Dynamic Container Scheduling via Node Cost Optimization
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Solution Overview
Problem
Existing container orchestration systems lack efficient mechanisms to dynamically optimize the scheduling of containerized instances across nodes in a cluster, considering factors like cost, energy consumption, and performance constraints.
Innovation Solution
A container orchestration system that determines the current states of containerized instances and nodes, calculates costs associated with each instance on each node, and creates a list of preferred nodes for each instance to execute on, optimizing for performance and resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If container orchestration systems use static scheduling methods to assign containerized instances to nodes, then the system complexity is low and ease of operation is maintained, but resource utilization efficiency deteriorates and productivity decreases
Solution Approach 1:
The patent implements dynamic scheduling by continuously monitoring node states (resource availability, cost, energy consumption) and container performance metrics, then automatically reassigning containers to optimal nodes based on current conditions rather than static initial placement
Solution Approach 2:
The system establishes feedback loops that collect real-time data on container performance and node states, use this information to calculate optimal scheduling decisions, and continuously adjust container placements to improve resource utilization while managing system complexity through automated control
2Productivity
If the system continuously monitors and optimizes container placement to improve resource utilization, then productivity increases, but the computational overhead and device complexity increase
Solution Approach 1:
The system optimizes scheduling by dynamically changing multiple parameters including node cost metrics, energy consumption levels, resource availability states, and container performance indicators to determine optimal placement decisions that improve cluster efficiency
Solution Approach 2:
The scheduling system is divided into separate functional modules including state monitoring components, cost calculation modules, optimization algorithms, and execution controllers, allowing complex scheduling tasks to be managed through specialized subsystems that reduce overall system complexity
3Productivity
If containerized instances are moved frequently to optimize performance, then productivity improves through better resource allocation, but system stability deteriorates due to increased migration overhead
Solution Approach 1:
The system implements periodic scheduling reviews where container placements are reevaluated at scheduled intervals rather than continuously, allowing stable operation between reviews while still achieving optimization through regular adjustments to container placements based on accumulated performance data
Data Source
AI summary
A computing device determines a state of each containerized instance of containerized instances executing on a compute node in a cluster of compute nodes. The computing device determines a state of each compute node in the cluster. The computing device calculates, for each respective compute node in the cluster, a cost associated with each containerized instance on the respective compute node based on the state of each containerized instance and the state of each compute node. The computing device creates a list of preferred compute nodes for each containerized instance to execute on based on the state of each containerized instance, the state of each compute node, and the cost associated with each containerized instance for each compute node in the cluster. The computing device causes, based on the list, a first containerized instance of the containerized instances to be moved to a first compute node identified in the list.