Logical Chunk Compression for Time-Critical System State Transfer
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Solution Overview
Problem
Current data center management systems apply uniform compression to system state information without considering the context of data collection, which can lead to suboptimal compression and inefficient data transfer, especially when critical information needs to be transmitted quickly or resources are limited.
Innovation Solution
An information processing system that determines optimal compression levels for logical chunks of system state information based on factors like the time of need and available resources, using machine learning algorithms to split and compress data accordingly, enabling context-based compression and efficient transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If uniform compression is applied to all system state information, then the compression process is simple, but the compression efficiency is suboptimal and transfer time increases
Solution Approach 1:
The patent divides system state information into multiple logical chunks based on different criteria (device type, data category, criticality). Each chunk is then compressed using appropriate compression levels and algorithms, allowing differential compression strategies rather than uniform compression across all data.
Solution Approach 2:
The compression level and algorithm are dynamically selected based on multiple factors including time of need, resource availability, data criticality, and device characteristics. This dynamic adaptation enables the system to optimize compression efficiency for each specific context rather than using a static uniform approach.
2Quantity of substance
If high compression levels are applied to all data, then file size is reduced, but processing time and resource consumption increase
Solution Approach 1:
Different compression levels are applied to different logical chunks of system state information based on their specific characteristics. Critical data with tight deadlines receives lower compression (faster processing), while non-critical data receives higher compression (smaller size). This local differentiation optimizes the balance between file size and processing time.
Solution Approach 2:
The system changes compression parameters (level, algorithm selection) based on multiple factors including time of need, resource availability, and data characteristics. This parameter adaptation allows the system to adjust compression intensity dynamically, reducing processing time for urgent data while still achieving size reduction for non-urgent data.
3Productivity
If context-based compression is applied to different logical chunks, then compression efficiency is optimized, but the determination process becomes more complex
Solution Approach 1:
The system segments system state information into logical chunks based on device type, data category, and criticality. This segmentation simplifies the subsequent compression determination by grouping data with similar characteristics, making it easier to apply appropriate compression strategies to each group rather than evaluating each data point individually.
Solution Approach 2:
The system automatically determines compression levels for each logical chunk based on pre-defined criteria and available resources, without requiring manual intervention. The compression determination process evaluates multiple factors (time of need, resources, data characteristics) and autonomously selects appropriate compression parameters, reducing operational complexity.
4Quantity of substance
If compression is applied to critical data that needs quick transmission, then file size is reduced, but transfer time may increase due to processing overhead
Solution Approach 1:
The system dynamically selects compression levels based on the time of need and criticality of data. For critical data with tight deadlines, the system chooses lower compression levels or faster algorithms that minimize processing time while still providing some size reduction. For non-critical data, higher compression levels are applied without time constraints.
Solution Approach 2:
Compression parameters are changed based on data criticality and time constraints. The system adjusts the compression level and algorithm selection to optimize the balance between size reduction and processing speed for each logical chunk, ensuring that critical data is processed quickly while still achieving compression benefits.
Data Source
AI summary
An apparatus comprises a processing device configured to collect system state information from host devices, to split the collected system state information into logical chunks, and to determine, based at least in part on a plurality of factors, a compression level to be applied to each of the logical chunks. The plurality of factors comprise a first factor characterizing a time at which the collected system state information is needed at a destination device and at least a second factor characterizing resources available for at least one of performing compression of the collected system state information and transmitting the collected system state information over at least one network to the destination device. The processing device is further configured to apply the determined compression level to each of the logical chunks to generate compressed logical chunks, and to transmit the compressed logical chunks to the destination device.