Dynamic Storage Allocation for Seamless ML Model Migration
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Solution Overview
Problem
Current methods for upgrading machine learning models in IoT systems face challenges such as resource duplication, increased storage costs, and insufficient memory space during seamless service migration, leading to inefficiencies and user experience issues.
Innovation Solution
A method that involves determining two instances of a machine learning model, adjusting storage space allocation policies to phase out and phase in resources, and reclaiming storage space to efficiently migrate services, thereby optimizing storage usage and reducing costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If storage space is allocated to both current version and upgraded version instances simultaneously, then service migration can be performed, but storage costs increase and resource duplication occurs
Solution Approach 1:
The system adjusts the storage space allocation policy of the current versioninstance before migration occurs, proactively reducing its allocation in preparation for resource reallocation to the upgraded version instance
Solution Approach 2:
The storage space allocation policy is made dynamic and adjustable based on the migration status. The system transitions from a static allocation state to a dynamic state where allocation policies can be modified during the migration process to optimize resource usage
2Quantity of substance
If storage space is reclaimed from the current versioninstance, then storage costs are reduced, but migration time increases
Solution Approach 1:
The system performs preliminary actions by adjusting allocation policies before actual migration occurs, preparing the storage space configuration in advance to minimize disruption during the migration process
Solution Approach 2:
The migration process maintains continuous service availability by overlapping the storage space adjustment and data migration operations, ensuring that the service remains functional throughout the transition without interruption
3Productivity
If storage space allocation policies are adjusted dynamically, then resource efficiency improves, but system complexity increases
Solution Approach 1:
The system implements self-service mechanisms where the machine learning model management component automatically adjusts storage space allocation policies based on migration status, eliminating the need for manual intervention and reducing operational complexity
Solution Approach 2:
The system manages complexity by changing key parameters such as allocation policies and storage configurations automatically during migration, rather than requiring complex manual configuration and control
Data Source
AI summary
A method includes determining a first instance of a current version for a machine learning model and a second instance of an upgraded version for the machine learning model, the first instance executing a service for processing data; adjusting respectively, if it is determined that the service is to be migrated from the first instance to the second instance, a first allocation policy for storage space of the first instance and a second allocation policy for storage space of the second instance to a first target policy and a second target policy, wherein the first target policy is used to phase out storage space and the second target policy is used to phase in storage space; reclaiming allocated storage space for the first instance based on the first target policy; and allocating required storage space for the second instance based on the second target policy to realize migration of the service.