Storage Space Optimization via Memory Block Clustering
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Solution Overview
Problem
Existing storage backup methods in enterprise databases are inefficient, leading to excessive storage space utilization, increased latency, and unoptimized storage management due to the use of multiple Logical Unit Numbers (LUNs for each backup volume, resulting in scalability limitations and human intervention requirements for storage infrastructure management.
Innovation Solution
A method and system that utilize a memory management unit to measure operational parameters, estimate storage capacity, cluster memory blocks based on capacity, and rank clusters for optimized storage operations, dynamically managing storage space complexity through machine learning techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If different sets of LUNs are provided to every volume of the storage backup unit, then each backup volume can be provided with storage capacity, but the LUNs and corresponding storage space get filled up excessively and require additional device investments
Solution Approach 1:
The storage backup unit is divided into multiple memory blocks, each further segmented into clusters. This hierarchical segmentation allows fine-grained management of storage space, enabling the system to allocate storage efficiently without filling up entire LUNs unnecessarily. The segmentation principle resolves the contradiction by breaking down the monolithic storage allocation into manageable units that can be dynamically controlled.
Solution Approach 2:
The system dynamically estimates storage capacity requirements for future memory operations and adapts cluster selection accordingly. Instead of static allocation, the memory management unit continuously monitors operational parameters and adjusts storage allocation in real-time. This dynamic approach prevents premature filling of LUNs while ensuring adequate storage capacity is available when needed.
2Quantity of substance
If different sets of LUNs are provided to every volume of the storage backup unit, then storage capacity is allocated, but latency in memory operations increases
Solution Approach 1:
The system performs preliminary estimation of storage capacity requirements before executing memory operations. By predicting future storage needs based on operational parameters and selecting appropriate clusters in advance, the system avoids delays during actual data operations. This preliminary action eliminates wait times that would otherwise occur during runtime allocation decisions.
Solution Approach 2:
The memory management unit continuously monitors operational parameters such as storage usage patterns and performance metrics, using this feedback to optimize cluster selection. The system learns from past operations and adjusts its storage allocation strategy to minimize latency. This closed-loop feedback mechanism ensures that storage operations are performed efficiently without unnecessary delays.
3Quantity of substance
If traditional storage backup methods are used, then storage space is allocated, but imbalance in utilization of LUNs results in unoptimized storage space management
Solution Approach 1:
The memory management unit autonomously performs storage capacity estimation, cluster selection, and optimization without requiring human intervention. The system self-manages storage allocation by analyzing operational parameters and automatically adjusting cluster usage to maintain balance across LUNs. This self-service capability eliminates the need for manual storage management while optimizing space utilization continuously.
Solution Approach 2:
The system changes storage allocation parameters dynamically based on monitored operational conditions. By adjusting cluster selection criteria and storage capacity estimates in response to changing workloads, the system maintains optimal utilization balance across all LUNs. This parameter adaptation prevents both over-allocation and under-utilization, maximizing storage management efficiency.
4Quantity of substance
If traditional storage backup methods are used, then storage capacity is consumed, but scalability of the storage backup units is limited
Solution Approach 1:
The hierarchical segmentation of storage into memory blocks and clusters enables modular scalability. New storage capacity can be added by introducing additional memory blocks or expanding existing clusters, without disrupting the overall storage architecture. This segmented structure makes the storage backup unit highly scalable and adaptable to growing storage requirements.
Solution Approach 2:
The memory management unit implements a universal cluster selection mechanism that can handle diverse storage operations and workload types. The same cluster management infrastructure serves multiple functions including capacity estimation, allocation, and optimization across different storage scenarios. This multi-functional approach enhances the adaptability and scalability of the storage system to various enterprise database environments.
Data Source
AI summary
Disclosed herein is method and system for managing storage space complexity in a storage unit. In an embodiment, operational parameters related to memory operations and storage parameters related to memory blocks of the storage unit are analyzed to estimate storage capacity of each of the memory blocks. Subsequently, the memory blocks are clustered into plurality of clusters based on the storage capacity. Further, one or more of the plurality of clusters are selected for performing future memory operations based on ranking of the plurality of clusters. In some embodiments, the present disclosure helps in dynamically managing storage space complexity in the storage unit and optimizes the storage space utilization. Also, the present disclosure automatically handles storage volumes, thereby reducing latency in memory backup operations and reducing amount of buffer/cache memory required.