Video Transfer System Using View Likelihood Prediction
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Solution Overview
Problem
Current monitoring systems face high data transfer costs during peak hours, as data is transferred regardless of viewing needs, leading to inefficient resource usage and increased costs.
Innovation Solution
An efficient video transfer system uses a view likelihood model to predict when data will be accessed, generating transfer requests based on historical viewing patterns, and prioritizes data transfer during off-peak times, incorporating a transfer deadline and analyzing transfer requests for network, compute, and power budgets to optimize data transfer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is transferred immediately when desired for viewing, then data availability is ensured, but data transfer costs increase due to peak-time pricing
Solution Approach 1:
The system performs preliminary actions by transferring data before it is actually needed for viewing. The job scheduler identifies data that will be needed in the future and initiates transfers during off-peak hours, allowing the data to be available when needed while avoiding peak-time transfer costs.
Solution Approach 2:
The system uses feedback from historical viewing patterns and job scheduling information to optimize transfer timing. The job scheduler analyzes when data is likely to be needed and uses this feedback to schedule transfers during optimal times, balancing availability requirements with cost considerations.
2Loss of energy
If data transfer is delayed to off-peak times, then transfer costs are reduced, but data may not be available when needed
Solution Approach 1:
The system performs data transfers in advance of when the data is actually needed. By analyzing job schedules and viewing patterns, the system schedules transfers during off-peak hours while ensuring the data arrives before it is required, thus maintaining reliability while reducing costs.
Solution Approach 2:
The system dynamically adjusts transfer timing based on multiple factors including data priority, deadline requirements, and predicted viewing patterns. High-priority data with tight deadlines is transferred sooner, while lower-priority data is transferred during off-peak hours, creating a dynamic scheduling system that adapts to different requirements.
3Reliability
If all data is transferred regardless of viewing needs, then complete data availability is ensured, but resource usage increases
Solution Approach 1:
The system extracts and transfers only the specific data regions that are actually needed based on viewing patterns and job requirements, rather than transferring all data. The data region identifier enables precise identification and transfer of only the necessary data portions, improving resource efficiency while maintaining availability for needed data.
Solution Approach 2:
The system applies partial action by transferring only the necessary portion of data rather than all data. By analyzing viewing patterns and job requirements, the system determines the minimum necessary data transfer required to meet future viewing needs, avoiding unnecessary resource consumption.
4Loss of energy
If data transfer is optimized for cost, then transfer costs are reduced, but transfer time may increase
Solution Approach 1:
The system performs data transfers in advance during off-peak hours, allowing more time for transfer without impacting when the data is actually needed. This preliminary action approach reduces costs by avoiding peak-time transfers while preventing time loss by ensuring data is transferred before deadlines.
Solution Approach 2:
The system dynamically adjusts transfer timing and prioritization based on deadline requirements and data importance. Critical data with tight deadlines receives higher priority and is transferred sooner, while non-critical data is transferred during off-peak hours, optimizing the balance between transfer time and cost.
Data Source
AI summary
A system for marking and transferring data of interest includes an interface and a processor. The interface is configured to receive an indication to mark data of interest. The processor is configured to: determine whether to generate a transfer request for the data of interest based at least in part on a viewing likelihood estimate, and in response to a determination to generate the transfer request for the data of interest, generate the transfer request for the data of interest.