Multi-Tenant Mobile Edge Computing Bandwidth Management
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current mobile edge computing technologies face challenges in efficiently managing and optimizing bandwidth and data traffic for multiple tenants in 5G networks, leading to potential bottlenecks and increased latency, especially in scenarios with high data consumption and diverse traffic demands.
Innovation Solution
Implementing a mobile edge computing multi-tenant system that includes a server with configurable filtering criteria, bandwidth monitoring, and health checks, allowing each tenant to manage their traffic and applications locally while ensuring seamless integration with the service provider network, with features like data partitioning, disaster recovery, and performance monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If mobile edge computing is implemented for multiple tenants in 5G networks, then bandwidth management and data traffic optimization are improved, but system complexity increases due to multi-tenant management requirements
Solution Approach 1:
The patent implements segmentation by creating separate virtual network instances for different tenants, with dedicated bandwidth pools and traffic management policies for each tenant. This allows independent optimization of bandwidth for each tenant while maintaining overall system manageability through modular architecture.
Solution Approach 2:
The patent employs a universal edge computing platform that serves multiple tenants simultaneously, providing common infrastructure resources (compute, storage, networking) that can be dynamically allocated to different tenants based on their requirements. This multi-tenant approach improves bandwidth utilization efficiency while avoiding the complexity of completely separate systems for each tenant.
2Loss of time
If data partitioning is implemented for each tenant, then service quality and latency are improved, but network configuration complexity increases
Solution Approach 1:
The patent segments network traffic into tenant-specific virtual networks with dedicated data paths from edge compute nodes to data stores. This segmentation enables direct, low-latency connections for each tenant while the virtualization layer abstracts the complexity of multiple partitions, presenting a simplified configuration interface to administrators.
Solution Approach 2:
The patent introduces a network virtualization intermediary layer that manages data partitioning and traffic routing between tenants and network resources. This intermediary automatically handles the complexity of configuring multiple partitioned networks, providing simplified policies for tenants while maintaining optimized data paths that reduce latency.
3Reliability
If bandwidth monitoring and health checks are implemented, then network reliability is improved, but processing overhead increases
Solution Approach 1:
The patent implements partial monitoring by applying bandwidth monitoring and health checks selectively based on tenant requirements, traffic patterns, and service level agreements. Critical tenants and traffic flows receive continuous monitoring, while less critical flows use periodic or event-driven monitoring, reducing overall processing overhead while maintaining necessary reliability.
Solution Approach 2:
The patent enables self-service monitoring where edge compute nodes and data stores perform autonomous health checks and bandwidth measurements on their own operations. This distributed self-monitoring approach reduces centralized processing overhead while improving reliability through local detection and immediate response to issues.
Data Source
AI summary
A mobile edge computing system can utilize a server to host and partition user equipment data for a local area network. For example, a first mobile device can send partition data to the service, wherein the partition data is representative criteria associated with which data should be associated with a particular business entity. After the criteria is established, the server can enforce the criteria upon any mobile device that is within a defined geographic location subject to the criteria. When the criteria are enforced, the server device can determine how much data has been utilized against an account of the business entity.