Multi-System Storage Provisioning with Real-Time Anomaly Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional datacenter management and storage provisioning applications lack the capability to provision storage across multiple data storage systems in a datacenter, failing to provide efficient resource allocation and autoconfiguration.
Innovation Solution
A multi-system provisioning tool creates a feedback cycle using real-time usage statistics to monitor and analyze data, detecting anomalies and automatically adjusting storage configurations across multiple data storage systems by swapping resources and generating configuration scripts for individual systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional datacenter management applications are used to manage multiple data storage systems, then multiple systems can be monitored and managed, but storage provisioning capability is not provided
Solution Approach 1:
The patent combines the multi-system management capabilities of datacenter management applications with storage provisioning functionality into a unified system. The provisioning system integrates with the existing management framework to monitor, analyze, and automatically provision storage resources across multiple data storage systems, eliminating the need for separate provisioning tools for each system.
Solution Approach 2:
The management application is enhanced to provide universal storage provisioning capabilities that work across different data storage systems and configurations. The system can adapt to various storage array types, logical disk configurations, and workload requirements, providing a single tool that performs both management and provisioning functions.
2Productivity
If conventional storage provisioning applications are used to provision storage for a single data storage system, then storage can be provisioned based on usage profiles, but automatic provisioning for multiple systems in a datacenter cannot be performed
Solution Approach 1:
The system implements continuous monitoring of storage usage across multiple data storage systems, comparing actual usage against defined usage profiles and thresholds. When storage resources are depleted or anomalies are detected, the system automatically triggers provisioning actions by allocating storage from other systems in the datacenter, creating a closed-loop feedback mechanism that enables autonomous multi-system provisioning.
Solution Approach 2:
The provisioning system operates autonomously by automatically detecting storage needs, analyzing usage patterns, and reallocating storage resources across multiple systems without requiring manual administrator intervention for each provisioning decision. The system self-manages the entire provisioning workflow from monitoring to execution.
3Ease of operation
If manual storage provisioning is performed for each data storage system, then detailed control over each system is maintained, but efficient resource allocation and autoconfiguration cannot be achieved
Solution Approach 1:
The system pre-configures usage profiles, storage thresholds, and provisioning policies across multiple data storage systems before actual storage needs arise. By establishing these parameters in advance, the system can immediately execute automated provisioning actions when storage depletion is detected, eliminating the need for manual configuration during time-critical provisioning events.
Data Source
AI summary
Improved techniques involve creating a feedback cycle between a framework for managing many data storage arrays and system for provisioning a data storage array using real-time usage statistics from the framework. Along these lines, a multi-system provisioning tool provides an initial configuration of storage systems in a datacenter; such an initial configuration includes provisioning of logical disks for each system. As this tool monitors activity within the datacenter, it generates live runtime data for each logical disk within each system. The tool then compares this live runtime data to external runtime data received from a central database and looks for anomalies in the live runtime data. Upon detecting an anomaly in a logical disk, the tool may respond by finding logical disks in other storage arrays from which storage resources may be swapped.