Compute Cluster Scheduler Balancing Resources
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Solution Overview
Problem
Conventional scheduling policies in computing clusters, such as first come, first served and priority-based allocation, often lead to inefficient resource utilization, resulting in suboptimal response times and inability to meet the minimum resource requirements of all jobs simultaneously, especially in scenarios with numerous parallel tasks.
Innovation Solution
A balanced scheduling policy that operates in multiple modes to allocate resources based on minimum requirements and desired distributions, dynamically adjusting resource allocation among jobs to prioritize higher priority tasks and ensure efficient use of computing resources, including de-allocating resources from excess allocations to meet minimum needs of other jobs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If first come, first served allocation policy is used, then jobs are assigned resources in submission order, but resource utilization becomes inefficient and response times increase
Solution Approach 1:
The scheduling policy dynamically adjusts resource allocation based on current system state and job characteristics. The scheduler continuously monitors resource availability and job priorities, reallocating resources from completed or low-priority jobs to high-priority jobs that need them, thereby improving both resource utilization and response times
Solution Approach 2:
The scheduling system incorporates feedback mechanisms where the scheduler receives information about job completion, resource availability, and priority changes. This feedback enables the scheduler to make informed decisions about resource reallocation, ensuring that resources are consistently directed to jobs that need them most, thus improving overall system efficiency and responsiveness
2Productivity
If priority-based allocation is used, then higher priority jobs receive resources first, but minimum resource requirements of all jobs cannot be met simultaneously
Solution Approach 1:
The resource allocation process is segmented into two distinct phases: first, allocating minimum guaranteed resources to all jobs to ensure reliability, and second, distributing excess resources to high-priority jobs to improve productivity. This segmentation allows the system to simultaneously meet minimum requirements and prioritize important jobs
Solution Approach 2:
Different allocation strategies are applied to different resource pools: guaranteed minimum resources are allocated based on job requirements and priorities, while excess resources are allocated based on priority weighting. This local differentiation allows the system to satisfy both reliability constraints and productivity goals
3Productivity
If resources are allocated to meet minimum requirements of all jobs, then resource distribution becomes balanced, but response time for high priority jobs increases
Solution Approach 1:
The scheduler operates continuously to monitor resource allocation and job priorities. When high-priority jobs emerge or resource availability changes, the scheduler immediately initiates reallocation to ensure these jobs receive necessary resources without delay, maintaining continuous optimization of the allocation state
Solution Approach 2:
The scheduler pre-allocates minimum guaranteed resources to all jobs before distributing excess resources. This preliminary action ensures that when high-priority jobs need additional resources, the foundation of minimum guarantees is already in place, allowing faster reallocation decisions
4Productivity
If excess resources are de-allocated from executing jobs, then minimum requirements of other jobs are met, but resource stability for executing jobs decreases
Solution Approach 1:
The system de-allocates only the excess resources beyond minimum guarantees, not the guaranteed portion. This partial action allows the system to meet the needs of waiting jobs while maintaining stable, guaranteed resource allocations for executing jobs, thus balancing productivity improvements with allocation stability
Data Source
AI summary
A scheduler for a compute cluster that allocates computing resources to jobs to achieve a balanced distribution. The balanced distribution maximizes the number of executing jobs to provide fast response times for all jobs by, to the extent possible, assigning a designated minimum for each job. If necessary to achieve this minimum distribution, resources in excess of a minimum previously allocated to a job may be de-allocated, if those resources can be used to meet the minimum requirements of other jobs. Resources above those used to meet the minimum requirements of executing jobs are allocated based on a computed desired allocation, which may be developed based on respective job priorities. To meet the desired allocation, resources may be de-allocated from jobs having more than their desired allocation and re-allocated to jobs having less than their desired allocation of resources.