Dingo optimization based network bandwidth selection to reduce processing time during data upload and access from cloud by user

Big data processing is considered as significant because a massive amount of data is generated due to the rapid usage of the internet by people all over the globe. Cloud computing technology is recently attracted many users of massive data. However, the cloud system experiences enormous time for storing and accessing this huge volume of data. So, many researchers are processing to reduce the amount of data and Time. So, for attaining reduced processing time in cloud an efficient data uploading and data accessing process is proposed in this current research. During the data uploading process, multiple users is processed using the RAM chunking technique to split the data into small chunks and separate blocks are created to store this chunked data. The process of indexing is carried out using multilevel indexing to create index attributes for the data. This entire processed data is stored in cloud. During the data accessing process, to achieve fast accessing of data from the cloud the selection of optimal bandwidth is done using the dingo optimization algorithm. The optimal bandwidth selected in this current research for fast data accessing is 97 Gbps. Simulation analysis is carried out on the proposed model, and some of the metrics like execution time, data uploading time, data processing time, and data transfer rate obtained for the proposed cloud model are 6.14 ms, 203 ms, 2.06 ms and 61.09 Mbps. Analysis suggests that reduced processing time is achieved in cloud using the proposed model during data uploading and accessing.