CLOUD load balancing for storing the internet of things using deep load balancer with enhanced security

Abstract

The traditional load balancing some disadvantages are (1) reduction in the wait times, (2) Storing encrypted data in a location is difficult. To solve this issues proposed work introduced a Deep Load Balancer (DLB) use of cloud load balancing for storing the Internet of Things (IoT). The work analyzed the drawbacks of traditional load balancing algorithms and proposes the use of DLB as an improved approach for balancing the load of IoT devices in a cloud environment. Proposed DLB strategy is the process of following steps are (i) normalizing and standardizing the attributes of the resources that are being managed. This step involves (ii) Allocating and optimizing constrained resources using a DLB. (iii) Succeeding in minimizing delay. The advantages of DLB for IoT storage are scalability, cost efficiency, and security is effectively discussed. Finally, DLB approach is which leverages the large amounts of data generated by IoT devices. Furthermore, a deep learning model is proposed for predicting the load balancing efficiency of DLB in a cloud environment. Finally, the experimental results are analyzed and the implications of the proposed approach are clearly discussed. The performance evaluation metrics are Response Time (RT), Makespan (MS), Associated Overhead (AO) and Migration Time (MT) of the proposed work DLB compared with existing techniques are TA, ESCE and TA + ESCE. The results demonstrated that DLB is a promising technique for cloud load balancing of IoT devices.