Reinforcement Learning Data Deduplication Resource Optimization
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Solution Overview
Problem
Existing data deduplication methods in distributed computing systems face challenges in optimizing computing resource utilization due to unpredictable data characteristics and resource contention, leading to suboptimal deduplication strategies that may incur high CPU utilization and network latency.
Innovation Solution
The implementation of reinforcement learning to model data deduplication as a multi-armed bandit problem, allowing the system to dynamically determine the optimal set of actions for deduplication, such as compressing, hashing, and coalescing data, based on real-time computing resource availability and user-specifiable allocations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data deduplication is performed with high compression and extensive hashing, then storage utilization is improved, but CPU utilization increases and computing resources are consumed
Solution Approach 1:
The patent applies dynamic adjustment of deduplication parameters (sequence size, compression level, hashing intensity) based on real-time computing resource availability. The system continuously monitors CPU utilization and adapts the deduplication strategy accordingly, transitioning between aggressive deduplication when resources are abundant and lighter processing when resources are constrained, thereby resolving the contradiction between storage optimization and CPU consumption
Solution Approach 2:
The system changes key parameters of the deduplication process dynamically - adjusting sequence size, compression ratios, and hashing depth based on current system state. By modifying these parameters in response to resource availability, the system achieves high storage utilization when possible while preventing excessive CPU utilization, directly addressing the technical contradiction
2Productivity
If larger sequences are used for deduplication, then deduplication database size is reduced and processing is faster, but the rate of matches decreases
Solution Approach 1:
The patent implements dynamic adjustment of sequence size based on data characteristics and resource constraints. The system selects optimal sequence lengths adaptively - using larger sequences when speed is prioritized and smaller sequences when match accuracy is more important, thereby balancing deduplication speed against match rate according to current operational requirements
3Quantity of substance
If aggressive deduplication is applied, then storage efficiency is improved, but network latency increases due to processing overhead
Solution Approach 1:
The system dynamically adjusts the aggressiveness of deduplication processing based on network conditions and resource availability. When network latency is a concern, the system reduces processing intensity to minimize latency, while maintaining storage efficiency through adaptive parameter selection. This dynamic balancing resolves the contradiction between storage efficiency and network latency
Data Source
AI summary
Systems and methods provide for optimizing data deduplication. A physical storage node can determine a cost of performing each exclusive action of a set of exclusive actions associated with data deduplication for a predetermined number of epochs to populate an exclusive action cost matrix. The node can select an optimal exclusive action from among the set of exclusive actions based on the cost of performing each exclusive action. In response to a random number being below a threshold value, the node can determine a first cost of performing a randomly selected exclusive action from the set of exclusive actions and update the exclusive action cost matrix with the first cost. In response to the random number exceeding the threshold value, the node can determine a second cost of performing the optimal exclusive action and update the exclusive action cost matrix with the second cost.