Dynamic Storage Class Memory Profiling via AI Utilization Prediction
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Solution Overview
Problem
Current storage class memory (SCM) configurations lack dynamic adaptability to changing workloads and future utilization patterns, leading to inefficiencies in resource allocation and performance optimization.
Innovation Solution
A computer-implemented method that retrieves historical and real-time data on SCM device utilization, functional properties, and customer needs, combined with artificial intelligence-predicted future utilization trajectories, to dynamically configure SCM devices with optimized partitions based on predefined profiles, allowing for flexible configurations such as SCM, DRaaS, and NAS partitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static SCM configurations are used, then device complexity is reduced and ease of operation is improved, but adaptability to changing workloads deteriorates and resource allocation efficiency worsens
Solution Approach 1:
The patent implements dynamic configuration of SCM devices by continuously monitoring utilization metrics and automatically adjusting partition allocations based on current workload conditions and predicted future demands, transforming static configurations into adaptive, self-adjusting systems
Solution Approach 2:
The system establishes closed-loop feedback mechanisms by monitoring SCM device utilization in real-time, comparing actual performance against targets, and automatically adjusting configurations based on this feedback to maintain optimal adaptability without manual intervention
2Productivity
If manual SCM configuration is used, then automation extent is reduced and ease of operation is improved, but productivity deteriorates and resource allocation efficiency worsens
Solution Approach 1:
The SCM configuration system performs self-service by automatically monitoring its own utilization metrics, predicting future demands using AI models, and adjusting its own partition configurations without external intervention, thereby maximizing both productivity and automation extent
Solution Approach 2:
The system performs preliminary actions by using AI models to predict future SCM utilization patterns and proactively adjusting configurations in advance of actual demand changes, improving resource allocation efficiency before workload shifts occur
3Adaptability or versatility
If fixed partition allocations are used, then device complexity is reduced and ease of operation is improved, but adaptability to different storage needs deteriorates and loss of time in reconfiguration worsens
Solution Approach 1:
The patent implements dynamic partition allocation where SCM device partitions are continuously adjusted based on real-time utilization monitoring and predicted future demands, allowing flexible reconfiguration without manual intervention and eliminating reconfiguration downtime
Solution Approach 2:
The system performs preliminary reconfiguration actions by using AI predictions to anticipate future storage needs and proactively adjusting partition allocations before actual demand changes occur, thereby maintaining adaptability without incurring reconfiguration time losses
Data Source
AI summary
Configuration and dynamic profiling of storage class memory (SCM) devices is provided. Information is retrieved that includes historical SCM device configurations, historical SCM device utilization, functional and non-functional properties of a plurality of SCM devices on a host node, current real time utilization of the plurality of SCM devices by an application workload of a customer running on the host node, and relationships between the plurality of SCM devices, needs of the customer, and resource capabilities and real time resource utilization on the host node. A configuration of each respective SCM device is determined based on retrieved information and an artificial intelligence-predicted SCM device future utilization trajectory of the customer. Each respective SCM device is dynamically configured with a set of SCM device partitions according to a corresponding SCM device profile based on the determined configuration of each respective SCM device of the plurality of SCM devices.