An AI-driven cloud infrastructure for automatic scaling and resource optimization

Abstract

An AI-driven cloud infrastructure system for automatic scaling and resource optimization (100) comprising: an input module configured to collect real-time data on resource usage, workload characteristics, and application performance metrics; a predictive analytics module comprising artificial intelligence and machine learning algorithms configured to predict future resource requirements based on historical data and real-time monitoring; a decision-making module configured to dynamically allocate cloud resources based on multidimensional metrics, including CPU utilization, memory usage, network latency, and memory input / output (I / O); and a scaling module configured to scale resources up or down in real time based on decisions made by the decision-making module.a cost optimization module configured to integrate cost-saving strategies, including the use of spot instances, reserved instances, and hybrid cloud models; a workload distribution module configured to dynamically distribute workloads across multiple regions or cloud providers to reduce latency and improve performance; a feedback module configured to analyze real-time and historical performance data to refine scaling and optimization decisions over time; and a monitoring and control interface configured to provide users with real-time insights into resource utilization, cost metrics, and application performance.

Description

The present invention relates to the field of cloud computing and artificial intelligence (AI), and more particularly to an AI-controlled system for automatic scaling and resource optimization of the cloud infrastructure.The emerging cloud computing has allowed companies to dynamically implement and scale their applications, thereby eliminating the need for a bulky infrastructure on site. However, conventional automatic scaling systems and methods for resource optimization suffer from significant restrictions that impair their effectiveness. Conventional systems rely largely on reactive scaling mechanisms that trigger resource allocation only when an increase or decrease in demand is detected. This delayed response often results in performance degradations, service interruptions or resource congestion. Moreover, these systems lack robust prediction capabilities because they rely on simplifying algorithms such as fixed patterns or moving averages that cannot account for complex loading dynamics, seasonal fluctuations, or unexpected peaks, resulting in suboptimal resource allocation.Another critical drawback of conventional methods is the tendency to oversupply resources to avoid possible performance problems. This approach results in high operating costs, since unused resources remain unused and do not contribute any value. Conversely, these systems often fail to efficiently reduce resources outside peak times, resulting in underutilization and increased energy consumption, which affects both cost and environmental compatibility. Moreover, conventional systems are rigid in workload distribution and do not have flexibility to dynamically optimize workloads across multiple regions or cloud providers, thereby increasing latency and limiting performance.Additionally, existing systems lack contextual arbitration capabilities because they do not consider multi-dimensional metrics such as network latency, memory I / O, or application specific requirements. This generalized approach is poorly suited for different and complex workloads. Moreover, they integrate only insufficiently cost-saving strategies such as the use of spot or reserved instances, hybrid cloud models or workload scheduling, and thus miss possibilities for cost optimization. Finally, conventional systems have deficiencies in adaptive learning because they do not use historical data or real-time feedback to refine their decisions for automatic scaling, resulting in repeated inefficiencies in similar scenarios.These constraints highlight the need for an advanced smart solution that allows proactive scaling and optimization. The present invention addresses these challenges by utilizing AI and machine learning to provide a comprehensive system that predicts future resource requirements, dynamically optimizes allocation, and ensures cost effective and efficient management of cloud infrastructure resources.To solve this problem, the present invention provides an AI-controlled system for automatic scaling and resource optimization of the cloud infrastructure.The AI-controlled system for automatic scaling and resource optimization of the cloud infrastructure enables the cloud resources to be scaled in real time and with predictive character, thus ensuring optimum performance in both peak and light load times.The AI-controlled system for automatic cloud infrastructure scaling and resource optimization may minimize operating costs by preventing oversupply, optimizing resource usage, and integrating cost-saving mechanisms such as spot instances, reserved instances, and workload scheduling.The AI-controlled system for automatic cloud infrastructure scaling and resource optimization may utilize AI and machine learning algorithms to predict future resource demand based on historical data, real-time monitoring, and workload patterns, thus ensuring proactive resource management.The AI-driven cloud infrastructure automatic scaling and resource optimization system may incorporate multi-dimensional metrics such as CPU and memory utilization, network latency, memory I / O, and application specific requirements for intelligent decision making and resource allocation.The AI-controlled system for automatic cloud infrastructure scaling and resource optimization may dynamically optimize workload distribution among multiple regions or cloud providers, thereby reducing latencies, increasing performance, and improving fault tolerance.The AI-controlled system for automatic scaling and resource optimization of the cloud infrastructure provides continuous monitoring of resource usage, application performance, and cost metrics, as well as usable awareness to administrators via an intuitive user dashboard.The AI-controlled