Smart Cluster Data Transmission Prioritization
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Solution Overview
Problem
The speed of cloud-based applications is limited by the slow upload of large data quantities, causing bottlenecks and performance degradation due to linear data uploads, which results in idle time for cloud servers and reduced operational efficiency.
Innovation Solution
A system that intelligently partitions large data streams into prioritized tranches based on static and dynamic factors, allowing for efficient and ordered data transmission to cloud-based applications, thereby preventing performance degradation and crashes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is uploaded in linear order from multiple users, then data integrity is maintained, but cloud server processing speed decreases due to idle time waiting for data
Solution Approach 1:
The patent segments large data streams into smaller batches or chunks, allowing the cloud server to process data in parallel rather than waiting for complete linear uploads. This segmentation enables the server to begin processing early batches while later batches are still being uploaded, eliminating idle time while maintaining data integrity through proper sequencing markers.
Solution Approach 2:
The system performs preliminary actions by uploading and processing early batches of data before all data from all users is complete. The smart cluster technology allows the server to start processing with available data and continue processing subsequent batches, rather than waiting for complete data sets. This preliminary processing eliminates server idle time while maintaining overall data integrity.
2Quantity of substance
If large quantities of data are uploaded to cloud applications, then comprehensive processing capability is utilized, but system performance degrades due to bottlenecks in data reception
Solution Approach 1:
The patent divides large data uploads into smaller manageable batches that can be processed concurrently. This segmentation prevents the bottleneck effect of processing massive data volumes sequentially, allowing the system to handle large quantities of data without performance degradation by maintaining steady processing throughput across multiple batches.
Solution Approach 2:
The smart cluster technology ensures continuous processing by always having data batches ready for the server. While one batch is being processed, the next batch is being prepared or uploaded, eliminating idle periods and maintaining continuous productive action. This continuity allows the system to utilize comprehensive processing capability without performance degradation.
3Measurement precision
If cloud servers wait for complete data uploads before processing, then accurate results are ensured, but operational efficiency is reduced due to significant idle time
Solution Approach 1:
The patent segments the data processing workflow into batches with sequencing information. Each batch contains metadata or markers that indicate its position in the overall data set, allowing the server to process batches in the correct sequence even when they are uploaded and processed out of strict chronological order. This maintains result accuracy while eliminating server idle time.
Solution Approach 2:
The system performs preliminary processing actions on early batches before all data is complete. By processing available batches preliminarily and then integrating results with subsequent batches, the system ensures accurate final results while minimizing idle time. The server never waits completely for all data before beginning any processing.
Data Source
AI summary
A smart transmission system is configured to upload data from a plurality of sources to a target application hosted in a cloud environment. A server receives a data stream from the plurality of sources. Using a priority algorithm, the server analyzes the data stream to determine which files and accounts are most critical for the target application's function. The files and their associated accounts are partitioned into tranches, and the tranches are uploaded to the target application in order of priority.