Cloud Data Cluster Optimization for Automated Cost Control
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Solution Overview
Problem
Organizations face significant challenges in optimizing cloud resource utilization, particularly in multi-cloud environments, leading to substantial overspending and inefficient use of cloud services, especially when processing large volumes of data.
Innovation Solution
A method and system that utilize a web crawler to collect usage metrics for each data cluster, determine justifications based on predefined thresholds through APIs, generate recommendations for optimal usage, and execute these recommendations via APIs or configuration files to optimize resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If organizations increase cloud service adoption for scalability and flexibility, then processing capability is improved, but cloud spending increases
Solution Approach 1:
The system implements continuous monitoring of cluster usage metrics and provides feedback through automated reports and notifications. The feedback loop tracks resource utilization patterns, identifies optimization opportunities, and verifies the impact of applied recommendations, enabling organizations to adjust their cloud resource allocation strategies based on actual usage data.
Solution Approach 2:
The system enables self-service automation by automatically discovering underutilized clusters, analyzing their usage patterns, generating optimization recommendations, and executing reconfiguration actions without requiring manual intervention. The automated web crawler and analysis engine perform self-directed optimization tasks, reducing the need for manual cloud cost management.
2Adaptability or versatility
If organizations deploy multiple data clusters for different projects, then project flexibility is improved, but resource utilization efficiency deteriorates
Solution Approach 1:
The system applies universal optimization algorithms that can analyze and recommend improvements across diverse cluster types and workloads. The automated analysis engine universally identifies underutilization patterns regardless of the specific project or cluster configuration, enabling consistent resource optimization across multiple projects while maintaining their individual functional requirements.
Solution Approach 2:
The system optimizes resource utilization by changing key parameters such as cluster size, instance types, and configuration settings. By automatically adjusting these parameters based on actual usage metrics, the system maintains project flexibility while improving overall resource efficiency across the multi-cluster environment.
3Loss of energy
If manual monitoring and optimization of cloud resources is performed, then cost control effort is improved, but time consumption increases
Solution Approach 1:
The system replaces manual mechanical monitoring and analysis processes with automated computational systems. The web crawler automatically discovers clusters, the analysis engine automatically evaluates usage metrics, and the recommendation system automatically generates optimization actions, substituting human manual effort with automated technological processes.
Solution Approach 2:
The system introduces an intermediary automated analysis layer between raw cloud usage data and cost optimization decisions. This intermediary automatically processes usage metrics, identifies optimization opportunities, and translates them into actionable recommendations, eliminating the need for direct manual monitoring while maintaining effective cost control.
Data Source
AI summary
A method and system for optimizing resource utilization in cloud-based data processing platforms is disclosed. A set of usage metrics of the data cluster is received for each cluster of a plurality of data clusters using a web crawler. A justification based on a predefined threshold is determined corresponding to each of the set of usage metrics of the data cluster through one or more application programming interfaces (APIs). One or more recommendations are generated based on the justification for optimal usage of the data cluster through the one or more APIs. The one or more recommendations are executed through at least one of an associated configuration file or the one or more APIs.