AI-driven adaptive microservices system for dynamic demand prediction and automatic scaling on retail marketplaces

Abstract

AI-driven adaptive microservices system (100) for dynamic demand prediction and automatic scaling on retail marketplaces, comprising: a data collection module configured to collect real-time and historical retail transaction data, user behavior patterns, and system performance metrics; a machine learning-based demand forecasting system that analyzes the collected data to predict future demand fluctuations; an auto-scaling module that dynamically adjusts computing resources by scaling microservices based on predicted demand fluctuations; a real-time monitoring system to monitor platform traffic, workload distribution and system health to ensure optimal resource utilization; a microservices management layer that enables independent scaling, deployment, and maintenance of various system components; a security and compliance module that detects anomalies and ensures compliance with data protection regulations; a cloud integration interface for seamless connection with retail marketplaces and cloud service providers.

Description

The present invention relates to an AI-controlled adaptive microservices system developed for dynamic demand prediction and automatic scaling at retail market locations.Retail market places are subject to highly dynamic demand variations that are affected by various factors, including seasonal trends, advertising events, consumer behavior, and market conditions. Conventional scaling approaches for retail platforms often rely on predefined thresholds or manual interventions, which may result in inefficiencies such as resource over-coverage or service interruptions due to under-coverage. Existing automatic scaling solutions are primarily rule-based and cannot be matched in real time to unpredictable demand spikes or gradual demand shifts. Moreover, conventional monolithic architectures often have problems with scalability because they require system-wide updates rather than component-specific optimizations.The introduction of the microservices architecture has enabled a modular approach to system scalability. However, most implementations lack intelligent automation that can dynamically predict demand patterns and proactively allocate resources. Without AI-driven findings, these systems either respond too slowly to demand changes or inefficiently allocate resources, leading to higher operating costs and poorer user experiences. To address these challenges, an AI-controlled adaptive microservices system that couples advanced models to predict demand with automatic scaling mechanisms is needed. Such a system should be able to analyze consumer trends in real time, predictively manage workload, and seamless optimization of resources to improve the agility and cost efficiency of retail market locations.To solve the problem, the present invention provides an AI-controlled adaptive microservices system for dynamic demand prediction and automatic scaling in retail market places.The system aims to provide an AI-controlled adaptive microservices framework that improves the efficiency, scalability, and responsiveness of retail market locations.The system utilizes machine learning algorithms to analyze historical and real-time data, thus allowing accurate prediction of demand variations in retail market locations.The system intelligently adjusts computing resources by automatically scaling microservices up or down based on the predicted demand, thus ensuring optimal performance while minimizing cost.The system prevents resource congestion at low demand times and ensures adequate resource availability at high demand times, resulting in significant cost savings.The system ensures fail-safe by effectively distributing workloads and providing fail-safe mechanisms for maintaining seamless operation.The system includes AI-controlled anomaly detection to detect potential security threats and ensures compliance with privacy regulations for retail transactions.In one embodiment, the present invention introduces an AI-controlled adaptive microservices system that is intended to improve the scalability, efficiency, and responsiveness of retail market locations. The system utilizes machine learning based models for demand prediction to analyze real-time and historical data to enable proactive and intelligent resource allocation. By integrating a cloud-native microservices architecture, the invention ensures modular, scalable and efficient system performance while minimizing operating costs. An essential feature of the system is its automatic resource scaling mechanism that dynamically adjusts the computing resources to the expected demand variations. Conventional scaling methods rely on static rules or manual interventions, which often leads to inefficiencies. In contrast, this system continuously monitors consumer trends, traffic patterns, and workload fluctuations to make scaling decisions in real time to ensure high availability and optimum performance.The AI-controlled system employs predictive analyses, self-learning algorithms, and real-time monitoring to improve compliance. It optimizes resource allocation at high traffic times to avoid system bottleneck and saves computing resources at low demand times to reduce cost. Moreover, the microservices-based architecture enables independent provisioning, scaling, and maintenance of various components of the retail platform, which increases operational flexibility and fault tolerance. In addition, the system includes AI-based anomaly detection to detect and mitigate potential safety threats or performance issues. It also ensures compliance with data protection regulations and is therefore suitable for large retail establishments. By seamless integration into existing e-commerce platforms and cloud infrastructures, the invention improves the agility, cost efficiency, and user-friendliness of retail market locations.The invention is explained again below with reference to the figure. The following shows: FIG. 1 : shows an AI-controlled system for optimizing the database performance ( 100), which uses prometheus for real-time observation.FIG. 1 shows an AI-controlled system for optimizing database performance ( 100) that uses prometheus for real-time observation. The system comprises an AI-controlled framework for performance optimization of databases, in which prometheus is integrated for observation and monitoring in real time. The system includes a data acquisition module that continuously acquires important performance metrics such as query execution times, resource usage, memory consumption, and I / O operations. The system processes these metrics using a machine learning based optimization engine that identifies inefficiencies, predicts performance bottleneck, and proposes real-time adjustments to improve database efficiency. The system includes an AI-controlled query optimizer that dynamically refines SQL queries, indices, and execution plans to minimize latency and improve throughput. The system uses workload and historical query pattern analysis to recommend indexing strategies, caching mechanisms, and database partitioning techniques. The system also has a self-optimization mechanism that automatically adjusts database parameters such as buffer sizes, connection pooling, and query scheduling based on workload fluctuations and real-time performance feedback.The system provides proactive anomaly detection by AI-based predictive analyses that detect deviations from normal performance patterns. The system generates warnings and recommendations in real time when abnormality is detected, thus preventing potential failures or decelerations. The system also includes a resource allocation smart module that optimizes CPU, memory, and disk usage by dynamically scaling the resources based on the current and expected database load. The system provides a central dashboard that visualizes insight into database performance in real time, detected anomalies, and AI-controlled optimization measures. The system allows database administrators to monitor system status, review performance trends, and implement recommended optimizations with minimal manual effort. The system supports seamless integration with various database management systems (DBMS) and cloud platforms, making it a scalable and adaptable solution for enterprise applications. The system automates database monitoring, optimization, and matching, thereby significantly reducing operating costs, minimizing downtime, and overall improving database efficiency. The system provides optimal resource utilization, improved polling performance, and high availability, and is thus a valuable tool for companies managing large database environments.List of reference characters100 System

