Bounceable Virtual Machine Selection Through Historical Performance Analysis
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Solution Overview
Problem
Identifying and addressing performance degradation in virtual machines serving multiple client devices is inefficient and resource-intensive, leading to delayed issue resolution and extended session performance degradation.
Innovation Solution
A system and method to identify low-performance machines by analyzing historical data, applying a sequence of actions, and simulating performance improvements, correlating these actions with user experience enhancements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual identification of machines for remedial actions is performed, then administrator control is maintained, but efficiency is reduced and time consumption increases
Solution Approach 1:
The system enables machines to be automatically identified and selected for remedial actions based on performance metrics and historical data analysis, reducing the need for manual administrator intervention while maintaining effective control through automated decision-making algorithms
Solution Approach 2:
The patent replaces manual mechanical processes of machine identification and performance monitoring with automated computational systems that use historical data analysis, machine learning algorithms, and performance metric evaluation to automatically determine which machines require remedial actions
2Reliability
If comprehensive monitoring of all machines is implemented, then performance issues are detected, but resource consumption and complexity increase
Solution Approach 1:
The system divides the monitoring task by analyzing historical data of individual machines separately, evaluating each machine's performance metrics, session information, and action history independently to identify specific machines requiring attention without requiring complex centralized analysis of all machines simultaneously
Solution Approach 2:
Instead of continuously monitoring all machines with equal intensity, the system performs partial monitoring by focusing computational resources on machines that show signs of performance degradation based on historical data patterns, session activity, and predefined performance thresholds
3Stability of the object's composition
If remedial actions are delayed, then system stability is maintained, but user experience deteriorates
Solution Approach 1:
The system performs preliminary analysis of machine performance using historical data to identify machines that are likely to experience performance issues before they fully degrade, allowing proactive remedial actions to be taken while the machine is still operational, thus preventing user experience deterioration while maintaining system stability
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring machine performance metrics, session information, and the effects of previously taken actions to dynamically adjust the timing and type of remedial actions, ensuring actions are taken at optimal moments to improve user experience without compromising system stability
Data Source
AI summary
Described embodiments provide systems and methods for determining bounce-able machines. One or more processors can be coupled to memory. The one or more processors can identify data associated with a history of actions performed on a plurality of machines. The one or more processors can determine, using the data, a change in performance of the plurality of machines if a sequence of actions were applied to one or more of the plurality of machines. The one or more processors can select a machine of the plurality of machines based on at least on the change in performance of the machine satisfying a threshold. The one or more processors can initiate, responsive to the selection, the sequence of actions on the machine.