Affinity-Based Processor Pool Scheduling for Build Systems
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Solution Overview
Problem
Existing build systems for software development life cycles do not fully optimize for parallel processing, leading to inefficient execution of tasks and increased end-to-end build times, particularly in continuous integration/continuous deployment pipelines.
Innovation Solution
A system that schedules tasks across multiple processors by grouping them into affinity-class-based pools, adjusts pool sizes based on workload measurements, and utilizes machine learning to predict future load and optimize resource allocation, ensuring efficient execution of tasks like compilation and machine learning model training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If tasks are executed using multiple servers without affinity-based pooling, then resource utilization may appear flexible, but end-to-end build times increase significantly and task execution becomes inefficient
Solution Approach 1:
The system segments processors into affinity-based pools, where each pool contains processors suited for specific task types. This segmentation allows tasks to be executed on processors with appropriate characteristics, reducing execution time and improving productivity.
Solution Approach 2:
Different pools are created with specific qualities or characteristics matched to task requirements. Each pool has local optimization for particular task types, ensuring that tasks receive processing from processors with relevant skills or hardware characteristics.
2Productivity
If pool size is fixed, then system configuration is simple, but resource utilization becomes inefficient under varying workload conditions
Solution Approach 1:
The system dynamically adjusts pool sizes based on measured workload conditions. Pool sizes are not fixed but adapt in response to actual task arrival rates and processor availability, optimizing resource utilization while managing complexity through automated adjustments.
Solution Approach 2:
The system incorporates feedback mechanisms where workload measurements are used to adjust pool sizes. This feedback loop ensures that pool configuration continuously adapts to changing conditions, improving resource utilization efficiency.
3Productivity
If manual pool size adjustment is used, then control is straightforward, but the system cannot respond quickly to changing workload patterns
Solution Approach 1:
The system performs self-adjustment of pool sizes based on automated workload measurements. This self-service capability eliminates the need for manual intervention while enabling rapid response to changing workload patterns through automated decision-making.
Solution Approach 2:
The system automatically changes pool size parameters in response to measured workload conditions. This parameter adjustment is performed dynamically based on observed task arrival rates and system state, enabling quick adaptation without manual configuration.
Data Source
AI summary
A system schedules tasks using multiple processors. Examples of tasks include build tasks, training of machine learning models, and testing. The system groups the processors into a set of pools. A pool represents a set of processors associated with an affinity class. The system schedules execution of new tasks by determining an affinity class for a new build task based on characteristics of the new build task. The system identifies a pool matching the affinity class of the new build task. The system adds the new build task to the queue data structure of the pool matching the affinity class. The system adjusts the size of a pool based on a measure of workload associated with the pool. The system may determine the measure of workload as a weighted aggregate of various features describing the pool or using a machine learning model.