Cloud Pipeline Execution State Tracking for Automated Retry
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Solution Overview
Problem
Managing complex software releases in cloud computing platforms is challenging due to the need for manual restarts and expertise in cloud platform-specific tools, leading to delays and resource wastage in multi-tenant systems with multiple data centers having different requirements.
Innovation Solution
A system that uses cloud platform-independent declarative specifications to generate and manage data centers across various cloud platforms, enabling automated retry strategies and idempotent pipeline execution to ensure efficient deployment and rollback of software artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual restart of pipeline is performed after failure, then pipeline can be restarted, but significant delays in delivery and waste of computing resources occur
Solution Approach 1:
The system performs preliminary actions by tracking the execution state of each stage before failure occurs. When a failure is detected, the system uses the pre-tracked state information to automatically resume execution from the appropriate stage rather than requiring manual restart from the beginning, thereby reducing delivery time while maintaining reliability
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring pipeline execution state and using this information to automatically adjust pipeline resumption behavior. The execution state tracking provides feedback that enables the system to determine which stages have completed successfully and which need to be re-executed, eliminating the need for manual intervention and reducing delays
2Reliability
If manual restart of pipeline is performed after failure, then pipeline can be restarted, but computing resources are wasted from repeated execution
Solution Approach 1:
The system performs preliminary tracking of execution state for each pipeline stage, storing information about which stages have completed successfully. When failure occurs, this pre-tracked information prevents wasteful re-execution of successful stages, reducing computing resource consumption while maintaining execution reliability through automatic state-aware resumption
Solution Approach 2:
The execution state tracking system provides continuous feedback about pipeline progress, enabling intelligent decisions about which stages require re-execution after failure. This feedback mechanism prevents unnecessary repetition of successful stages, optimizing computing resource usage while ensuring reliable pipeline completion
3Reliability
If complex pipeline management is performed without automation, then expert knowledge can be applied, but significant manual intervention is required
Solution Approach 1:
The system implements self-service by automatically tracking execution state and managing pipeline resumption without requiring expert intervention. The automatic state tracking and intelligent resumption logic enable the system to manage itself, reducing the need for manual operations while maintaining reliable pipeline execution through embedded expertise in the automation logic
Solution Approach 2:
The system replaces manual mechanical pipeline management operations with automated computational processes. Instead of requiring experts to manually track and manage pipeline state, the system uses automated state tracking and resumption logic, substituting human expertise with programmable intelligence that achieves the same reliability while dramatically improving ease of operation
Data Source
AI summary
Computing systems, for example, multi-tenant systems deploy software artifacts in data centers created in a cloud platform using a cloud platform infrastructure language that is cloud platform independent. The system generates pipelines for deploying software artifacts in data center entities configured in a cloud platform. The system allows partial execution of pipelines such that the pipeline can be executed again to complete execution of the remaining stages. The system maintains state of the pipeline execution and checks the state to determine whether a stage should be executed during subsequent executions. The system allows a failed stage to be retried multiple times based on a retry strategy. A retry strategy may depend on the data center entity in a hierarchy of data venter entities of a data center.