system for automatic scaling and resource optimization of the cloud infrastructure may provide efficient resource utilization by reducing resources at low demand times, lowering energy consumption, and promoting ecological sustainability.The AI-controlled system for automatic scaling and resource optimization of the cloud infrastructure may provide efficient resource utilization by reducing resources at low demand times, lowering energy consumption, and promoting ecological sustainability.The AI-controlled system for automatic scaling and resource optimization of the cloud infrastructure may utilize real-time feedback and historical data to improve the accuracy and efficiency of scaling decisions over time.The AI-controlled system for automatic scaling and resource optimization of the cloud infrastructure is compatible with various cloud platforms such as AWS, Azure, and Google Cloud, and enables seamless integration and operation in different environments.The AI-controlled system for automatic scaling and resource optimization of the cloud infrastructure may provide high availability and reliability by implementing smart strategies to mitigate system failures and interruptions.In one embodiment, an AI-controlled system for automatic scaling and resource optimization of the cloud infrastructure is provided. The AI-controlled cloud infrastructure automatic scaling and resource optimization system utilizes artificial intelligence (AI) and machine learning (ML) to dynamically manage cloud resources and to address the limitations of conventional methods such as oversupply, underutilization, and delayed response times. The system has an input module that collects real-time data for resource usage, workload characteristics, and application performance that is processed by a predictive analysis module using advanced ML algorithms, including time-series predictions, to predict future workload requirements. A decision making module analyzes multidimensional metrics such as CPU load, memory usage, memory I / O, and network latency to intelligently allocate resources, while a scaling module implements proactive scaling mechanisms to dynamically adapt resources in real-time. The cost optimization module minimizes operating costs by integrating strategies such as spot and reserved instances and workload scheduling, while the workload distribution module improves latency, fault tolerance, and availability through dynamic reallocation of workloads across regions or cloud providers. Moreover, a feedback module refines the scaling decisions by reinforcement learning, thus providing continuous performance improvement, and a user-friendly monitoring interface provides real-time insights into resource usage, cost metrics, and application performance.The invention is explained again below with reference to the figure. The following shows: FIG. 1 : shows an AI-controlled system for automatic scaling and resource optimization of the cloud infrastructureFIG. 1 shows an AI-controlled system for automatic scaling and resource optimization of a cloud infrastructure. The system (100) for automatic scaling and resource optimization of an AI-controlled cloud infrastructure comprises an input module, a predictive analysis module, a decision making module, a scaling module, a cost optimizing module, a workload distribution module, a feedback module, and a monitoring and control interface. The input module is configured to acquire real-time data about resource usage, workload characteristics, and application performance metrics. The predictive analysis module includes artificial intelligence and machine learning algorithms configured to predict future resource demand based on historical data and real-time monitoring. The decision making module is configured to dynamically allocate cloud resources based on multi-dimensional metrics including CPU utilization, memory usage, network latency, and memory input / output (I / O). The scaling module is configured to scale the resources up or down in real time based on decisions made by the decision making module. The cost optimization module is configured to integrate cost-saving strategies including use of spot instances, reserved instances, and hybrid cloud models. The workload distribution module is configured to dynamically distribute workloads across multiple regions or cloud providers to reduce latencies and improve performance. The feedback module is configured to analyze real-time and historical performance data to refine scaling and optimization decisions over time. The monitoring and control interface is configured to provide users with real-time insights into resource usage, cost metrics, and application performance. The predictive analysis module uses machine learning models trained based on time-series data and workload patterns to predict workload peaks and valleys. The decision making module takes into account contextual parameters such as application specific requirements, fault tolerance, and Quality of Service (QoS) thresholds to optimize resource allocation. The scaling module is configured to use proactive scaling policies to prevent performance degradation by predicting spikes and pre-allocating resources. The cost optimization module integrates policies to minimize resource waste by automatically shutting down resources in light load times and ensuring optimal usage. The workload distribution module is configured to dynamically redistribute workloads to lower cost, better performance, or lower latency regions or cloud providers based on real-time metrics. The feedback module uses reinforcement learning techniques to improve the accuracy of the decisions for resource scaling and optimization. The monitoring and control interface provides warnings and recommendations for further optimizations based on resource utilization and cost trends. The fault tolerance mechanism is configured to ensure high availability and reliability by redistributing workloads in the event of hardware or network failures. The predictive analysis module integrates seasonal and regional load fluctuations into its predictive models to improve the accuracy of resource scaling.List of reference characters100 System

