Wear-Leveling Data Management for Compute Node Asset Placement
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Solution Overview
Problem
The increasing amount of data in enterprises poses a challenge for efficient asset placement management in shared pools of configurable computing resources, particularly due to hardware failure risks and resource imbalances, which can lead to early hardware failure and inefficient resource distribution.
Innovation Solution
The solution involves monitoring bus traffic data to determine wear-leveling data for compute nodes, which is then used to manage asset placement, prioritizing assets on hosts based on their usage history and resource needs, thereby reducing the risk of hardware failure and optimizing resource distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If assets are placed on hosts without considering wear-leveling data, then asset placement is simple and fast, but hardware failure risk increases and resource distribution becomes unbalanced
Solution Approach 1:
The system performs preliminary wear-leveling data collection and analysis before asset placement decisions are made. Bus traffic data is monitored and wear-leveling scores are calculated in advance, allowing the asset placement system to select optimal hosts based on pre-computed wear levels rather than making decisions without this critical information.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring bus traffic data and updating wear-leveling scores based on actual usage patterns. This feedback loop allows the system to adapt asset placement decisions to current system states, placing assets on hosts with lower wear levels while preventing any single host from becoming overly utilized.
2Duration of action of stationary object
If wear-leveling data is collected and analyzed, then resource distribution is optimized and hardware service life is prolonged, but monitoring and processing overhead increases
Solution Approach 1:
The system merges the wear-leveling data collection process with existing bus traffic monitoring infrastructure. By utilizing already-available bus traffic data for wear calculation rather than implementing separate monitoring mechanisms, the system reduces redundant overhead while still achieving comprehensive wear-leveling information for all hosts.
Solution Approach 2:
The system changes the parameter of wear measurement from direct hardware usage counts to derived bus traffic metrics. By calculating wear-leveling scores based on bus traffic patterns rather than directly counting individual hardware operations, the system achieves accurate wear assessment with reduced processing requirements.
3Productivity
If asset placement is optimized based on wear-leveling data, then resource distribution improves and hardware failure risk reduces, but placement decision time increases
Solution Approach 1:
Wear-leveling scores are calculated and updated in advance based on monitored bus traffic data, so that when asset placement decisions are required, the system can quickly query pre-computed scores rather than performing complex analysis in real-time. This preliminary preparation significantly reduces placement decision time while maintaining optimization quality.
Solution Approach 2:
The system performs wear-leveling calculations at appropriate intervals rather than continuously, updating placement decisions only when necessary. This partial action approach avoids excessive processing while still maintaining effective resource distribution by refreshing wear data at frequencies sufficient for optimal placement without unnecessary overhead.
Data Source
AI summary
Disclosed aspects include managing a set of wear-leveling data for a set of compute nodes. A set of bus traffic data may be monitored with respect to a bus which is connected to a computer hardware component of the set of compute nodes. In response to monitoring the set of bus traffic, the set of wear-leveling data may be determined using the set of bus traffic. The wear-leveling data determined using the set of bus traffic may then be established in a data store. The wear leveling data may be used to manage asset placement with respect to a shared pool of configurable computing resources.