Compute Environment Management for Dynamic Task Configuration
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Solution Overview
Problem
Current compute environments lack efficient methods for optimizing task execution across diverse computing resources, leading to suboptimal performance and cost utilization due to the inability to dynamically select and configure resources based on specific task requirements.
Innovation Solution
A compute environment management system that tests and selects optimal configurations for computing resources, including instance types and software configurations, to optimize task execution for performance and cost, allowing for automatic or client-approved deployment of resources based on job definitions and metrics analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If computing resources are statically allocated without optimization, then device complexity is reduced, but task execution efficiency deteriorates
Solution Approach 1:
The system dynamically selects and configures computing resources based on task requirements rather than using static allocation. The compute environment management system evaluates multiple configuration options and adapts resource deployment to match specific task characteristics, enabling optimal performance while maintaining manageable complexity through automated decision-making.
Solution Approach 2:
The system changes configuration parameters of computing resources (instance types, software configurations, hardware specifications) to optimize task execution. By systematically varying and evaluating different parameter combinations, the system identifies optimal configurations for specific tasks, improving execution efficiency without requiring manual intervention for each change.
2Reliability
If computing resources are over-provisioned to ensure performance, then task execution reliability is improved, but cost increases
Solution Approach 1:
The system applies local quality by matching specific computing resource configurations to specific task requirements rather than using a uniform over-provisioned setup for all tasks. Each task receives precisely the computational resources it needs based on its characteristics, ensuring reliable execution while eliminating waste from unnecessary resource allocation.
Solution Approach 2:
The compute environment management system automatically determines optimal resource allocation without manual intervention. It self-services by evaluating task requirements, selecting appropriate configurations, and deploying resources accordingly, thereby ensuring reliable task execution while minimizing resource waste through automated optimization.
3Productivity
If computing resources are customized for each task, then task execution performance is improved, but device complexity increases
Solution Approach 1:
The compute environment management system serves multiple functions: it evaluates task requirements, selects optimal configurations, deploys resources, and monitors performance. This universal system handles diverse task types and configuration options through a single automated platform, improving task execution performance while managing configuration complexity centrally rather than requiring separate custom solutions for each task.
Solution Approach 2:
The compute environment management system acts as an intermediary between task requirements and computing resource deployment. It translates task specifications into optimal resource configurations, mediating the complexity of resource customization by providing a standardized interface for task submission and automated configuration selection, thereby improving performance without exposing users to configuration complexity.
4Loss of energy
If computing resources are dynamically selected and configured, then cost utilization is improved, but device complexity increases
Solution Approach 1:
The system uses feedback mechanisms to continuously evaluate task execution performance and resource utilization. By monitoring outcomes and adjusting resource allocation based on observed results, the system optimizes cost efficiency through iterative improvement. The automated feedback loop manages the complexity of dynamic resource selection by using data-driven decisions rather than requiring complex manual configuration management.
Data Source
AI summary
Methods, systems, and computer-readable media for task-level optimization of compute environments are disclosed. Execution is initiated of one or more tasks using a plurality of computing resources provisioned from a multi-tenant provider network. At least some of the computing resources vary in configuration. One or more metrics are determined that are associated with the execution of the one or more tasks. A configuration of the computing resources is selected based at least in part on the one or more metrics. A modified job definition associated with the one or more tasks is generated. The modified job definition indicates the selected configuration.