Batch Job Failover Management Using Matrix-Based Recovery
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Solution Overview
Problem
Current batch job failover management in cloud computing environments is inefficient, as it typically requires a rollback to the beginning of all batch jobs upon interruption, leading to time and resource wastage due to lack of fine-grained identification of which jobs or processing actions need to be re-run, resulting in incomplete and resource-intensive re-execution of the entire set.
Innovation Solution
The solution involves generating a running result matrix and internal/external dependency matrices to calculate a recovery matrix, which identifies specific processing actions within batch jobs that need re-execution, allowing for targeted failover management and minimizing redundant processing by executing only necessary actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If rollback to the beginning of all batch jobs is performed upon interruption, then failover management is simplified, but time and resources are wasted due to re-execution of entire job sets
Solution Approach 1:
The patent segments the batch job execution into individual processing actions and tracks their execution status using matrices. When a failure occurs, only the specific processing actions that failed are identified and re-executed, rather than rolling back the entire batch job set. This segmentation enables precise failover management at the processing action level.
Solution Approach 2:
The patent performs preliminary actions by generating execution matrices and dependency matrices before failover occurs. These matrices capture the execution status and dependencies of all processing actions, enabling rapid identification of failed actions and their dependencies without requiring full job re-execution during actual failover events.
2Reliability
If rollback to the beginning of all batch jobs is performed upon interruption, then completeness of job recovery is ensured, but resource usage increases due to redundant processing
Solution Approach 1:
The patent divides the batch job into discrete processing actions and uses execution matrices to track which specific actions succeeded or failed. This segmentation allows the system to identify exactly which processing actions need re-execution, ensuring complete recovery of failed actions while avoiding redundant execution of successful ones, thus optimizing resource usage.
Solution Approach 2:
The patent implements feedback mechanisms through execution matrices that capture the status of each processing action. This feedback information is used to determine which specific processing actions require re-execution during failover, ensuring that recovery is both complete (all failed actions are addressed) and efficient (no redundant processing of successful actions).
3Productivity
If fine-grained identification of failed processing actions is implemented, then re-execution efficiency is improved, but system complexity increases due to matrix calculations
Solution Approach 1:
The patent creates simplified representations (execution matrices and dependency matrices) that copy and organize the complex relationships between processing actions. These matrices serve as abstract models that enable efficient calculation and identification of failed processing actions without requiring complex real-time analysis of the actual batch job structure during failover events.
4Measurement precision
If matrix-based recovery calculation is performed, then precise failover management is achieved, but computational overhead increases
Solution Approach 1:
The patent performs computationally intensive matrix generation and population actions in advance, before failover events occur. The execution matrices and dependency matrices are prepared beforehand, capturing the structure and status of processing actions. During actual failover events, the system only needs to perform relatively simple matrix operations to identify failed actions, significantly reducing real-time computational overhead.
Data Source
AI summary
Computer-implemented methods, computer program products, and computer systems are provided. A method includes generating a running result matrix for a plurality of batch jobs, indicating corresponding running results for respective processing actions in batch jobs of the plurality of batch jobs. The method further includes obtaining an internal dependency matrix for the plurality of batch jobs, indicating corresponding dependencies between respective processing actions within a batch job of the plurality of batch jobs. The method further includes calculating a recovery matrix for the plurality of batch jobs based, at least in part, on the running result matrix and the internal dependency matrix, the recovery matrix indicating corresponding recovery actions for respective processing actions in batch jobs of the plurality of batch jobs. The method further includes executing failover management for one or more batch jobs based, at least in part, on the calculated recovery matrix.