Network Traffic Alignment for Power Consumption Reduction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current computing devices face challenges in aligning network wake interrupts with hardware sleep schedules, leading to shorter sleep modes and increased power consumption due to the lack of awareness of networking protocols by the CPU and NIC, resulting in inefficient power management.
Innovation Solution
Implementing an AI-based model to classify network workloads and align network interrupts with hardware sleep schedules by determining the type of incoming network data packets and scheduling network activity accordingly, using machine learning techniques such as neural networks to predict workload types and adjust power states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If network interrupts are not aligned with hardware sleep schedules, then network connectivity is maintained, but power consumption increases due to shorter sleep modes
Solution Approach 1:
The system performs preliminary classification of network workloads using AI models before scheduling interrupts. By predicting workload types in advance and pre-scheduling network activity around hardware sleep schedules, the system ensures network connectivity is maintained while extending sleep modes to reduce power consumption.
Solution Approach 2:
The system continuously monitors network traffic patterns and uses this feedback to refine AI-based workload predictions. This feedback loop enables dynamic adjustment of interrupt scheduling to align with hardware sleep schedules, optimizing both power consumption and network connectivity reliability.
2Productivity
If AI-based workload classification is implemented, then power management efficiency is improved, but device complexity increases
Solution Approach 1:
The patent introduces AI-based workload classification as an intermediary layer between network traffic and power management decisions. This intermediary component analyzes network data packets, predicts workload types, and provides scheduling recommendations, thereby improving power management efficiency without requiring direct complex integration throughout the entire system.
3Duration of action of stationary object
If network interrupts are scheduled independently of hardware sleep schedules, then implementation is simple, but sleep mode duration is reduced
Solution Approach 1:
The system dynamically adjusts interrupt scheduling based on real-time network conditions and predicted workload types. By making the scheduling policy adaptive rather than static, the system can extend sleep mode duration when safe while maintaining simple implementation through flexible, condition-based scheduling decisions.
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture are disclosed to align network traffic to improve power consumption. Example instructions cause one or more processors to classify a workload based on network packets obtained via a wireless communication; determine heuristics of platform activities corresponding to the workload; and schedule network interrupts based on hardware-based wake interrupts from a sleep mode using the heuristics.