Process Run-Time Prediction via Canonical Command Matching
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Solution Overview
Problem
Predicting the run-time of processes on server computers is challenging due to variability and the need for user notification during scheduled maintenance, which can disrupt application execution.
Innovation Solution
A database system is implemented to store user histories, command lines, and runtime data, allowing for process run-time estimation and maintenance notification by converting command strings into canonical forms and comparing them with stored records to determine potential maintenance impacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If process run-time prediction is implemented using historical data, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the prediction task by separating data collection (historical runtime data), data processing (canonical form conversion), and prediction execution (run-time estimation). This modular approach improves prediction accuracy while managing system complexity through organized functional components.
Solution Approach 2:
The system performs preliminary actions by pre-processing command strings into canonical forms and storing historical runtime data before actual prediction is needed. This advance preparation improves prediction accuracy by having ready-to-use reference data while reducing real-time computational complexity.
2Reliability
If server maintenance is scheduled, then hardware reliability is improved, but process execution stability deteriorates
Solution Approach 1:
The system performs preliminary notification by predicting process run-times and alerting users before scheduled maintenance occurs. This advance warning allows users to migrate or save processes, maintaining execution stability while enabling necessary hardware maintenance for reliability.
Solution Approach 2:
The system applies preliminary anti-action by proactively notifying users of potential maintenance disruptions and enabling preventive process migration. This counteracts the destabilizing effect of maintenance by preparing alternative execution environments before the maintenance window.
3Productivity
If process migration is enabled during maintenance, then service continuity is improved, but operational complexity increases
Solution Approach 1:
The system enables self-service by automatically predicting run-times and generating maintenance notifications without requiring manual intervention. Users receive automated alerts and can independently migrate their processes, maintaining service continuity while reducing operational complexity through automation.
Data Source
AI summary
Various embodiments provide process run-time prediction for processes running on server computers. In one embodiment, process run-time of a process is determined by building a database with a history of users, command lines and runtime associated with each command line, and comparing the process with stored records of completed processes in the database. In some embodiments, in response to a determination that the time interval of a process is likely to intersect a planned maintenance period on a server computer, a maintenance notification can be sent to a user of the process and therefore allow the affected process to be migrated to unaffected server computer(s).