Network Node Power State Transitioning via Traffic Interrupts
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Solution Overview
Problem
Current power management in data centers transitions into deeper diminished power states based on system disuse, leading to increased energy consumption during frequent state transitions and longer recovery times, which is inefficient and costly.
Innovation Solution
The technique determines temporal intervals of inactivity in network traffic and generates an interrupt to transition the network node into a deepest diminished power state that allows for timely recovery to a fully operational state, reducing energy consumption while minimizing recovery time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If the system transitions to deeper diminished power states based on system disuse, then energy consumption is reduced, but recovery time increases
Solution Approach 1:
The patent applies dynamics by making the power state transition strategy adaptive rather than static. The system dynamically adjusts the depth of power state transitions based on predicted traffic patterns and actual system workload. Instead of always transitioning to the deepest power state, the system selectively chooses appropriate power states based on real-time conditions, optimizing the balance between energy savings and recovery time.
Solution Approach 2:
The patent employs preliminary action by predicting future traffic patterns and proactively planning power state transitions before they are needed. The system analyzes historical and current traffic data to forecast upcoming workload, then pre-determines the optimal power state sequence to achieve energy savings while ensuring timely recovery when traffic resumes.
2Loss of energy
If frequent power state transitions occur, then energy consumption during transitions increases, but system responsiveness decreases
Solution Approach 1:
The patent applies periodic action by implementing scheduled power state transitions based on predicted traffic periodicity. Instead of reacting to every traffic fluctuation, the system identifies periodic patterns in traffic and plans power state transitions to align with these patterns, reducing the frequency of unnecessary transitions while maintaining system responsiveness during actual traffic periods.
Solution Approach 2:
The system uses feedback mechanisms to monitor actual traffic patterns and adjust power state transition timing accordingly. By continuously observing traffic characteristics and comparing them with predictions, the system refines its transition strategy to minimize both energy consumption during transitions and impact on system responsiveness.
Data Source
AI summary
In nonlinear power state transitioning, temporal intervals of inactivity in network traffic traversing a network node are determined from transport control data carried in the network traffic. An interrupt is generated that compels a deepest diminished power state from a plurality of power states in the network node. The deepest diminished power state is the deepest power state from which recovery to a completely powered state within a minimum one of the inactivity intervals is assured. Electrical energy consumption in the network node is reduced in response to the interrupt in accordance with a power management schedule.