Design And Implemention Of A Big Data Storage Space Optimization System Oriented To Cloud Platform

With the popularization of massive high-speed mobile Internet devices,data storage has shown explosive growth.Traditional storage solutions cannot meet the complex,diverse,and large-scale new storage requirements.The massive data going to the cloud brings a full range of challenges to the storage system in the cloud platform.Existing cloud storage solutions still have shortcomings in adapting to the diversity of system architectures and reducing redundant data in storage space,resulting in a large waste of storage space resources,which in turn increases the total cost of ownership of data in the cloud platform(storage,acquisition,and data migration cost).Therefore,this article will research two aspects of database file storage and block storage provided in the cloud platform.First of all,this article studies the database file infrastructure stored in the cloud platform,conducts an in-depth analysis of the current database read-write separation architecture,starts with the content characteristics of the database files stored in the cloud platform,and proposes a database file storage optimization strategy.Next,the data request of the write-only instance of the database hosted on the cloud platform is aggregated with the system workload.A high-relevance mode access data compression method is proposed to compress the data stored in the write-only instance,improving data storage efficiency.Then optimize the database file backup storage in the cloud platform to improve the data backup speed and storage space utilization.The method was validated using data in real enterprise scenarios.The experimental results show that compared with the or iginal method,the proposed method has improved storage space utilization and data file backup time to varying degrees.Secondly,redundant big data deduplication is an essential part of cloud platform storage space optimization.This article finds that th e current data deduplication method forms data block fingerprints by dividing data files into data blocks and looking for the only data block in the stored data.The influence of low-entropy strings in the block storage space on data deduplication is ignor ed.This paper proposes a block storage space optimization method,which uses local extreme values as data block cut-points.It uses the internal characteristics of low-entropy strings for effective identification,which improves the deduplication rate.On this basis,the redundant detection is optimized by using the repeated location attribute of the redundant data block.The proposed method is verified on the public data set and the data set in the actual enterprise scene.The experimental results show that the method proposed in this paper has a better deduplication rate and deduplication throughput rate than the current method.The significant improvement proves its application ability in actual scenarios.Finally,based on the research results of database file storage optimization strategy research and block storage space research,combined with actual application scenario requirements,this paper designs and implements a cloud platform-oriented extensive data storage space optimization system.The sys tem includes storage space optimization functions,which effectively improve the utilization of data storage space and provides intelligent operation and maintenance services,which verifies the feasibility of the research content in this article from practical applications.