Cloud Computing Storage Backup and Recovery Strategy Based on Secure IoT and Spark

Abstract

Spatial data occupies a large proportion of the large amount of data that is constantly emerging, but a large amount of spatial data cannot be directly understood by people. Even a highly configured stand-alone computing device can hardly meet the needs of visualization processing. In order to protect the security of data and facilitate for users the search for data and recover by mistake, this paper conducts a research on cloud computing storage backup and recovery strategies based on the secure Internet of Things and Spark platform. In the method part, this article introduces the security Internet of Things, Spark, and cloud computing backup and recovery related content and proposes cluster analysis and Ullman two algorithms. In the experimental part, this article explains the experimental environment and experimental objects and designs an experiment for data recovery. In the analysis part, this article analyzes the challenge-response-verification framework, the number of data packets, the cost of calculation and communication, the choice of Spark method, the throughput of different platforms, and the iteration and cache analysis. The experimental results show that the loss rate of database 1 in the fourth node is 0.4%, 2.4%, 1.6%, and 3.2% and the loss rate of each node is less than 5%, indicating that the system can respond to applications.

Data Recovery and Backup management: A Cloud Computing Impact

“…As a result, based on use-based pricing, the cost of a cloud server that is rarely fully installed would be relatively low. Cloud service providers can deal with many customers if customers need only I/O power or computational services randomly [13]- [16]. This allows for efficient multiplexing.…”

“…The challenge-response-verification framework, the number of data packets, the cost of computation and communication, the selection of the Spark method, the throughput of various platforms, and the iteration and cache analysis are all examined in the analysis section of this paper. The testing findings demonstrate that each node's loss rate is less than 5% and that database 1's loss rate in the fourth node is 0.4%, 2.4%, 1.6%, and 3.2%, demonstrating the system's ability to handle applications [16]. Alzahrani et al [17] discussed an architecture that uses a hybrid method to combine the benefits of replication and erasure coding to achieve the best storage solution, emphasizing reliability and recovery.…”

“…Compared to traditional storage, the new characteristics of cloud storage have given rise to several new security concerns, including the requirement to guarantee the privacy and security of stored data and its availability and integrity [16], [20]- [22]. Some common data recovery approaches include the Seed Block Algorithm Architecture, Parity Cloud Services, and Bloom Filter [13].…”

A Secured Database Monitoring Method to Improve Data Backup and Recovery Operations in Cloud Computing

“…This happens for various reasons. This is best explained with an example [30]. A backup system is a service subsystem of a data center and has the following features:…”

Data Recovery Technology Based on Subspace Clustering

“…Data recovery and clustering are of great signi cance to the analysis of high-dimensional data [9][10][11][12][13][14]. Many highdimensional data usually exist approximately in low-dimensional subspaces, and the low rank prior of data becomes the key to e ective data recovery [15][16][17][18].…”