Backup Client Allocation via Neural Network Reward Scoring
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Solution Overview
Problem
In large-scale backup systems, manual allocation of backup clients to servers is inefficient, leading to poor performance, high management overhead, and frequent re-definition of allocations due to network changes and hardware updates, resulting in duplicated jobs.
Innovation Solution
A method using a neural network to determine a reward score based on the backup system's state, which then adjusts the allocation of backup clients to servers in real time, optimizing performance and reducing management overhead by automatically configuring the allocation of backup clients to backup servers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual allocation of backup clients to servers is used, then system simplicity is maintained, but management overhead increases and allocation efficiency deteriorates
Solution Approach 1:
The system employs reinforcement learning agents that automatically perform allocation decisions without human intervention. The agents learn optimal allocation strategies through continuous interaction with the environment, receiving rewards for successful allocations and penalties for failures, enabling the system to self-optimize allocation configurations
Solution Approach 2:
The allocation configuration is made dynamic and adaptable through machine learning models that continuously learn from system states and performance outcomes. The system can automatically adjust allocation strategies based on changing conditions, transforming the static manual allocation process into a dynamic self-optimizing system
2Adaptability or versatility
If frequent re-definition of client allocations is performed to adapt to network changes and hardware updates, then system adaptability improves, but duplicated jobs increase and system stability deteriorates
Solution Approach 1:
The reinforcement learning system implements continuous feedback loops where allocation outcomes are monitored and fed back to the learning agents. This feedback mechanism enables the system to learn from past allocation decisions and adjust future allocations to avoid duplicated jobs while maintaining adaptability to network and hardware changes
Solution Approach 2:
The system performs preliminary learning and exploration during training phases to establish optimal allocation patterns before actual deployment. This preliminary action allows the system to pre-adapt to various scenarios, reducing the need for frequent re-definition operations during production and thereby improving stability
3Ease of operation
If automated neural network-based allocation is implemented, then management overhead is reduced and allocation optimization improves, but system complexity increases
Solution Approach 1:
The patent replaces manual mechanical allocation processes with automated neural network-based decision-making systems. The neural networks learn complex allocation patterns and make decisions automatically, substituting human operators and simplifying the ease of operation despite the underlying computational complexity being managed through software rather than manual processes
Data Source
AI summary
Embodiments of the present disclosure provide a method, device, and computer program product for managing a backup system. The method comprises obtaining a state of a backup system, wherein the backup system comprises a plurality of backup servers and a plurality of backup clients, the plurality of backup servers is communicatively coupled to the plurality of backup clients via a network, and wherein at least one backup server from the plurality of backup servers is configured to back up data of at least one backup client allocated from the plurality of backup clients to the at least one backup server, determining a reward score corresponding to the state of the backup system and, determining, based on the state of the backup system and the reward score, configuration information for the backup system, the configuration information indicating allocation of the plurality of backup clients to the plurality of backup servers.