Data Migration Reservation System for Network Load Balancing
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Solution Overview
Problem
Migrating large amounts of data from multiple devices to remote storage locations can overload networks and degrade cloud storage service performance, making it difficult for administrators to manage and track the process effectively without manual intervention.
Innovation Solution
A reservation system managed by a migration manager that determines data migration loads across devices, issues reservations to control the migration process, and dynamically adjusts parameters to prevent network overload and service degradation, allowing for automated data migration without user or administrator interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data migration is performed from multiple devices simultaneously, then migration speed and productivity are improved, but network overload and service degradation occur
Solution Approach 1:
The system segments the data migration process by dividing the plurality of source devices into different groups or cohorts. Each group migrates data in staggered batches rather than simultaneously, which reduces network overload while maintaining overall migration productivity. The migration manager controls which devices migrate in each batch, effectively segmenting the migration load.
Solution Approach 2:
The system implements periodic action by scheduling data migrations in repeated cycles or batches. Instead of continuous simultaneous migration, devices are assigned to migrate in periodic intervals controlled by the migration manager. This periodic batching approach maintains productivity while preventing network overload by allowing intervals between migration waves.
2Reliability
If manual tracking and control of data migration is performed, then migration can be completed without network overload, but administrator time and resources are consumed
Solution Approach 1:
The system implements self-service by enabling the migration manager to automatically track, monitor, and control data migration across all source devices without requiring continuous administrator intervention. The migration manager autonomously assigns migration tasks, monitors progress, handles failures, and optimizes migration schedules, freeing administrators from manual tracking while maintaining reliable network performance.
Solution Approach 2:
The system uses feedback mechanisms where the migration manager continuously receives status information from source devices about migration progress, errors, and network conditions. Based on this feedback, the migration manager dynamically adjusts migration schedules, reassigns devices, and optimizes performance automatically, eliminating the need for manual administrator monitoring while maintaining system reliability.
3Extent of automation
If automated migration scripts are used, then administrator intervention is reduced, but ability to quickly respond to migration problems is diminished
Solution Approach 1:
The migration manager implements continuous feedback loops that automatically detect migration problems, errors, or failures and immediately respond by adjusting migration schedules, reassigning devices, or notifying administrators only when necessary. This maintains high automation while preserving rapid problem response capability through intelligent monitoring and automatic corrective actions.
Solution Approach 2:
The system enables the migration manager to autonomously handle routine migration problems and optimizations without administrator intervention, while maintaining the capability to escalate complex issues. The automated system serves itself by detecting and resolving common migration issues, preserving both automation extent and operational ease.
4Loss of time
If all devices migrate data at the same time, then total migration time is reduced, but cloud storage service performance degrades
Solution Approach 1:
The system segments source devices into multiple groups that migrate data in staggered batches rather than all simultaneously. This segmentation reduces the concurrent load on cloud storage services, maintaining service performance while still achieving efficient overall migration through coordinated batch processing managed by the migration manager.
Solution Approach 2:
The system applies local quality by assigning different migration schedules, priorities, or parameters to different source devices or device groups based on their specific characteristics, network conditions, or service requirements. This allows optimization of each device's migration while maintaining overall cloud storage service performance, rather than applying a uniform migration schedule to all devices.
Data Source
AI summary
Systems and methods for migrating data. One system includes a server including at least one electronic processor. The electronic processor is configured to receive local storage information from a migration client executed by each of a plurality of source devices. The electronic processor is also configured to aggregate the local storage information received for each of the plurality of source devices to determine a migration load, and determine a reservation model based on the migration load. In addition, the electronic processor is configured to issue a first reservation to a first source device included in the plurality of source devices based on the reservation model, the first reservation triggering migration of data stored on the first source device to the at least one remote storage location, and, in response to completion of the first reservation, issue a second reservation based on the reservation model.