Storage Capacity Forecasting With Workload-Based Cost Visualization
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Solution Overview
Problem
Large-scale distributed storage systems face challenges in efficiently managing increasing data volumes and complexity, particularly in data centers, due to inefficiencies in storage management and data handling processes, leading to suboptimal performance and resource utilization.
Innovation Solution
Implementing a direct-mapped flash storage system that addresses data blocks directly without intermediate translation by storage controllers, combined with zoned storage devices and separate management of storage drives by higher-level operating systems to optimize data operations and reduce redundant processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional storage controllers translate and manage data blocks, then storage management is centralized, but system complexity increases and performance decreases
Solution Approach 1:
The patent extracts the data block translation function from the storage controller and implements it directly at the storage device level. Each storage device independently manages its own data blocks without requiring intermediary translation by centralized controllers, thereby reducing system complexity while maintaining efficient storage operations.
Solution Approach 2:
Storage devices perform self-management of data blocks through direct mapping capabilities. The devices independently handle translation and management operations without relying on external storage controllers, enabling autonomous operation that reduces overall system complexity while improving productivity.
2Reliability
If multiple storage controllers manage data blocks, then centralized management is achieved, but redundant processes increase and performance decreases
Solution Approach 1:
The patent removes redundant translation processes by eliminating multiple storage controllers that performed centralized management. Data block management is extracted to the storage device level, where each device independently manages its own blocks without creating redundant processing layers, thereby reducing computational resource consumption while maintaining reliability.
Solution Approach 2:
Storage devices independently manage their own data blocks through direct mapping, eliminating the need for multiple storage controllers to perform redundant management tasks. This self-service approach reduces computational resource consumption while preserving system reliability through distributed autonomous management.
3Ease of operation
If storage controllers translate data blocks, then data management is simplified, but write operations increase and efficiency decreases
Solution Approach 1:
The patent extracts the data block translation function from centralized storage controllers to the storage device level. This allows devices to directly manage their own data blocks without intermediary translation, reducing the number of write operations required while maintaining simple data management through direct mapping at the device level.
Solution Approach 2:
Storage devices perform self-management of data blocks through direct mapping capabilities, eliminating the need for storage controllers to translate data blocks. This self-service approach reduces write operation frequency and improves efficiency while maintaining ease of operation through autonomous device-level management.
Data Source
AI summary
Systems and methods for generating visualizations for storage capacity utilization estimates and storage cost estimates are provided. The method include receiving a first set of parameters associated with user workload characteristics, determining capacity utilization estimates for a set of time intervals based on the first plurality of parameters, receiving a second set of parameters associated with a configuration of storage resources, determining cost estimates for the set of time intervals based on the second set of parameters, and generating a visualization of the capacity utilization estimates for the set of time intervals and the cost estimates for the set of time intervals.