Dynamic Mirror Selection for Data Transfer Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems for data downloading from multiple mirrors lack dynamic intelligent selection, leading to suboptimal connections due to static mirror selection and inefficient benchmarking processes, which fail to account for changing network conditions and client locations.
Innovation Solution
A method and system for dynamically evaluating mirrors based on observed characteristics, assigning quality values, and repeatedly selecting the optimal mirror for data download, ensuring that the selection adapts to current network conditions and client location changes without requiring superfluous benchmarking downloads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If static mirror selection is used, then system complexity is reduced, but download quality and transfer speed deteriorate due to inability to adapt to changing network conditions
Solution Approach 1:
The patent implements dynamic mirror selection by continuously monitoring download characteristics (transfer speed, error rates, latency) and automatically switching between mirrors based on real-time performance. This transforms the static mirror selection into a dynamic system that adapts to changing network conditions, resolving the contradiction between system simplicity and download speed.
Solution Approach 2:
The system incorporates feedback mechanisms by monitoring download characteristics from each mirror and using this information to make intelligent selection decisions. The feedback loop continuously evaluates mirror performance and adjusts selection accordingly, improving download speed without requiring complex manual configuration.
2Measurement precision
If synthetic workload benchmarking is performed, then mirror performance can be evaluated, but unnecessary data transfer occurs increasing time and energy consumption
Solution Approach 1:
The patent extracts only the necessary performance metrics (transfer speed, error rates, latency) from the actual download process itself, rather than performing separate synthetic benchmarking. This allows accurate mirror evaluation to occur as a byproduct of the useful download activity, eliminating wasted time transferring benchmark data.
Solution Approach 2:
The system performs mirror evaluation continuously during the actual download process rather than requiring separate benchmarking phases. The download activity serves dual purposes: transferring useful data and simultaneously gathering performance metrics for mirror selection, thus maintaining continuous useful action without time loss.
3Extent of automation
If user manually selects mirror, then system automation is reduced, but selection accuracy may improve if user has network knowledge
Solution Approach 1:
The system performs self-service by automatically monitoring download characteristics and selecting optimal mirrors without user intervention. The intelligent algorithm processes performance data and makes selection decisions autonomously, achieving both high automation and reliable accuracy through objective real-time measurements rather than subjective user judgment.
4Device complexity
If mirror selection is permanently set, then system complexity is reduced, but adaptability to network changes deteriorates
Solution Approach 1:
The patent implements dynamic mirror selection that automatically adapts to changing network conditions by continuously monitoring download characteristics and switching between mirrors as needed. This dynamic approach maintains reasonable system complexity while achieving high adaptability to network changes.
Solution Approach 2:
The system performs periodic evaluation of mirror performance during the download process, reassessing which mirror is optimal at regular intervals or when performance thresholds are crossed. This periodic reevaluation enables adaptation to network changes without requiring constant complex management.
Data Source
AI summary
Systems and methods for dynamic intelligent mirror selection are presented. Dynamic intelligent mirror selection may include evaluating available mirrors at a client to determine an optimum mirror from which to download a portion of data. For each portion of data downloaded, reevaluation of the available mirrors may be provided such that dynamic selection of the mirrors may reflect changes in network conditions. The mirror selection may be based at least partially on observed characteristics of mirrors observed while downloading from a mirror, such that no synthetic work load is needed to benchmark mirror performance. The observed characteristics may include transfer speed, error counts, latency, and mirror load. Furthermore, a random bonus and usage bonus may be provided to help in facilitating the evaluation of available mirrors.