Kubernetes Pod Scheduling via Eviction Feedback
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Solution Overview
Problem
Container orchestration schedulers face inefficiencies due to lack of visibility into the container-orchestration system, leading to suboptimal resource utilization and operational inefficiencies, as they continue to assign pods to unsuitable nodes despite resource unavailability or eviction, due to their stateless nature and limited information about scheduling attempts.
Innovation Solution
Implementing a processing device that schedules pods on multiple nodes, updates scheduling data with node IDs and timestamps, and iteratively determines suitable nodes based on this data to avoid unsuitable hosts, ensuring successful pod placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the scheduler operates in compliance with configured predicates without visibility into system state, then the scheduler maintains simplicity and compliance with policies, but the scheduler repeatedly assigns pods to unsuitable nodes causing evictions and resource underutilization
Solution Approach 1:
The patent implements a feedback mechanism where the scheduler monitors pod eviction events and uses this information to update its scheduling decisions. When a pod is evicted from a node, the scheduler records this information and avoids reassigning pods to the same unsuitable node, creating a closed-loop control system that learns from past scheduling outcomes
Solution Approach 2:
The patent introduces an intermediary component that bridges the scheduler and the underlying system state. This intermediary collects and processes information about node suitability, pod evictions, and resource availability, providing the scheduler with enhanced visibility without requiring changes to the core scheduling logic or predicates
2Speed
If the scheduler is stateless and lacks visibility into scheduling attempts, then the scheduler maintains simplicity and fast operation, but the scheduler cannot learn from failed assignments and continues repetitive mis-scheduling
Solution Approach 1:
The patent performs preliminary actions by pre-collecting and storing scheduling attempt information, eviction data, and node suitability metrics before new scheduling decisions are made. This advance preparation of information allows the scheduler to make informed decisions without adding significant computational overhead to the actual scheduling operation
Solution Approach 2:
The system implements self-service by automatically tracking and storing its own scheduling history and performance metrics. The scheduler serves itself by maintaining internal state about past scheduling attempts and using this information to improve future decisions, eliminating the need for external monitoring systems
3Stability of the object's composition
If the scheduler continues to assign pods to the same unsuitable nodes, then the scheduler maintains consistent behavior and simplicity, but the system experiences repeated evictions and operational inefficiencies
Solution Approach 1:
The patent introduces dynamic adaptability to the scheduling system by enabling it to adjust its behavior based on observed performance and eviction patterns. The scheduler maintains consistent behavior for suitable nodes while dynamically avoiding nodes that have demonstrated unsuitability, creating a adaptive scheduling policy that responds to system conditions
Data Source
AI summary
Systems and methods for intelligently scheduling a pod in a cluster of worker nodes are described. A scheduling service may account for previous scheduling attempts by considering the time and node (scheduling data) on which a preceding attempt to schedule a node were made, and factoring this information into the scheduling decision. Upon making a determination of a node on which to attempt to schedule the pod, the scheduling data may be updated with the time and node ID of the determined node and the pod may be scheduled on the determined node. In response to determining that the pod has been evicted from the determined node, the above process may continue iteratively until the pod has been successfully scheduled.