Context-Aware Data Migration Job Rescheduling
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Solution Overview
Problem
Current multi-cloud systems are inadequate in pushing and prioritizing context-relevant data to cloud servers in a timely and relevant fashion, failing to adapt to changing contextual situations and not optimizing network bandwidth utilization.
Innovation Solution
The method involves identifying contextual situations, determining relevant data sets and applications, calculating bandwidths, and dynamically scheduling job processing sequences to prioritize and migrate data based on contextual relevance and available bandwidth.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data migration is performed using traditional multi-cloud systems, then data can be stored in multiple locations, but the system fails to prioritize context-relevant data and does not optimize bandwidth utilization
Solution Approach 1:
The system dynamically adjusts data migration priorities and bandwidth allocation based on contextual situations. The job rescheduling mechanism continuously adapts to changing conditions, modifying the processing sequence of data migration tasks to optimize both productivity and resource utilization in real-time.
Solution Approach 2:
The system changes migration parameters such as priority levels and bandwidth allocation based on contextual relevance. By evaluating contextual situations and application requirements, the system adjusts these parameters to ensure that context-relevant data is migrated with appropriate priority while optimizing overall bandwidth utilization.
2Reliability
If all data sets are migrated with equal priority, then comprehensive data coverage is achieved, but contextually relevant data is not processed in a timely manner
Solution Approach 1:
The system applies different priority levels to different data sets based on their contextual relevance. Instead of uniform treatment, each data set receives a priority level tailored to its specific importance and urgency, ensuring that contextually critical data is processed faster while maintaining overall data completeness.
Solution Approach 2:
The system focuses computational resources and bandwidth on migrating context-relevant data with higher priority, rather than treating all data equally. This partial action approach ensures timely processing of critical data while still maintaining comprehensive data coverage through job rescheduling.
3Device complexity
If data migration is performed without contextual analysis, then system complexity is reduced, but the system cannot adapt to changing contextual situations
Solution Approach 1:
The system performs preliminary contextual analysis and application identification before data migration begins. By pre-evaluating contextual situations and determining priority levels in advance, the system prepares migration strategies that can adapt to specific contexts without adding significant complexity during the actual migration process.
Solution Approach 2:
The job rescheduling mechanism incorporates feedback from contextual analysis to dynamically adjust migration priorities. The system continuously monitors contextual situations and uses this feedback to modify the processing sequence, enabling adaptability while maintaining manageable system complexity through structured feedback loops.
Data Source
AI summary
A method, system, and computer program product for context relevant data migration to a cloud server with job rescheduling are provided. The method identifies a contextual situation associated with at least a portion of data sets available for migration to a cloud server by a set of data sources. The method identifies an application associated with the contextual situation. A set of use characteristics are determined for the data sets based on the contextual situation and the application. Bandwidths are determined for one or more data sources. One or more data sets are selected for prioritization based on a contextual relevance, the set of use characteristics, and the bandwidths for the one or more data sources. The method schedules a job processing sequence of the selected one or more data sets based, at least in part, on the contextual relevancy, the set of use characteristics, and the bandwidths.