Automated Execution Capacity Management System
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Solution Overview
Problem
Current virtualization technologies for managing large-scale computing resources face challenges in predicting and dynamically modifying program execution capacity to meet varying user demands efficiently and securely, leading to potential bottlenecks and resource misallocation.
Innovation Solution
An automated execution capacity management system predicts future program execution capacity based on historical data and recent activity, using forecasting models to generate ranges of expected usage, and dynamically modifies computing resources by adding or removing nodes to ensure optimal capacity alignment with predicted needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If virtualization technologies are used to share computing resources among multiple users, then resource utilization efficiency is improved, but the ability to predict and dynamically modify program execution capacity deteriorates
Solution Approach 1:
The system performs preliminary actions by predicting future program execution capacity requirements based on historical data and recent activity patterns. Forecasting models generate expected usage ranges in advance, allowing the system to proactively allocate or release computing resources before actual demand occurs, thus resolving the contradiction between static resource sharing and dynamic adaptability
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual program execution capacity usage and comparing it with predicted values. This feedback loop enables dynamic adjustment of resource allocation, allowing the virtualization system to adapt to changing user demands while maintaining efficient resource utilization across multiple users
2Stability of the object's composition
If computing resources are statically allocated to virtual machines, then system stability is improved, but resource misallocation and bottlenecks occur
Solution Approach 1:
The system transitions from static to dynamic resource allocation by implementing automated modification of program execution capacity. The forecasting models predict future capacity requirements, and the system dynamically adjusts resource allocation accordingly, maintaining system stability through controlled, prediction-based changes rather than abrupt modifications
Solution Approach 2:
The system changes key parameters of resource allocation based on predicted demand. By modifying program execution capacity parameters in response to forecasting results, the system optimizes resource distribution among virtual machines, preventing both over-provisioning and under-provisioning while maintaining operational stability
Data Source
AI summary
Techniques are described for performing automated predictions of program execution capacity or other capacity of computing-related hardware resources that will be used to execute software programs in the future, such as for a group of computing nodes that execute one or more programs for a user. The predictions that are performed may in at least some situations be based on historical data regarding corresponding prior actual usage of execution-related capacity (e.g., for one or more prior years), and may include long-term predictions for particular future time periods that are multiple months or years into the future. In addition, the predictions of the execution-related capacity for particular future time periods may be used in various manners, including to manage execution-related capacity at or before those future time periods, such as to prepare sufficient execution-related capacity to be available at those future time periods.