Predictive Hash Table Memory Allocation for CO2 Reduction
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Solution Overview
Problem
Current data storage systems face inefficiencies in memory allocation for fingerprint hash tables, leading to unnecessary power consumption and greenhouse gas emissions due to over-allocation of memory resources, as they rely on static reservations rather than predictive optimization based on deduplication workload metrics.
Innovation Solution
A predictive hash table and CO2 minimizing system uses a trained time series forecasting engine to predict the optimal amount of memory needed for fingerprint hash tables, adjusting reservations based on predicted deduplication processes and hash table sizes, and incorporates user-defined thresholds to manage memory allocation dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by stationary object
If static memory reservation is used for fingerprint hash tables, then memory allocation is simple and stable, but memory resources are over-allocated leading to unnecessary power consumption and CO2 emissions
Solution Approach 1:
The patent implements dynamic memory allocation for fingerprint hash tables by using a machine learning model to predict future hash table sizes based on historical data. The system continuously adjusts memory reservations according to predicted workload, transforming the static allocation approach into a dynamic one that adapts to changing conditions, thereby reducing power consumption while maintaining system reliability
Solution Approach 2:
The system changes the parameter of memory allocation from fixed to variable by incorporating predictions from a machine learning model. The memory reservation is adjusted based on predicted deduplication workload and hash table size, allowing the system to optimize power consumption by allocating only the necessary memory resources rather than maintaining static over-provisioning
2Productivity
If predictive optimization is implemented, then memory allocation efficiency improves and power consumption decreases, but system complexity increases due to forecasting engine
Solution Approach 1:
The patent applies preliminary action by using a machine learning forecasting engine to predict future fingerprint hash table sizes and deduplication workload before they occur. The system proactively allocates memory based on these predictions, allowing optimization of power consumption in advance rather than reacting to actual usage patterns, thereby improving memory allocation efficiency
Solution Approach 2:
The system implements self-service through automated machine learning-based predictions and dynamic memory adjustment. The forecasting engine autonomously analyzes historical data, predicts future requirements, and triggers memory reallocation without manual intervention, reducing the need for complex manual management while improving allocation efficiency
3Reliability
If over-allocation of memory resources is performed, then system reliability is maintained under varying workloads, but unnecessary power consumption and CO2 emissions occur
Solution Approach 1:
The system changes memory allocation from static over-provisioning to dynamic prediction-based allocation. By using a machine learning model to forecast hash table sizes, the system maintains reliability by allocating sufficient memory when needed while avoiding energy waste by reducing allocations during low-utilization periods, thus optimizing the trade-off between reliability and energy consumption
Data Source
AI summary
An information handling system executing a predictive hash table and carbon dioxide (CO2) minimizing system may comprise a network interface device to receive from a remote data center deduplication process workload metrics, fingerprint hash table size metrics, and a current reserved memory size allocated for storage of a fingerprint hash table, a processor executing code instructions to predict a future workload of deduplication processes and a future fingerprint hash table size to determine a predicted amount of memory to reserve for storage of a future fingerprint hash table based on the predicted future workload of deduplication processes and fingerprint hash table size, and to determine an over-allocated portion of the current reserved memory size where the network interface device transmits an instruction to the data center to limit power provided to the over-allocated portion of the current reserved memory size.