IoV Platform Dynamic Scaling with Stateful Data Sequence
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Solution Overview
Problem
Existing Internet of Vehicles (IoV) platform expansion and contraction solutions are limited in dynamically adapting to actual needs while ensuring a time sequence of data, leading to resource waste and reduced user experience.
Innovation Solution
A method and system for IoV platform expansion and contraction that involves acquiring resource prediction basic information, constructing a resource prediction model, determining a middleware messaging solution based on service type, generating an expansion and contraction solution, and correcting it in real-time based on changes in the consumer cluster.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If resource allocation is based on maximum demand, then user experience during peak periods is improved, but resource waste occurs during low usage periods
Solution Approach 1:
The patent implements dynamic resource allocation by continuously monitoring system load and automatically adjusting resource capacity. The resource expansion and contraction module dynamically scales computing resources based on real-time traffic patterns, ensuring adequate capacity during peak demand while reducing resources during low usage periods to eliminate waste.
Solution Approach 2:
The system changes resource allocation parameters based on demand conditions. By adjusting resource capacity parameters dynamically rather than maintaining fixed maximum allocation, the system adapts to varying traffic conditions, improving both service reliability during peaks and resource efficiency during lows.
2Loss of energy
If resource allocation is based on average traffic usage, then resource efficiency is improved, but service quality deteriorates during high demand periods
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously monitors traffic patterns, system load, and service performance. Based on this feedback, the resource expansion and contraction module automatically adjusts resource allocation to maintain service quality during peak demand while optimizing resource efficiency during normal periods.
Solution Approach 2:
The system transitions from static average-based allocation to dynamic demand-responsive allocation. By continuously adapting resource capacity to actual demand conditions, the system maintains high service quality during peaks while avoiding the resource waste associated with always maintaining maximum capacity.
3Device complexity
If conventional load balancing strategy is used for stateful services, then service distribution is simplified, but session adhesion cannot be realized and status data reaches resource upper limit
Solution Approach 1:
The patent introduces an intermediary mechanism (resource expansion and contraction module with consumer instance identification) that sits between the load balancer and stateful services. This intermediary tracks consumer instances and ensures that requests from the same consumer are directed to the same service instance, maintaining session adhesion without significantly increasing overall system complexity.
Solution Approach 2:
The system segments the load balancing function into two parts: basic load distribution and session-adhesion-aware routing. By separating these functions and applying session-aware routing specifically to stateful services, the system maintains session adhesion for stateful services while keeping the overall service distribution mechanism relatively simple.
4Ease of manufacture
If passive expansion and contraction based on CPU and memory thresholds is used, then implementation is simple, but response time delay increases due to reactive triggering
Solution Approach 1:
The patent implements proactive resource adjustment by predicting future resource needs based on traffic patterns and system trends. Instead of waiting for CPU or memory thresholds to be exceeded, the system anticipates demand changes and adjusts resources in advance, reducing response time delay while maintaining relatively simple implementation through automated prediction algorithms.
Data Source
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AI summary
Disclosed are an Internet of Vehicles platform expansion and contraction method and system, and a storage medium, which belong to the technical filed of Internet of Vehicles platforms. The method includes: acquiring resource prediction basic information, and performing resource prediction based on the resource prediction basic information and a pre-constructed resource prediction model; determining a corresponding middleware messaging solution based on a service type; generating an expansion and contraction solution based on a predicted resource and the middleware messaging solution, and performing Internet of Vehicles platform expansion and contraction based on the expansion and contraction solution; and correcting, based on a change in a consumer cluster, the Internet of Vehicles platform expansion and contraction solution in real time during operation. The middleware messaging solution is determined based on the service type, to realize stateful service deployment. A partitioning strategy and a threading model are used to ensure that device data is processed by the same partition, the same consumer instance, and a thread, so as to ensure a time sequence of the device data.