AI Packet Merging for High-Throughput CPU Load Reduction
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Solution Overview
Problem
The increasing speed of network data poses a significant burden on central processing units (CPUs) in processing data packets, particularly with technologies like TCP offload engines, which require efficient methods to reduce processing load.
Innovation Solution
An electronic device employs artificial intelligence (AI) learning-based algorithms to merge and process data packets based on specified conditions, using a first algorithm before a flag is set and a second algorithm until the specified period ends, optimizing CPU usage and data throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI learning-based algorithms are applied to merge and process data packets, then CPU load is reduced and data transmission efficiency is improved, but device complexity and processing overhead increase
Solution Approach 1:
The patent applies AI learning-based algorithms to preliminarily merge and process data packets before they reach the CPU, performing packet consolidation and classification in advance. This preliminary action reduces the number of packets requiring CPU processing and prepares data in an optimized format, thereby reducing CPU load while improving overall transmission efficiency.
Solution Approach 2:
The patent introduces an intermediary processing layer between the network interface and CPU that uses AI algorithms to merge and classify packets. This intermediary component handles the complex processing tasks, acting as a mediator that shields the CPU from direct packet processing complexity while maintaining high data throughput efficiency.
2Use of energy by moving object
If packet merging is performed aggressively to reduce processing load, then CPU usage decreases, but data transmission delay may increase
Solution Approach 1:
The patent employs dynamic packet merging strategies where the merging behavior adapts based on real-time network conditions, packet types, and application requirements. The system dynamically adjusts merging parameters to balance CPU usage reduction with delay minimization, preventing excessive merging that would cause delays while still achieving significant CPU load reduction.
Solution Approach 2:
The patent changes processing parameters such as merging thresholds, time windows, and priority levels based on network conditions and application requirements. By dynamically adjusting these parameters, the system optimizes the balance between reducing CPU usage through merging and maintaining acceptable transmission delays for different traffic types.
Data Source
AI summary
An electronic device may include a communication circuit, a memory, and at least one processor. The memory may store instructions that cause the electronic device to execute an application, determine a property of the application, receive a plurality of data packets for the application from an external device through the communication circuit, determine whether at least one of data throughput information on the external device, state information about a channel transmitting the plurality of data packets, or signal round trip time information satisfies a specified condition, apply artificial intelligence learning-based algorithms to merge and process the plurality of data packets to the plurality of data packets, based on the specified condition being satisfied, and refrain from applying the artificial intelligence learning-based algorithms to the plurality of data packets, based on the specified condition not being satisfied.