Virtual Network Compression Service for SLA-Aware Data Transmission
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Solution Overview
Problem
Existing data compression methods are inefficient as they often use generic algorithms, requiring significant computational resources and may not optimize compression ratios based on data content and context, leading to suboptimal performance and potential increased transmission times.
Innovation Solution
A framework for providing content, context, and service-level agreement (SLA) aware compression/decompression as a service, utilizing a virtual network engine that selects the appropriate compression algorithm based on data type, intended application, and SLA constraints, decoupling compression from applications and operating independently in computing networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If generic compression algorithms are used indiscriminately, then device complexity is reduced, but compression ratio and transmission efficiency deteriorate
Solution Approach 1:
The system segments the compression algorithm selection process into multiple stages: data characterization phase (analyzing data type, size, and content), algorithm candidate generation phase (identifying suitable algorithms from a library), and selection phase (choosing the optimal algorithm based on SLA constraints). This segmentation allows the system to maintain low complexity while achieving high transmission efficiency through targeted algorithm selection.
2Productivity
If multiple compression algorithms are installed and evaluated, then compression ratio improves, but compute resources and processing time increase
Solution Approach 1:
The system performs preliminary data characterization before algorithm selection, analyzing data type, size, and content patterns to pre-filter suitable algorithm candidates. This preliminary action reduces the search space and eliminates the need to evaluate all available algorithms, thereby achieving high compression ratios while minimizing compute resource consumption during the actual compression process.
3Productivity
If compression algorithms are selected based on data content and context, then compression efficiency improves, but device complexity and algorithm availability requirements increase
Solution Approach 1:
The system introduces an intermediary compression service component that acts as a mediator between the data transmission application and the compression algorithms. This intermediary handles the complex task of data characterization, algorithm candidate identification, and selection based on SLA constraints, thereby improving compression efficiency while shielding the application from the complexity of algorithm management and availability requirements.
4Loss of energy
If compression is performed at the source, then bandwidth consumption is reduced, but transmission time may increase due to compression overhead
Solution Approach 1:
The system dynamically changes compression parameters including algorithm selection, compression level, and data block size based on real-time analysis of data characteristics and SLA constraints. By adjusting these parameters, the system optimizes the balance between bandwidth consumption and compression time, achieving significant bandwidth reduction while minimizing the impact on overall transmission time through adaptive parameter tuning.
Data Source
AI summary
A compression system is disclosed. A compression service removes compression responsibilities from an application. The compression system can deploy virtual network engines to locations near the applications. The virtual network engines compress the data using a compressor selected from multiple compressors. The compressed data can then be transmitted, decompressed, and delivered to the destination.