Hierarchical Scheduler Resource Allocation
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Solution Overview
Problem
Existing scheduler systems lack an efficient method to assign resource consumers to available resources across a hierarchy of schedulers, leading to suboptimal resource allocation and scalability issues.
Innovation Solution
A hierarchical scheduler system where placement requests are propagated down to leaf schedulers, which report scores back up the hierarchy, allowing parent schedulers to compare and propagate the best scores to the root scheduler for optimal resource bucket assignment, with randomization to ensure even distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a flat scheduler architecture is used, then implementation is simple, but scalability and resource allocation efficiency deteriorate
Solution Approach 1:
The scheduler is segmented into a hierarchical structure with root schedulers, intermediate schedulers, and leaf schedulers. Each scheduler manages a specific subset of resource buckets, allowing the system to scale by adding more schedulers and buckets without requiring complete reconfiguration. This segmentation enables parallel processing of resource allocation requests across multiple schedulers simultaneously.
Solution Approach 2:
The system transitions from a single-dimensional flat scheduler to a multi-dimensional hierarchical scheduler structure. Requests can be routed through multiple paths in the hierarchy, and scores are aggregated from multiple levels, adding dimensional complexity that improves resource allocation efficiency and scalability.
2Adaptability or versatility
If resource buckets are frequently added or removed, then system adaptability improves, but allocation stability deteriorates
Solution Approach 1:
The hierarchical scheduler structure is designed to be dynamic, allowing resource buckets to be added or removed at any level without requiring complete system reconfiguration. When resource buckets change, only the affected leaf schedulers and their parent schedulers need to be updated, while the rest of the hierarchy remains stable and continues to function.
Solution Approach 2:
The system incorporates score propagation and comparison mechanisms that anticipate resource changes. By maintaining score information at multiple hierarchical levels and having parent schedulers compare scores from multiple children, the system is prepared for resource bucket additions or removals, ensuring stable allocation decisions even as the system evolves.
3Productivity
If randomization is applied for even distribution, then resource utilization improves, but assignment predictability deteriorates
Solution Approach 1:
The system uses score propagation as a feedback mechanism where leaf schedulers report resource availability scores up the hierarchy, and parent schedulers compare these scores to make informed decisions. This feedback loop allows the system to balance randomization for even distribution with score-based guidance for predictable, high-quality assignments, ensuring both resource utilization and assignment quality.
Data Source
AI summary
Techniques disclosed herein provide an approach for assigning resource consumers to available resources. In one embodiment, components of a distributed scheduler are organized into a hierarchy, such as a tree. A placement request received at a root scheduler of the hierarchy is propagated down the hierarchy, either to all children or to randomly selected subsets of children of each scheduler in the hierarchy. Leaf schedulers in the hierarchy that receive the request each propagate back up a score indicating the amount of free resources in its corresponding resource bucket. Branch schedulers then compare scores that they receive, and each further propagate one of the received scores, such as the highest score, based on the comparison, until the root scheduler is reached. The root scheduler makes an additional comparison and returns one of the resource buckets in response to the received placement request.