Cluster Control Plane Hibernation for On-Demand Activation
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Solution Overview
Problem
Existing cluster infrastructures face inefficiencies in cost and energy consumption due to continuous operation of the control plane, even when there are no active workloads, and hibernation methods reduce reactivity to new traffic requests.
Innovation Solution
Implementing a demand-based activation mechanism for cluster infrastructures that allows the control plane to scale to zero utilization by checkpointing and restoring the cluster infrastructure using snapshot techniques, ensuring consistent service responsiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the control plane operates continuously to maintain service availability, then service responsiveness is improved, but energy consumption and cost increase
Solution Approach 1:
The control plane transitions between active and hibernation states based on workload conditions. During active periods, the control plane processes requests normally. During idle periods, the control plane enters hibernation by checkpointing its state to persistent storage. This periodic activation/deactivation pattern reduces energy consumption while maintaining service availability when needed.
Solution Approach 2:
The system performs checkpointing of the control plane state to persistent storage in advance, before complete shutdown. This preliminary action ensures that when the control plane needs to reactivate, it can restore its state quickly from the checkpoint rather than initializing from scratch, maintaining service responsiveness.
2Loss of energy
If the control plane hibernates to reduce resource utilization, then energy efficiency is improved, but reactivity to new traffic requests deteriorates
Solution Approach 1:
The control plane performs checkpointing of its state to persistent storage before entering hibernation. This preliminary action ensures that when traffic arrives and the control plane needs to reactivate, it can quickly restore its state from the checkpoint without lengthy initialization processes, maintaining fast reactivity to new requests.
Solution Approach 2:
The control plane creates a checkpoint copy of its state to persistent storage. This copy allows the control plane to be restored quickly from the checkpoint data rather than rebuilding state from scratch, enabling fast reactivation while the original control plane instance can be shut down to save energy.
3Productivity
If the cluster infrastructure scales to zero utilization, then cost efficiency is improved, but service responsiveness may be affected
Solution Approach 1:
The cluster infrastructure implements periodic scaling between active and zero-utilization states. When workload is present, the control plane is active and processes requests. When workload is absent for a threshold period, the control plane scales to zero by hibernating. This periodic scaling pattern reduces costs during idle periods while maintaining service responsiveness when needed.
Solution Approach 2:
Before scaling to zero, the system performs preliminary checkpointing of the control plane state to persistent storage. This ensures that when scaling back up, the control plane can quickly restore service from the checkpoint rather than performing lengthy initialization, maintaining service responsiveness despite scaling to zero.
Data Source
AI summary
A method, a system, and a non-transitory computer-readable storage medium are provided. The method includes detecting an access point of a cluster infrastructure. The cluster infrastructure includes an application associated with the access point and a control plane to schedule execution of the application. The method includes, responsive to detecting the access point, creating a proxy access point to intercept requests directed to the access point of the cluster infrastructure. The method includes controlling an operating state of the control plane responsive to the intercepted requests of the proxy access point.