Claims

A AI-controlled adaptive microservices system (100) for dynamic demand prediction and automatic scaling in retail market places, comprising: a data acquisition module configured to acquire real-time and historical retail transaction data, user behavior patterns, and system performance metrics; a machine learning based demand prediction system that analyzes the collected data to predict future demand fluctuations; an automatic scaling module that dynamically adjusts the compute resources by scaling the microservices based on the predicted demand fluctuations; a real-time monitoring system for monitoring platform traffic, workload distribution, and system state to ensure optimal resource utilization; a microservice management layer that enables independent scaling, provisioning, and maintenance of various system components; a security and compliance module that detects anomalies and ensures compliance with privacy regulations; a cloud integration interface for seamless connection to retail market locations and cloud service providers.The system of claim 1, wherein the machine learning-based demand prediction engine uses deep learning algorithms including recurrent neural networks (RNN) or long-term memory (LSTM) models to improve prediction accuracy.The system of claim 1, wherein the automatic scaling module supports both horizontal and vertical scaling and enables the provision of microservices in containers.The system of claim 1, wherein the real-time monitoring system comprises AI-controlled anomaly detection to prevent system failures and security violations.The system of claim 1, wherein the microservices management layer enables independent provisioning and rollback of system components without affecting the entire retail platform.The system of claim 1, wherein the security and compliance module implements encryption, access control policies, and policy compliance frameworks to protect sensitive retail data.

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