Compression Parameter Selection Using Dynamic Cost Models
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Solution Overview
Problem
Existing data compression systems fail to consider the financial cost of CPU usage and changing market prices when selecting compression algorithms and settings, leading to suboptimal resource management in cloud computing environments.
Innovation Solution
A system and method that use a cost model to select appropriate compression algorithms and settings, taking into account CPU time costs, data volume, access frequency, and latency, allowing for dynamic adjustments based on changing conditions to optimize computational and storage costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If more computational effort is spent on compression (using more expensive algorithms or adjusting parameters), then compression effectiveness is improved, but computational cost increases
Solution Approach 1:
The system dynamically adjusts compression parameters and algorithm selection based on real-time cost models that evaluate CPU usage patterns, spot market pricing, and workload characteristics. This allows the compression effectiveness and computational cost to be optimized adaptively rather than using fixed settings, resolving the contradiction by making the system responsive to changing conditions.
Solution Approach 2:
The patent changes compression parameters (such as compression level, algorithm type, and dictionary size) based on cost model evaluations. By adjusting these parameters dynamically according to computational cost assessments and market conditions, the system achieves optimal compression effectiveness while controlling computational expenditure.
2Productivity
If compression is performed to the maximum of available CPU power, then compression power is improved, but adaptability to changing cost conditions deteriorates
Solution Approach 1:
The system implements feedback loops that continuously monitor CPU usage, spot market pricing signals, and compression performance metrics. This feedback enables the system to adjust compression power dynamically, maintaining high productivity when conditions are favorable while adapting to cost constraints when market conditions change, thus resolving the contradiction between compression power and adaptability.
Solution Approach 2:
By making compression power dynamic rather than static, the system can scale computational effort up or down based on real-time cost conditions. This dynamic approach allows the system to maintain high compression power when CPU resources are abundant and pricing is low, while reducing compression power when costs increase, thereby achieving both high productivity and adaptability.
3Device complexity
If existing compression systems are used without cost models, then device complexity is reduced, but resource management effectiveness deteriorates
Solution Approach 1:
The patent introduces a cost model intermediary layer that sits between the compression algorithms and the data stream. This intermediary evaluates computational costs, monitors CPU usage patterns, and makes intelligent decisions about compression parameter selection without requiring complex changes to the underlying compression algorithms themselves, thus maintaining relative system simplicity while dramatically improving resource management effectiveness.
Solution Approach 2:
The system performs self-service by automatically evaluating cost models and selecting optimal compression parameters without external intervention. The cost model autonomously monitors system state, assesses computational expenses, and adjusts compression settings accordingly, improving resource management effectiveness while avoiding the need for complex external control systems.
Data Source
AI summary
In various embodiments, the system and method described herein provide functionality for selecting an appropriate compression algorithm and settings given a cost model. Specifically, in selecting a compression method and configuration, the described system and method use a cost model to take into account the financial cost of a number of aspects of a particular compression scenario, including, but not limited to, the cost of performing the compression/decompression and the cost of storing the data. In this manner, intelligent trade-offs can be made between CPU/computing cost and data storage/transmission cost in an environment where a dollar amount can be associated with CPU processing time and storage/transmission volume. The described system and method can make such decisions dynamically, so that compression and/or decompression operations can respond to changing conditions on the fly, thus leading to better and more cost-effective management of resources.