prescribe resource optimization strategies that are both cost-effective and performance-oriented.

[0007] With the increasing reliance on cloud-based services, companies are looking for intelligent systems that can autonomously manage resources to achieve maximum efficiency. The growth of multi-cloud and hybrid cloud environments underscores the need for adaptive solutions that can operate seamlessly across different architectures while ensuring optimal resource allocation.

[0008] The invention bridges this gap by introducing a generative AI-powered prescriptive analytics system that not only predicts future workload patterns but also prescribes the best course of action for optimizing cluster node configurations. By leveraging advanced AI capabilities, the invention overcomes the limitations of conventional methods and provides a scalable, efficient, and cost-effective solution for managing data warehouse clusters in cloud environments. Summary of the invention

[0009] The present invention presents a generative AI-powered prescriptive analytics system designed to optimize cluster node configurations in cloud-based data warehouses. The invention leverages the capabilities of generative AI to analyze real-time data, simulate workload scenarios, and prescribe actionable solutions for efficient resource allocation and management.

[0010] At its core, the system integrates generative AI models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) with a robust prescriptive analytics engine to deliver intelligent recommendations. These recommendations address challenges such as workload distribution, query performance, and cost management in dynamic cloud environments.

[0011] The architecture of the invention includes key components, including: 1. Data Ingestion Layer: Continuously monitors and collects real-time metrics such as CPU usage, memory consumption, query execution times, and workload patterns. 2. Generative AI model layer: Simulates various operating scenarios and generates optimal cluster configurations. 3. Prescriptive Analytics Engine: Interprets AI-generated insights to recommend specific actions for resource allocation, workload balancing, and cluster node scaling. 4. Control and execution layer: Automates the implementation of recommendations using cloud service APIs for real-time adjustments. 5. Performance Monitoring Module: Tracks the results of implemented strategies and refines AI models through a feedback loop for continuous improvement.

[0012] The system dynamically adapts to fluctuating workloads, ensuring cost-efficient resource utilization and increasing the overall performance of cloud-based data warehouses. Its closed-loop feedback mechanism continuously improves the accuracy of AI-driven insights and enables effectiveness in changing operating environments.

[0013] This invention addresses the urgent need for intelligent, adaptive, and scalable solutions for resource optimization in cloud computing. By combining generative AI with prescriptive analytics, it offers a transformative approach to managing data warehouse clusters, delivering significant benefits in terms of performance, cost-effectiveness, and scalability. Short description of the drawing Fig. . shows a block diagram of the system according to the invention. Detailed description of the invention

[0014] The present invention provides a transformative approach to optimizing the configuration and management of cluster nodes in cloud-based data warehouses by integrating generative AI with prescriptive analytics. This innovative system addresses the challenges associated with dynamically varying workloads, inefficient resource allocation, and cost management, providing a comprehensive solution for intelligent, real-time cluster optimization.

[0015] At the heart of the invention is the synergy between advanced generative AI models and a robust prescriptive analytics engine. Generative AI models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) are used to simulate various workload scenarios and predict resource allocation needs. These simulations enable a detailed understanding of the impact of workload variations and query complexities on cluster performance. Unlike traditional predictive analytics, which focuses exclusively on predicting future states, the generative AI models in this invention actively generate possible configurations tailored to specific operational requirements.

[0016] The system works by first ingesting real-time data from the data warehouse environment. Metrics such as CPU utilization, memory consumption, query execution times, and workload distribution are captured via a dedicated data ingestion layer. This layer ensures a continuous stream of accurate and up-to-date information, which forms the basis for all subsequent analysis and decision-making processes.

[0017] The collected data is processed by the generative AI model layer, where it is analyzed and modeled to simulate potential utilization scenarios. These scenarios reflect the diverse conditions a cloud-based data warehouse may face, such as peak load periods, fluctuating query volumes, and sudden load spikes. By simulating these scenarios, the generative AI models generate several potential cluster configurations, each optimized for specific conditions.

[0018] The results of the generative AI models are fed into the prescriptive analytics engine, which evaluates the generated configurations against predefined constraints such as performance thresholds, resource availability, and cost budgets. This engine determines the most effective configuration and provides actionable recommendations for optimizing cluster nodes. The recommendations include strategies such as reallocating resources between nodes, scaling nodes up or down, rebalancing workloads, or adjusting query execution priorities to strike a balance between performance and cost.

[0019] To implement these recommendations, the system uses a control and execution layer that integrates seamlessly with cloud providers' APIs. This layer automates the application of recommended strategies and ensures real-time adjustments to the cluster configuration. For example, the system can add additional nodes during periods of high workload to maintain performance levels. Conversely, during periods of low activity, it can reduce resources to minimize costs, thus achieving an ideal balance between performance and efficiency.

[0020] A key component of the invention is the performance monitoring module, which evaluates the effectiveness of the applied configurations. This module tracks key performance indicators such as query latency and resource utilization and compares them with predefined benchmarks. The results are fed back into the generative AI models, creating a closed feedback loop that continuously improves the accuracy and adaptability of the system. Over time, this feedback mechanism enables the generative AI models to refine their simulations and recommendations to ensure the system can respond to evolving workloads and data patterns.

[0021] This invention is designed to operate in various cloud environments, including private, public, and hybrid clouds, making it extremely versatile. Its architecture supports various data warehouse platforms and meets the specific needs of industries such as finance, healthcare, e-commerce, and IT services. By optimizing cluster node configurations in real time, the system not only increases the performance of data warehouses but also reduces operating costs, making it a valuable tool for companies seeking to maximize the efficiency of their cloud infrastructure.

