Physical Node Optimizer for Containerized Application Management
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Solution Overview
Problem
Containerized application management (CAM) systems face inefficiencies in energy consumption due to servers remaining powered on during periods of low usage, leading to wasteful energy usage and increased costs.
Innovation Solution
A system and method that monitor operational parameters of nodes in a CAM system to determine when nodes can be powered down during low resource requirements, using a configuration data structure to analyze patterns and thresholds to efficiently redeploy pods and power down or power up nodes based on resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If nodes remain powered on to support containerized workloads, then system reliability and responsiveness are improved, but energy consumption increases during low usage periods
Solution Approach 1:
The system dynamically adjusts node power states based on real-time workload monitoring. Nodes transition between active and powered-off states according to resource utilization thresholds, enabling the infrastructure to adapt its energy consumption to actual operational needs while maintaining reliability during high-demand periods
Solution Approach 2:
The control plane continuously monitors resource usage metrics from nodes and workloads, using this feedback to make intelligent decisions about node power states. This closed-loop system ensures nodes remain powered on when needed for reliability while being powered off during low-usage periods to reduce energy consumption
2Use of energy by moving object
If nodes are powered down during low usage periods, then energy consumption is reduced, but system responsiveness and availability deteriorate
Solution Approach 1:
The control plane proactively monitors workload trends and predicts future resource requirements, powering nodes off before low-usage periods begin and powering them back on in advance of anticipated high-demand periods. This preliminary action ensures energy savings are achieved without compromising system responsiveness when workloads increase
Solution Approach 2:
The system implements dynamic power state transitions with automated node provisioning capabilities. When nodes are powered down, the control plane rapidly provisions new nodes or migrates workloads to maintain system responsiveness, ensuring that energy savings do not come at the cost of operational speed
3Loss of energy
If monitoring and management systems are enhanced to optimize node power states, then energy efficiency is improved, but system complexity increases
Solution Approach 1:
The control plane autonomously monitors node resource usage, determines optimal power states, and executes power management decisions without requiring external intervention. This self-service capability simplifies the user interface while achieving energy efficiency goals through automated, intelligent decision-making based on real-time system state
Data Source
AI summary
The described technology is generally directed towards minimizing energy requirements of nodes/servers during periods of low resource usage by collection of pods deployed on the nodes. An application can be deployed on a set of nodes via a set of pods. During a period(s) of low application resource requirement, a subset of the available nodes can be powered down to reduce the operational overhead/energy consumption of the set of nodes. Prior to powering down, any pods operating of the subset of nodes can be redeployed to nodes that are to remain powered, thereby freeing up the subset of nodes to be powered down. Operation of the subset of powered nodes can be monitored to determine the pods exerting operational pressure on the resources, whereby the subset of nodes currently powered down can be powered up to make their resources available to reduce the operational pressure of the pods.