Energy-Efficient Data Offloading using Data Access Strategy-Based Data Grouping Scheme

With technological advancements, the large volume of data generated every day is increasing exponentially. The primary source smart devices, Internet Things (IoT), and social communication network groups. This paved way for cloud computing to offer massive storage capacity powerful computational resources. conventional method processing in not scalable cannot meet requirements latency-critical applications. main problem arises when a single task requires multiple items stored different servers. Efficient placement minimizes access cost scheduling between globally distributed centres. Giving high priority this alarming issue, work aimed propose an efficient called Data Access Strategy based Grouping scheme (DAS-DGS). existing sets are centres using similarity measures, newly datasets also dynamically placed on appropriate servers cloud. