Edge Compute Orchestration via Geolocation-Indexed Performance Datasets
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Solution Overview
Problem
Current communication networks face challenges in efficiently assigning edge compute tasks to optimize both performance and efficiency, as existing methods lack effective and efficient access to performance metric data across various geolocations, leading to suboptimal resource allocation and increased costs.
Innovation Solution
A system and method for generating and managing multi-access edge computing performance data, which involves a UE device and an edge compute orchestration system that perform performance tests to collect and integrate geolocation-indexed performance metrics, enabling intelligent edge compute node selection based on performance metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If distributed computing architectures with multi-access edge computing are deployed to enable UE devices to interoperate with network-edge-deployed computing resources, then computing efficiency and latency are improved, but access to performance metric data across various geolocations becomes complex and inefficient
Solution Approach 1:
The patent segments the communication network into multiple geolocation-based partitions, each with its own performance metric dataset. This allows the system to divide the large-scale performance data management problem into smaller, manageable geolocation-specific segments, reducing access complexity while maintaining high computing efficiency at each edge node
Solution Approach 2:
The patent introduces an intermediary system that mediates between UE devices and edge compute nodes by providing centralized performance metric data access. This intermediary layer simplifies the complexity of direct access to distributed performance data across multiple geolocations, enabling efficient task assignment while reducing system-wide access complexity
2Measurement precision
If performance tests are conducted to collect performance metric data for various edge compute nodes, then node selection accuracy is improved, but time and resources required for data collection increase
Solution Approach 1:
The patent performs preliminary performance tests and collects performance metric data in advance before actual edge compute tasks are assigned. By pre-collecting and storing performance data for multiple edge compute nodes across different geolocations, the system eliminates the need for real-time data collection during task assignment, thereby maintaining high measurement precision while significantly reducing time loss
Solution Approach 2:
The patent implements a dynamic performance dataset that is updated periodically and adaptively based on changing network conditions and edge node performance. This dynamic approach allows the system to maintain accurate performance metrics without conducting continuous tests, optimizing the balance between measurement precision and time consumption by updating data only when necessary
Data Source
AI summary
An exemplary edge compute orchestration system that is communicatively coupled with a set of edge compute nodes in a communication network accesses performance data aggregated by a particular edge compute node of the set. The performance data includes a performance metric and geolocation data detected by a user equipment (UE) device communicatively coupled to the communication network. The edge compute orchestration system integrates the performance data into a geolocation-indexed performance dataset representative of detected performance metrics, indexed by geolocation, for the communication network. Then, based on the geolocation-indexed performance dataset, the edge compute orchestration system selects the particular edge compute node for performance of an edge compute task. Corresponding systems and methods are also disclosed.