Edge Server Scheduling Optimization for Vehicle Computing Requests
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Solution Overview
Problem
Intelligent transportation systems (ITS) face challenges in handling computing requests from vehicles due to overload, leading to delayed or lost processing, sub-optimal quality of service (QoS), and inefficiencies in edge server management, particularly during surges in demand or vehicle movement.
Innovation Solution
An optimization system that predicts computing requests using a prediction solver and generates a request handling scheme to distribute processing loads across edge servers, prioritizing safety and infotainment services, and determines when to offload tasks to cloud servers based on resource constraints, ensuring reliable QoS and revenue optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If edge servers queue and prioritize computing requests during surge demand, then service coverage is maintained, but processing latency increases and requests may timeout
Solution Approach 1:
The patent implements dynamic request handling schemes that adapt in real-time to changing traffic conditions and server loads. The system continuously monitors surge patterns and adjusts queue management strategies, prioritization rules, and offloading decisions dynamically rather than using static configurations, thereby maintaining service coverage while minimizing latency during demand surges.
Solution Approach 2:
The system performs preliminary actions by predicting surge demand patterns in advance and pre-configuring request handling schemes before overloads occur. The optimization system analyzes historical traffic data and vehicle movement patterns to anticipate demand spikes, preparing appropriate queue management and offloading strategies proactively, which reduces processing latency when surges actually occur.
2Productivity
If edge servers reject or offload computing requests to cloud servers, then resource overload is avoided, but service latency increases and time-sensitive requests are sub-optimally handled
Solution Approach 1:
The patent introduces an optimization system as an intermediary between edge servers and cloud servers that intelligently manages request routing. This intermediary analyzes request characteristics, server capacity, and traffic patterns to determine the optimal handling location, preventing premature offloading to cloud servers and reducing unnecessary latency while maintaining effective resource utilization.
Solution Approach 2:
The system dynamically changes operational parameters such as offloading thresholds, queue capacity limits, and prioritization weights based on real-time conditions. By adjusting these parameters adaptively rather than using fixed rules, the system optimizes the balance between resource utilization and service latency, keeping time-sensitive requests at edge servers while offloading non-urgent tasks to cloud infrastructure.
3Reliability
If edge servers degrade QoS level to satisfy all computing requests, then service availability is maintained, but quality of service decreases
Solution Approach 1:
The patent applies local quality by implementing differentiated QoS handling for different request types and vehicles. Instead of uniformly degrading all services, the system identifies critical requests (e.g., safety-related, time-sensitive) and maintains high QoS for these while allowing non-critical services to be degraded or deferred. This selective approach preserves service availability while maintaining quality where it matters most.
Solution Approach 2:
The optimization system incorporates feedback mechanisms that continuously monitor QoS levels, server loads, and traffic patterns. Based on this feedback, the system dynamically adjusts request handling schemes to prevent unnecessary QoS degradation. When resources are sufficient, the system maintains high QoS; when overloaded, it selectively degrades only non-critical services while preserving critical ones, thereby maintaining service availability without blanket quality reduction.
4Adaptability or versatility
If edge servers handle rapidly changing computing request types due to vehicle movement, then service adaptability is improved, but processing complexity and timeout risks increase
Solution Approach 1:
The patent implements dynamic request handling schemes that automatically adapt to changing vehicle movement patterns and request types. The system continuously updates prioritization rules, queue management strategies, and offloading decisions based on real-time traffic conditions, vehicle speeds, and request characteristics, enabling high service adaptability without requiring complex manual reconfiguration.
Solution Approach 2:
The optimization system enables self-service by automatically analyzing incoming requests, vehicle contexts, and server capacities to determine optimal handling strategies without external intervention. The system autonomously adjusts to rapidly changing request types and vehicle movements, managing processing complexity internally while presenting simple, adaptive service to users.
Data Source
AI summary
System, methods, and other embodiments described herein relate to improving execution of processing requests by an edge server. In one embodiment, a method includes predicting a number of computing requests from vehicles for execution by the edge server using a prediction solver for a time period that is forthcoming. The prediction solver may predict the number of computing requests using a prediction model selected in association with service constraints of the edge server and information from an additional server. The method also includes determining a request handling scheme using an optimization solver according to the number of computing requests, the service constraints of the edge server, and a service area of the edge server. The method also includes communicating the request handling scheme and a resource schedule to the edge server on a condition that a resources criteria are satisfied for the time period.