Edge Computing Request Processing Optimization
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Solution Overview
Problem
Current multi-access edge computing (MEC) systems lack dynamic decision-making capabilities for optimizing processing of application requests with low latency, failing to consider user device processing capabilities and network congestion effectively.
Innovation Solution
The proposed solution involves using machine learning (ML) methods to predict processing modes for application requests, which can include local processing, edge processing, or hybrid processing. This approach considers parameters such as signal strength, network congestion, and user preferences to decide the optimal processing mode and derive a cost function for resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If application requests are processed using traditional MEC systems, then processing can be performed at the edge, but the system lacks dynamic decision-making capabilities and cannot effectively optimize processing with low latency
Solution Approach 1:
The system dynamically selects processing modes (local processing, edge processing, or hybrid processing) based on real-time conditions such as network congestion, signal strength, and device battery level. This dynamic adaptation resolves the contradiction by enabling the system to switch between static processing configurations to optimize for low latency when conditions permit, while maintaining adaptability when conditions change.
Solution Approach 2:
The system incorporates feedback mechanisms that continuously monitor network conditions, device state, and processing performance. This feedback loop enables the system to learn from past decisions and adjust processing mode selections in real-time, providing both the low latency performance and dynamic adaptability required to resolve the contradiction.
2Productivity
If the system uses machine learning methods to predict processing modes and derive cost functions, then processing optimization is improved, but system complexity increases
Solution Approach 1:
The system segments the processing decision into distinct components: local processing capabilities assessment, edge processing evaluation, hybrid processing options, and cost function derivation. This segmentation allows the complex ML-based optimization to be broken down into manageable modules, improving productivity while controlling system complexity through structured organization.
Solution Approach 2:
The system performs preliminary actions by pre-calculating cost functions and processing mode predictions based on current conditions before actual request processing. This preliminary ML-based analysis enables optimized processing decisions to be made quickly during request handling, improving productivity without adding complexity during the critical processing path.
3Productivity
If the system dynamically selects processing modes based on real-time conditions, then resource utilization is improved, but the decision-making process becomes more complex
Solution Approach 1:
The system changes parameters such as network congestion level, signal strength, and battery status to determine optimal processing modes. By monitoring and responding to parameter changes in the environment rather than hardcoding complex decision logic, the system achieves improved resource utilization through dynamic adaptation while keeping the decision-making framework relatively simple and parameter-driven.
Data Source
AI summary
The present disclosure relates to a communication method and system for converging a 5th-Generation (5G) communication system for supporting higher data rates beyond a 4th-Generation (4G) system with a technology for Internet of Things (IoT). The present disclosure may be applied to intelligent services based on the 5G communication technology and the IoT-related technology, such as smart home, smart building, smart city, smart car, connected car, health care, digital education, smart retail, security and safety services. The present disclosure relates to a method for optimizing processing of application requests.