Workload Scheduler Priority Preemption for Private Cloud Resource Allocation
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Solution Overview
Problem
Private clouds face resource constraints, leading to undeployed workloads due to insufficient resources, as adding resources is not financially viable, and existing solutions do not effectively manage workload prioritization to free up resources for new deployments.
Innovation Solution
A workload scheduler that implements priority scheduling based on infrastructure group metadata tags to preempt lower-priority workloads, ensuring resources are allocated to higher-priority workloads by identifying preemptible workloads and reallocating their resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If resources are added to private cloud to accommodate new workloads, then workload deployment capability is improved, but expansion costs increase
Solution Approach 1:
The system dynamically adjusts resource allocation by preempting lower-priority workloads when resources are needed for higher-priority workloads. The workload scheduler continuously monitors resource availability and workload priorities, making real-time decisions to migrate or terminate lower-priority workloads to free up resources for critical deployments without permanent resource expansion.
Solution Approach 2:
The system changes the operational state of workloads by modifying their priority levels and resource allocation parameters. By adjusting workload priorities and implementing preemption policies, the system optimizes resource utilization dynamically, allowing new workloads to be deployed by reallocating resources from lower-priority tasks rather than adding new infrastructure.
2Reliability
If resources are allocated to existing workloads, then operational stability is maintained, but new workload deployment is delayed
Solution Approach 1:
The system performs preliminary actions by pre-identifying and pre-migrating lower-priority workloads to different resources before new workloads need to be deployed. The workload scheduler proactively manages resource allocation by anticipating future resource needs and preparing resource pools in advance, reducing deployment delays while maintaining operational stability of existing workloads.
Solution Approach 2:
The workload scheduler acts as an intermediary between existing workloads and new deployment requests. It manages the resource allocation process by mediating conflicts between maintaining operational stability and enabling new deployments, using priority-based preemption to resolve resource contention and coordinate resource transfer from lower-priority to higher-priority workloads.
3Productivity
If priority scheduling is implemented to preempt lower-priority workloads, then resource allocation efficiency is improved, but system complexity increases
Solution Approach 1:
The system segments workloads into distinct priority groups (high, medium, low priority) and manages resource allocation separately for each segment. This segmentation simplifies the scheduling complexity by creating clear hierarchies and rules for preemption, where lower-priority segments can be preempted to serve higher-priority segments, making the complex resource allocation problem more manageable through structured categorization.
Data Source
AI summary
Examples described herein relate to a workload scheduler for deploying a workload in a private cloud. Responsive to determining that the private cloud has insufficient resources to host a pending workload, the workload scheduler may determine whether the private cloud comprises a preemptible workload based on infrastructure group metadata tags assigned to the already-deployed workloads. Further, the workload scheduler may preempt the preemptible workload from the private cloud upon determining that the private cloud comprises the preemptible workload. Then, the workload scheduler may deploy the pending workload in the private cloud.