Self-Learning Recovery Framework for Datacenter Failure Response
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing datacenter recovery processes are often time-consuming and lack optimal planning, leading to significant business disruptions during failures or disruptions caused by natural calamities or vandalism.
Innovation Solution
A method for optimal service recovery involving a vendor recovery service that processes a production inventory file using learning models to generate a production recovery file, which is then transmitted to the client infrastructure for optimized recovery operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional datacenter recovery processes are used, then recovery operations can be performed, but the recovery process is time-consuming and lacks optimal planning
Solution Approach 1:
The system performs preliminary actions by generating recovery files and determining optimal recovery strategies before actual failures occur. The vendor recovery service processes production inventory files and creates predefined recovery plans that are stored and ready for immediate execution when failures happen, eliminating the need for time-consuming on-the-spot decision-making during actual recovery operations
Solution Approach 2:
The system enables self-service through automated recovery operations where the client recovery service automatically executes the optimal recovery strategy without requiring manual intervention. The monitored failure conditions trigger automated recovery processes that implement the pre-determined strategy, reducing both time loss and operational complexity
2Reliability
If manual recovery planning is used, then recovery strategies can be developed, but significant business disruptions occur during failures
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring production environments for failure conditions and using this information to trigger appropriate recovery actions. The vendor recovery service receives feedback from production inventory files and adjusts optimal recovery strategies based on monitored failure patterns, improving service continuity through data-driven decision-making
Solution Approach 2:
Optimal recovery strategies are determined and stored in advance before failures occur. The system prepares recovery files with predefined actions based on historical data and production inventory information, enabling immediate execution when failures happen and minimizing business disruption time while maintaining high service continuity
Data Source
AI summary
A method for optimal service recovery. The method includes: receiving, by a vendor recovery service, a production inventory file reflecting a current configuration of a client production environment of a client infrastructure; processing, by the vendor recovery service and using at least one learning model, a corpus of production inventory files including the production inventory file to obtain a production recovery file; transmitting, by the vendor recovery service, the production recovery file to the client infrastructure; receiving, by a client recovery service of the client infrastructure, the production recovery file for the client production environment; making a determination, by the client recovery service and based on a monitoring of the client production environment, that the client production environment is experiencing a failure; and performing, by the client recovery service and based on the determination, an optimized recovery of the client production environment according to the production recovery file.