Claims

A AI-controlled cloud infrastructure automatic scaling and resource optimization system (100) comprising: an input module configured to acquire real-time data on resource usage, workload characteristics, and application performance metrics; a predictive analysis module comprising artificial intelligence and machine learning algorithms configured to predict future resource demand based on historical data and real-time monitoring; a decision making module configured to dynamically allocate cloud resources based on multi-dimensional metrics including CPU load, memory usage, network latency, and memory input / output (I / O); a scaling module configured to scale resources up or down in real time based on decisions made by the decision making module; a cost optimization module configured to integrate cost saving strategies including spot instances, reserved instances, and hybrid cloud models usage; a workload distribution module configured to dynamically distribute workloads across multiple regions or cloud providers to reduce latency and improve performance; a feedback module configured to analyze real-time and historical performance data to refine scaling and optimization decisions over time; and a monitoring and control interface configured to provide users with real-time insights into resource usage, cost metrics and application performance.The AI-controlled cloud infrastructure automatic scaling and resource optimization system (100) of claim 1, wherein the predictive analysis module uses machine learning models trained on time-series data and workload patterns to predict workload peaks and valleys.The AI-controlled system (100) for automatic scaling and resource optimization of the cloud infrastructure of claim 1, wherein the decision making module incorporates contextual parameters including application specific requirements, fault tolerance, and quality of service (QoS) thresholds for optimized resource allocation.The AI-controlled system (100) for automatic scaling and resource optimization of the cloud infrastructure of claim 1, wherein the scaling module is configured to use proactive scaling policies to prevent performance degradation by predicting and pre-allocating resources prior to demand spikes.The AI-controlled cloud infrastructure automatic scaling and resource optimization system (100) of claim 1, wherein the cost optimization module integrates policies to minimize resource waste by automatically reducing resources during light load times and to ensure optimal usage.The AI-controlled system (100) for automatic scaling and resource optimization of the cloud infrastructure of claim 1, wherein the workload distribution module is configured to dynamically redistribute workloads to lower cost, better performance, or lower latency regions or cloud providers based on real-time metrics.The AI-controlled system (100) for automatic scaling of cloud infrastructures and resource optimization of claim 1, wherein the feedback module employs reinforcement learning techniques to improve the accuracy of decisions for scaling and resource optimization.The AI-controlled system (100) for automatic scaling and resource optimization of the cloud infrastructure of claim 1, wherein the monitoring and control interface provides alerts and recommendations for the further optimization based on resource usage and cost trends.The AI-controlled system (100) for automatic scaling and resource optimization of the cloud infrastructure of claim 1, wherein the fault tolerance mechanism is configured to provide high availability and reliability by redistribution of workloads in the event of hardware or network failures.The AI-controlled system (100) for automatic cloud infrastructure scaling and resource optimization of claim 1, wherein the predictive analysis module integrates seasonal and regional workload fluctuations into its prediction models to improve resource scaling accuracy.

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