Zero-input maintenance assistant for virtual desktop infrastructure
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Solution Overview
Problem
Planning and performing maintenance in virtualized computing environments, such as virtual desktop infrastructure (VDI), is inefficient and error-prone, as it requires manual intervention and can lead to uncontrollable capacity risks due to unpredictable session logoff times, resulting in either excessive buffer reservation or prolonged maintenance periods.
Innovation Solution
A zero-input intelligent maintenance assistant that determines the number of hosts to shut down and the maintenance window based on risk models, ensuring minimal capacity risk, allowing for automated planning and execution of maintenance tasks without prior knowledge of specific host needs or maintenance durations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual maintenance planning is performed, then maintenance tasks can be executed, but excessive buffer reservation is required and the process is error-prone
Solution Approach 1:
The system performs self-service by automatically evaluating maintenance capacity risk and generating maintenance plans without manual intervention. The maintenance assistant autonomously monitors host status, predicts session logoff times, and determines optimal maintenance windows, eliminating the need for excessive buffer reservations and manual planning errors.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual session logoff times and comparing them with predictions. This feedback loop allows the system to refine its risk evaluation models and improve maintenance planning accuracy over time, reducing the need for conservative buffer reservations while maintaining reliability.
2Reliability
If manual maintenance planning is performed, then maintenance tasks can be executed, but the process is inefficient and time-consuming
Solution Approach 1:
The maintenance assistant autonomously performs all maintenance planning tasks including host selection, window determination, and risk evaluation without requiring manual administrator intervention. This automation dramatically reduces the time and effort required for maintenance planning while maintaining or improving reliability through consistent application of risk models.
Solution Approach 2:
The system replaces manual mechanical planning processes with automated computational models. Risk evaluation algorithms and prediction models substitute for human judgment and manual calculations, enabling rapid generation of maintenance plans without the time-consuming nature of manual procedures.
3Productivity
If maintenance is performed during unpredictable session logoff times, then maintenance can be executed, but capacity risk becomes uncontrollable
Solution Approach 1:
The system performs preliminary actions by predicting session logoff times in advance and determining maintenance windows before maintenance execution. This advance planning allows the system to identify optimal time slots where capacity risk is minimized, enabling maintenance execution without uncontrollable capacity risks.
Solution Approach 2:
The system uses feedback from actual session behavior patterns to refine predictions of future logoff times. By continuously learning from observed session durations and logoff patterns, the system improves its ability to predict when sessions will end, thereby better controlling capacity risk during maintenance operations.
4Productivity
If automated maintenance planning is implemented, then efficiency is improved, but system complexity increases
Solution Approach 1:
The maintenance assistant serves as an intermediary layer between the virtualized computing environment and administrators. It absorbs the complexity of risk evaluation and prediction algorithms, presenting simplified maintenance recommendations to users. This intermediary approach enables automated efficient planning without exposing the full system complexity to end users.
Solution Approach 2:
The maintenance assistant performs multiple functions including monitoring, prediction, risk evaluation, and plan generation within a single integrated system. This multi-functionality consolidates what would otherwise require multiple separate complex systems, achieving automated efficiency while managing overall system complexity through functional integration.
Data Source
AI summary
Intelligent maintenance may be planned and performed for hosts in a pool of hosts that run virtual desktop sessions. A number of hosts to be shut down for maintenance, as well as a start time for a maintenance window, may be determined based on a first risk model and on a capacity risk level. A second risk model may be used to determine whether a capacity risk is still less than the capacity risk level, if some hosts have sessions that take longer than expected to log off and so delay the start time of the maintenance window.