Dynamic Data Volume Placement for Storage Servers
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Solution Overview
Problem
In distributed computing systems, data volume placement can be less than ideal, leading to performance issues and increased risk of correlated failures due to factors like server health, utilization, and over-commitment, especially when servers are replaced or experience changes in workload.
Innovation Solution
A storage management system that monitors server health, utilization, and resource conditions to dynamically transfer data volumes to more suitable servers, ensuring optimal placement based on performance criteria, durability, and resource availability, while also considering premium performance resources and system diversity to mitigate risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data volumes are statically placed on servers, then placement simplicity is maintained, but system adaptability and performance optimization deteriorate when servers are replaced or workload changes
Solution Approach 1:
The patent implements dynamic data volume placement by continuously monitoring server health metrics, utilization levels, and performance indicators, then automatically relocating data volumes in response to changing conditions. This transforms the static placement system into a dynamic one that adapts to server replacements, workload changes, and performance degradation without manual intervention.
Solution Approach 2:
The system employs feedback mechanisms by monitoring server health, utilization, and performance metrics, then using this information to make intelligent placement decisions. The feedback loop continuously evaluates placement quality and triggers data volume transfers when suboptimal conditions are detected, enabling the system to self-optimize without external control.
2Reliability
If data volumes remain on failed servers, then data retention is maintained, but system reliability and availability deteriorate due to correlated failures and performance issues
Solution Approach 1:
The system performs preliminary actions by proactively transferring data volumes from servers showing signs of degradation or failure risk, before actual failures occur. By monitoring health metrics and utilization levels, the system anticipates potential problems and relocates data in advance, preventing correlated failures and maintaining system reliability.
Solution Approach 2:
The patent extracts data volumes from problematic servers and relocates them to healthy servers. This extraction principle allows the system to remove potentially failing data from at-risk servers while maintaining data availability, thereby improving system reliability without sacrificing productivity.
3Productivity
If servers are over-committed with data volumes, then resource utilization efficiency is improved, but system stability and performance deteriorate due to resource contention
Solution Approach 1:
The system dynamically changes placement parameters by adjusting which servers receive data volumes based on real-time monitoring of utilization levels, health metrics, and performance indicators. When servers approach over-commitment thresholds, the system automatically relocates data volumes to maintain optimal utilization levels, balancing resource efficiency with system stability.
Data Source
AI summary
A storage management system monitors an indicator of whether data storage is or will exceed a threshold of storage utilization as stored on a current implementation resource, such as a storage server. The indicator may be used to determine whether none, some or all of the data storage should be moved from the current implementation resource to an available implementation resource.