Edge Cloud Data Readiness Manager for Mobility-Aware Session Migration
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Solution Overview
Problem
Conventional solutions for managing user session data in distributed edge cloud environments are inefficient, as they often unnecessarily replicate data across all or a fixed subset of data nodes, wasting resources and increasing complexity.
Innovation Solution
A data readiness manager is implemented to proactively migrate and replicate user session data by predicting user mobility and determining target data nodes associated with service instances likely to be accessed by the user, thereby conserving storage resources and simplifying service implementation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If user session data is replicated across all data nodes or a fixed subset of data nodes, then user session data availability is improved, but storage resources are wasted and service complexity increases
Solution Approach 1:
The patent applies local quality by dynamically determining which specific data nodes should store user session data based on predicted user mobility patterns. Instead of uniformly replicating data to all nodes or a fixed subset, the system identifies and replicates data only to the specific target data nodes where users are predicted to access services, thereby optimizing storage resource usage while maintaining data availability.
Solution Approach 2:
The patent implements dynamics by making the data replication strategy adaptive and dynamic rather than static. The system continuously predicts user mobility and dynamically adjusts which target data nodes receive user session data replicas. This dynamic approach allows the system to respond to changing user behavior patterns and optimize storage resource allocation in real-time, avoiding waste on nodes that will not be accessed.
2Reliability
If conventional data replication solutions are implemented across all data nodes, then data access reliability is improved, but device complexity increases
Solution Approach 1:
The patent introduces an intermediary component (the data replication system with mobility prediction capabilities) that manages the complexity of data replication. This intermediary layer handles the sophisticated logic of predicting user mobility and determining optimal target data nodes, shielding service implementations from the complexity while ensuring reliable data access. The intermediary absorbs the computational and logical complexity, presenting a simplified interface to services.
3Ease of manufacture
If data is replicated to a fixed subset of data nodes, then service implementation is simplified, but adaptability to new user mobility patterns is reduced
Solution Approach 1:
The patent applies dynamics by replacing fixed, static data replication configurations with dynamic, adaptive ones. The system uses mobility prediction algorithms that can adapt to new user behavior patterns and triggers automatically. When new mobility patterns emerge or user behavior changes, the system dynamically recalculates optimal target data nodes without requiring manual reconfiguration or complex service implementation changes, thus maintaining ease of implementation while greatly improving adaptability.
4Reliability
If user session data is migrated proactively based on predicted mobility, then seamless user experience is maintained, but computational resources are consumed for prediction and determination
Solution Approach 1:
The patent applies preliminary action by performing user mobility prediction and data replication decisions in advance, before users actually move or request data. The system proactively predicts where users will be and replicates data to target data nodes beforehand, ensuring seamless access when users arrive. This preliminary action eliminates the need for complex real-time data migration during user movement, actually reducing overall computational resource consumption by avoiding reactive, time-critical operations.
Data Source
AI summary
Disclosed herein is a method performed by one or more computing devices implementing a data readiness manager to proactively migrate and replicate user session data in a distributed edge cloud. The method includes receiving, from a local data management agent of a source data node, an indication to perform data readiness operations for a user session, wherein the source data node is associated with a service instance being accessed by a user associated with the user session, determining a predicted mobility of the user, determining one or more target data nodes based on the predicted mobility of the user, wherein each of the one or more target data nodes is associated with a service instance that is determined to have a sufficiently high probability of being accessed by the user at a future time, and causing data associated with the user session to be migrated and replicated from the source data node to each of the one or more target data nodes.