Distributed Real-Time Pricing Model for Cloud ERP Network Traffic
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Solution Overview
Problem
Existing network configurations in cloud-based ERP systems face inefficiencies due to dynamic user queries and permissions, leading to suboptimal network performance and difficulty in maintaining Service-Level Agreements (SLAs) for IoT traffic, especially in scenarios with high transaction volumes.
Innovation Solution
A distributed real-time pricing model is implemented, using demand-supply principles to evaluate prices for ERP-IoT traffic flows and dynamically assign them to optimal queues based on response time and data transfer, along with a scheduler to maintain network resource utility and meet SLAs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If dynamic user queries and permissions are implemented in cloud-based ERP systems, then system adaptability and user flexibility are improved, but network performance deteriorates and SLA maintenance becomes difficult
Solution Approach 1:
The patent segments network traffic into different queues based on SLA requirements, user permissions, and query types. Each queue is handled with appropriate priority and resource allocation, allowing dynamic adaptability while maintaining network performance through structured segmentation of traffic flows.
Solution Approach 2:
The patent implements dynamic queue assignment and pricing models that adapt in real-time based on network conditions, user permissions, and query characteristics. The system dynamically adjusts resource allocation and pricing based on current system state, maintaining both adaptability and performance through continuous optimization.
2Productivity
If traditional network allocation is used in multi-tenant ERP systems, then implementation simplicity is maintained, but resource utilization efficiency deteriorates under high transaction volumes
Solution Approach 1:
The patent implements a feedback mechanism through real-time pricing models that monitor network usage, queue depths, and resource availability. The system continuously adjusts pricing and queue assignment based on feedback from actual network conditions, optimizing resource utilization efficiency while managing complexity through automated control loops.
Solution Approach 2:
The patent changes key parameters dynamically including queue assignment priorities, pricing rates, and resource allocation thresholds based on network conditions and transaction volumes. By adjusting these parameters in real-time, the system achieves high resource utilization efficiency without requiring complex manual configuration.
3Reliability
If real-time pricing and scheduling is implemented for ERP-IoT traffic flows, then bandwidth efficiency and SLA compliance are improved, but computational complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-configuring queue structures, pricing models, and scheduling algorithms before traffic arrives. SLA requirements and user permissions are pre-processed to establish baseline queue assignments, reducing the computational complexity of real-time decision-making while maintaining high SLA compliance.
Data Source
AI summary
Some embodiments are associated with multi-tenant software defined data center network traffic management. A data center computing system may calculate a first value for a first traffic flow, associated with a first user, using a dynamic, distributed, and substantially real-time model. The system may calculate a second value for to a second traffic flow, associated with a second user, using the dynamic, distributed, and substantially real-time model. The system may then dynamically allocate network resources to the first and second traffic flows based on the first and second priorities. Some embodiments may establish a plurality of network device queues and perform queue selection for optimization. According to some embodiments, the first user may be categorized as a premium user while the second user is categorized as an enterprise user.