Wireless Network Traffic Coordination via Dynamic Latency and Intelligent Buffering
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Solution Overview
Problem
Current Wi-Fi technologies face challenges in balancing power consumption and performance across wireless network devices and computing platforms, particularly due to the inability to distinguish between different workload scenarios, leading to inefficient power management and potential data loss from buffer overrun.
Innovation Solution
The implementation of dynamic latency values and intelligent buffering mechanisms, utilizing machine learning models to classify network data packets and coordinate power states based on workload and user requirements, allowing for optimized power saving and performance balancing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Wi-Fi devices continuously monitor and process network traffic to maintain performance, then user experience is improved, but power consumption increases
Solution Approach 1:
The system dynamically adjusts power states and buffering strategies based on real-time workload classification. The modem transitions between active and power-saving states adaptively, with buffer sizes and latency thresholds adjusted according to the classified workload type, resolving the contradiction between continuous monitoring for performance and power conservation
Solution Approach 2:
The system changes key parameters such as buffer size, latency threshold, and power state based on workload classification results. Different workload types (e.g., video streaming vs. web browsing) trigger different parameter configurations, allowing the system to optimize both performance and power consumption for each scenario
2Reliability
If buffer size is increased to prevent data loss during high traffic, then reliability is improved, but device memory usage and power consumption increase
Solution Approach 1:
The buffer size is dynamically adjusted based on workload classification and current network conditions. During high-traffic periods with latency-tolerant workloads, the buffer expands to prevent data loss. During low-traffic periods or latency-sensitive workloads, the buffer contracts to conserve memory and reduce power consumption from continuous buffer management
Solution Approach 2:
The system classifies workloads in advance and pre-configures appropriate buffer sizes before data arrival. This preliminary classification allows the system to allocate buffer memory efficiently, expanding it only when and where needed based on the predicted workload characteristics, rather than maintaining large buffers continuously
3Speed
If the modem remains in active state to quickly process network data, then processing speed is improved, but power consumption increases
Solution Approach 1:
The modem dynamically transitions between active and power-saving states based on workload classification. For latency-sensitive workloads (e.g., video calls, gaming), the modem remains active or quickly transitions to maintain low latency. For latency-tolerant workloads (e.g., batch downloads, email), the modem enters power-saving states longer, reducing power consumption while accepting higher latency
Solution Approach 2:
The system implements periodic monitoring and state transitions rather than continuous active processing. The modem checks for new data periodically at configured intervals, allowing it to enter low-power states between checks. This periodic action reduces average power consumption while maintaining responsiveness when data arrives
4Device complexity
If the system uses simple power management without workload classification, then device complexity is reduced, but power saving optimization is limited
Solution Approach 1:
The system segments workloads into distinct categories (e.g., latency-sensitive, latency-tolerant, real-time, batch) based on classification. Each segment receives tailored power management and buffering strategies. This segmentation allows sophisticated optimization without requiring complex continuous analysis, as the classification framework provides structured decision-making rules for each workload type
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture are disclosed that coordinate network traffic between a wireless network device and a computing platform. An example apparatus includes a wake-up selector to generate a target wait time parameter based on a workload type of a number of packets obtained from a network device and a user preference, the target wait time parameter indicative of a time interval that, when met, causes a modem to retrieve the number of packets, a data frame generator to generate a data frame that causes the network device to buffer the number of packets for the time interval, and a network packet controller to negotiate, using the data frame, the target wait time parameter with a network device.