Adaptive Data Compression for Faster, Lower-Energy Uploads
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Solution Overview
Problem
The rapid increase in data traffic to and from cloud servers leads to communication delays and energy consumption, particularly during file uploads, which can be costly for users on mobile networks, and existing data compression methods do not effectively improve user experience or energy efficiency for uploading files.
Innovation Solution
An adaptive data compression system that includes a context monitoring module to gather contextual information and a policy module to gather user preferences, with an analysis module determining whether to compress data based on these factors, using a compression module to apply appropriate algorithms for efficient transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If data is compressed prior to transmission, then data transmission size is reduced and energy consumption is decreased, but upload time increases due to compression processing
Solution Approach 1:
The system dynamically adjusts compression settings based on real-time network conditions and file characteristics. The compression level is not fixed but adapts to current circumstances, allowing the system to optimize between compression time and energy savings according to varying upload scenarios.
Solution Approach 2:
The system changes compression parameters such as compression level, algorithm selection, and block size based on file type, size, and network conditions. By adjusting these parameters dynamically, the system can minimize both compression time and energy consumption for different upload scenarios.
2Quantity of substance
If data is compressed prior to transmission, then network traffic is reduced and cost is decreased, but upload time increases due to compression processing
Solution Approach 1:
The system dynamically determines whether to compress data based on real-time assessment of network conditions, file characteristics, and user preferences. This dynamic decision-making allows the system to reduce network traffic when beneficial while avoiding unnecessary compression that would increase upload time.
Solution Approach 2:
The system performs preliminary analysis of the file and network conditions before initiating compression. By evaluating compression ratios, file types, and current network state in advance, the system can make informed decisions about whether compression will actually reduce overall upload time and network traffic.
3Use of energy by moving object
If compression is applied to all data, then energy efficiency is improved, but device complexity increases due to multiple modules and algorithms
Solution Approach 1:
The system employs a universal compression framework that can handle multiple file types and compression algorithms through a single integrated architecture. The context monitoring module, policy module, and analysis module work together to provide energy-efficient compression across diverse scenarios without requiring separate systems for each file type.
Solution Approach 2:
The system automatically monitors context, analyzes file characteristics, and selects appropriate compression strategies without requiring user intervention. The self-service nature of the system reduces the perceived complexity for users while maintaining energy efficiency through automated decision-making.
Data Source
AI summary
Embodiments of apparatuses and methods for adaptive data compression and associated contextual information are described. In various embodiments, an apparatus may include a context monitoring module to gather contextual information for transmission of data and a policy module to gather user preference on cost associated with transmission of data. The apparatus may further include an analysis module to determine whether to compress data prior to transmission, based at least in part on the contextual information and the user preference. Other embodiments may be described and/or claimed.