Incentive-Based Edge Application Deployment and Pricing
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Solution Overview
Problem
In edge computing, the passive nature of edge servers in application deployment leads to resource inefficiency and delayed deployment due to lack of incentive, and challenges in resource allocation across multiple servers complicate high-quality mobile application deployment with low response times.
Innovation Solution
A spontaneous edge application deployment and pricing method based on an incentive mechanism, which includes building a deployment system architecture with monitoring, planning, and execution, and defining an incentive mechanism using deployment willingness and profit optimization through a Stackelberg game model to maximize profits for both the application provider and edge server.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If edge servers passively wait for deployment requests, then resource consumption is reduced, but deployment response time increases and resource allocation efficiency deteriorates
Solution Approach 1:
Edge servers proactively announce their available resources and deployment capabilities before receiving deployment requests. The system pre-establishes resource information databases and deployment policy frameworks, enabling immediate action when requests arrive without passive waiting, thus reducing response time while maintaining efficient resource allocation.
2Ease of operation
If incentive mechanisms are introduced to encourage spontaneous deployment, then deployment主动性 improves, but system complexity increases
Solution Approach 1:
The system implements a feedback mechanism where edge servers receive deployment policies and resource information from the network side, actively announce their capabilities, and adjust their deployment behavior based on real-time feedback. This structured feedback loop encourages spontaneous deployment while keeping the system manageable through clear policy guidance and information flow.
3Reliability
If multiple edge servers are allocated resources, then service availability improves, but load balancing difficulty increases
Solution Approach 1:
The system segments resource allocation by dividing the edge computing network into multiple independent edge servers, each with its own resource database and deployment capabilities. This segmentation enables parallel resource distribution across multiple servers, improving service availability while simplifying load balancing through independent resource management at each edge node.
4Adaptability or versatility
If heterogeneous computing platforms are used across edge servers, then system versatility improves, but deployment compatibility difficulty increases
Solution Approach 1:
The system handles heterogeneity by dynamically adjusting deployment parameters and resource allocation strategies based on the specific capabilities of each edge server. Edge servers announce their technical parameters (CPU architecture, memory capacity, storage type), and the system adapts deployment configurations accordingly, enabling versatile deployment across heterogeneous platforms while managing compatibility through parameter-based adaptation rather than universal compatibility requirements.
Data Source
AI summary
Disclosed in the present invention is a spontaneous edge application deployment and pricing method based on an incentive mechanism. The method comprises the following steps: building an edge end application oriented spontaneous deployment system architecture; then proposing an incentive mechanism aiming at spontaneous edge application deployment and prizing; solving the spontaneous edge application deployment and prizing problem based on a backward induction method, thereby obtaining an optimal deployment solution of an edge server and an optimal prizing strategy of an application provider.