Storage Array Compression Control for Energy-Aware Data Reduction
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Solution Overview
Problem
Data compression in storage arrays is resource-intensive, requiring significant time and energy, especially at higher compression ratios, without considering the availability and cost of energy sources.
Innovation Solution
Implementing machine learning models to predict compression times and energy costs based on historical data, weather conditions, and renewable energy production, allowing for energy-aware data compression decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is compressed at higher compression ratios, then storage space is conserved better, but time and energy costs increase excessively
Solution Approach 1:
The patent applies dynamics by making the compression ratio adjustable and adaptive rather than fixed. The system dynamically selects compression ratios based on real-time energy cost signals, allowing the compression process to adapt its intensity to current energy pricing conditions. This resolves the contradiction by enabling the system to operate at high compression ratios when energy is cheap and at lower ratios when energy is expensive, optimizing both storage efficiency and energy cost.
Solution Approach 2:
The patent changes the parameter of compression ratio based on energy cost conditions. By varying this critical parameter according to external energy pricing signals, the system can achieve better storage compression when energy is abundant/cheap while avoiding excessive energy consumption when prices are high. This parameter adaptation directly addresses the trade-off between storage efficiency and energy expenditure.
2Quantity of substance
If data is compressed at higher compression ratios, then storage space is conserved better, but compression time increases
Solution Approach 1:
The system dynamically adjusts compression time allocation based on energy cost signals. When energy costs are low, the system can afford to spend more time on high-ratio compression to maximize storage efficiency. When energy costs are high, the system reduces compression time and operates at lower compression ratios, accepting slightly larger file sizes in exchange for energy savings. This dynamic time adjustment resolves the contradiction between storage efficiency and compression time.
Solution Approach 2:
The patent changes the compression ratio parameter in response to energy cost conditions, which directly affects compression time. By lowering the compression ratio when energy costs are high, the system automatically reduces the computational time required. This parameter adaptation enables the system to balance storage efficiency gains against the time cost of compression operations.
3Quantity of substance
If compression operations are performed regularly to save storage space, then storage requirements are reduced, but energy consumption increases
Solution Approach 1:
The patent implements feedback by continuously monitoring energy cost signals and using this information to control compression operations. The system receives feedback about current energy pricing conditions and adjusts its compression behavior accordingly. This feedback loop enables the system to perform compression operations strategically - intensifying them when energy is cheap and reducing them when energy is expensive - thereby minimizing total energy consumption while still achieving storage space savings over time.
Solution Approach 2:
The system employs periodic compression operations rather than continuous compression, timing these operations to coincide with periods of low energy costs. By scheduling compression tasks periodically based on energy price cycles, the system achieves storage space reduction while concentrating energy consumption during favorable pricing periods, thus reducing overall energy loss compared to continuous compression.
Data Source
AI summary
Energy aware data compression is disclosed. A storage array may receive data from a client to be compressed. The storage array may request information from a compression awareness engine that is configured to estimate compression times in the context of energy source and energy cost. The storage array makes a decision to compress the data based on the estimates or response received from the compression awareness engine. The data is then compressed and stored in the storage array.