[0022] The functionality of the generative AI-powered prescriptive analytics system for optimizing data warehouse cluster nodes in cloud computing revolves around the seamless integration of data collection, generative AI-driven analysis, and the automatic implementation of optimization strategies. This system operates in a dynamic and continuous loop to ensure efficient resource utilization, cost minimization, and high performance.

[0023] The process begins with the data collection phase, in which the system collects real-time operational metrics from the cloud-based data warehouse environment. These metrics include CPU utilization, memory consumption, query execution times, workload patterns, and other resource usage statistics. This information is collected through monitoring agents or APIs integrated into the cloud infrastructure. The data ingestion layer ensures a constant stream of accurate and timely information, cap

[0024] Once the data is ingested, it is processed by the generative AI model layer. This layer is designed to simulate and analyze the dynamic nature of data warehouse workloads. Using advanced models such as generative adversarial networks (GANs) or variational autoencoders (VAEs), the system generates a spectrum of potential workload scenarios and corresponding cluster configurations. These simulations provide valuable insights into how different configurations might perform under different conditions. For example, the system can predict how increasing the size of a particular node or rebalancing queries might affect query latency or resource utilization during peak periods.

[0025] The results of the generative AI model layer are passed to the prescriptive analytics engine. This engine evaluates the simulated configurations against predefined constraints and objectives, such as meeting performance benchmarks, adhering to cost budgets, and ensuring availability during peak loads. Based on this assessment, the prescriptive analytics engine identifies the optimal configuration and suggests actionable strategies for implementation. These strategies include redistributing workloads across different nodes, dynamically scaling resources up or down, or adjusting query processing priorities to balance cost and performance.

[0026] The prescribed strategies are then executed via the system's control and execution layer. This layer interacts with the cloud provider's APIs to implement the recommended changes in real time. For example, if the system detects a potential performance bottleneck during a period of high demand, it can automatically increase the size of additional cluster nodes, allocate more memory, or distribute queries more effectively across the available nodes. Likewise, the system can pare down unnecessary resources during periods of low activity, thus minimizing operating costs without impacting performance.

[0027] An important aspect of the invention is the performance monitoring module, which continuously tracks the results of the implemented configurations. This module measures key performance indicators (KPIs) such as query latency, resource utilization, and overall system responsiveness. The monitored results are compared with expected benchmarks to evaluate the effectiveness of the optimizations.

[0028] The monitored data is then fed back into the generative AI models, creating a closed feedback loop. This feedback loop allows the system to refine its simulations and recommendations over time, improving its ability to adapt to new workloads, unexpected changes, or evolving operational requirements. Because the system continuously learns from the results of its decisions, it becomes increasingly accurate and efficient with extended use.

[0029] Overall, the invention functions as an intelligent, autonomous system that can dynamically adapt to the requirements of modern cloud-based data warehouses. Its ability to predict and prescribe optimal configurations in real time ensures that workloads are managed efficiently, resources are used effectively, and costs are kept under control. This continuous optimization process not only increases the performance of data warehouses but also provides a scalable and robust solution for managing complex cloud infrastructures across various industries.

[0030] In summary, the invention provides an intelligent and adaptable solution for managing data warehouse cluster nodes in cloud computing environments. Through the innovative use of generative AI and prescriptive analytics, it overcomes the limitations of conventional optimization methods and provides a dynamic, cost-effective, and scalable approach to resource management. This invention represents a significant advance in the field of cloud-based data warehousing and addresses the critical need for intelligent resource optimization in the age of Big Data. List of reference symbols 100 systems 101 Data transfer module 102 Generative AI model layer 103 Module for prescriptive analytics 104 Control and execution module 105 Performance Monitoring Module

Claims

[1] An AI-enhanced system for prescriptive analytics in cloud-based data warehouse cluster optimization, including: o a data input module (101) configured to collect real-time operational metrics, including resource utilization, query execution times, and workload patterns, from a cloud-based data warehouse environment; ◯ a generative AI model layer (102) comprising at least one generative model, wherein the generative model generates and simulates a plurality of workload scenarios and corresponding cluster node configurations based on the operational metrics; ◯ a prescriptive analysis module (103) operatively coupled to the generative AI model layer, wherein the prescriptive analysis module evaluates the simulated configurations to prescribe optimal resource allocation strategies based on predefined constraints including costs, performance benchmarks and resource availability; ◯ a control and execution module (104) configured to automatically implement the prescribed resource allocation strategies in the cloud environment using cloud service provider interfaces; and ◯ a performance monitoring module (105) configured to track the results of the implemented resource allocation strategies, compare them with expected benchmarks, and feed the performance results back to the generative AI model layer for continuous improvement, whereby the system dynamically optimizes resource allocation, node scaling, and workload distribution in real time to improve the performance of the data warehouse and reduce operating costs. [2] The system of claim 1, wherein the generative AI model layer uses generative adversarial networks (GANs) or variational autoencoders (VAEs) to simulate workload scenarios and generate adaptive resource optimization strategies. [3] The system of claim 1, wherein the data input module continuously monitors CPU usage, memory usage, query load, and latency metrics to provide real-time operational data. [4] The system of claim 1, wherein the prescriptive analysis module determines resource optimization strategies based on cost constraints, workload priorities, and performance thresholds specific to different operating conditions. [5] The system of claim 1, wherein the performance monitoring module uses machine learning techniques to analyze the efficiency of the implemented configurations and adaptively improve future optimization recommendations.

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