Research On Heat Partition Strategy And Optimization Algorithm Of IoT Data Storage

The development of Internet of Things(Io T)has brought about the widespread application of Io T sensors,followed by the challenge of storing massive Io T data.In the practical application of computer systems,there is a phenomenon of locality of reference to data,that is,specific data will be accessed collectively in a period of time.Data can be divided into "hot data" and "cold data".Hot data refers to data that is accessed more frequently in a period of time,while cold data refers to data that is accessed less frequently in a period of time.The advancement of storage technology has brought different options for data storage.For massive Io T data storage,different storage media can be used to store data with different heat characteristics based on data heat characteristics.Hot data often requires fast response to system requirements and requires high performance storage media,so high-performance storage media such as solid-state drives can be used for storage.Cold data often requires little or no response to system requirements and has low performance requirements for storage media.Therefore,relatively low performance storage media such as mechanical hard drives can be used for storage.In practical applications,traditional cold and hot data partitioning strategies based on cache replacement algorithms cannot accurately reflect the cold and hot data situation.Faced with storage media with varying performance,it is difficult to provide accurate basis for subsequent data scheduling decisions through simple cold and hot partitioning.This paper mainly conducts the following three studies:(1)Analyze the business background and combine it with the rules of business data access,design a data heat quantification method using a time decay model,quantify the heat and cold situation of the data through numbers,provide a more accurate and visible data heat situation,and ultimately construct a temporal heat data record for the data generated by specific sensors.(2)Analyze the characteristics of temporal heat data and the advantages of different deep learning networks,a prediction model for multi feature long temporal data is constructed based on CNN-GRU combined with attention mechanism.Through experimental testing and comparison,it is concluded that the prediction error is reduced by 10.5% compared to models such as CNN-GRU.(3)A two-stage data scheduling strategy is proposed by combining data heat quantification methods and heat data prediction results.In the first stage,a heat based data replacement algorithm is used to improve data access hit rate,and in the second stage,a genetic algorithm is used to achieve comprehensive optimization of data storage and transmission costs.In addition,based on the above research results,this article designs and implements an Io T data storage and management system,detailing the design and implementation of three main modules: data heat calculation module,data heat prediction module,and data scheduling module,and preliminarily demonstrating the operational effectiveness of the system.