Predictive Execution Capacity Governance for Dynamic Resource Scaling
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Solution Overview
Problem
Managing the dynamic execution-related capacity in large-scale computer networks is complex due to increased scale and scope, requiring efficient and automated methods to allocate and modify computing resources in response to changing demands.
Innovation Solution
An automated execution capacity management system predicts future capacity needs and dynamically modifies computing resources by adding or removing nodes, using historical data and real-time activity analysis to ensure optimal resource allocation and utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual provisioning and management of physical computing resources is performed, then control and customization are maintained, but administrative complexity and time consumption increase significantly with network scale
Solution Approach 1:
The system enables automated self-service through the execution capacity management system that automatically monitors, predicts, and adjusts computing resource allocation without requiring manual administrative intervention. The system serves itself by using historical data and machine learning algorithms to autonomously provision and manage physical computing resources, thereby reducing administrative complexity while maintaining control and customization.
Solution Approach 2:
The system performs preliminary actions by predicting future execution capacity requirements using historical data analysis and machine learning models. This allows the system to proactively allocate and provision computing resources before they are actually needed, preventing resource bottlenecks and reducing the need for reactive manual management interventions.
2Productivity
If computing resources are statically allocated, then resource allocation is simple and predictable, but responsiveness to changing demands decreases
Solution Approach 1:
The system implements dynamic resource allocation by continuously monitoring execution capacity metrics and automatically adjusting computing resource provisioning based on real-time and historical data. This dynamic approach allows the system to adapt resource allocation to changing demands while maintaining high productivity through optimized utilization, resolving the contradiction between static simplicity and dynamic responsiveness.
Solution Approach 2:
The system employs feedback mechanisms by continuously collecting execution capacity data, analyzing trends through machine learning models, and using this feedback to automatically adjust resource allocation. This closed-loop feedback system ensures that computing resources are dynamically optimized for productivity while remaining highly responsive to changing demands.
3Power
If more computing nodes are added to increase execution capacity, then processing power and capacity increase, but system complexity and management overhead increase
Solution Approach 1:
The execution capacity management system automatically manages the complexity of coordinating multiple computing nodes through self-service automation. It autonomously monitors performance metrics, predicts capacity requirements, and dynamically provisions or deprovisions nodes as needed, thereby increasing execution capacity while preventing management overhead from becoming unmanageable through automated self-coordination.
Solution Approach 2:
The system performs preliminary provisioning actions by predicting future execution capacity needs and pre-configuring computing nodes before they are fully required. This allows the system to scale execution capacity efficiently by having nodes ready in advance based on predicted demand, reducing the complexity of last-minute scaling operations.
Data Source
AI summary
Techniques are described for managing program execution capacity or other capacity of computing-related hardware resources used to execute software programs, such as for a group of computing nodes that is in use executing one or more programs for a user. Dynamic modifications to the program execution capacity of the group may include adding or removing computing nodes, such as in response to automated determinations that previously specified triggers are currently satisfied, and may be automatically governed at particular times based on automatically generated predictions of program execution capacity that will be used at those times by the group, such as to verify that requested dynamic execution capacity modifications at a time are within the predicted execution capacity values for that time. In some situations, the techniques are used in conjunction with a fee-based program execution service that executes multiple programs on behalf of multiple users of the service.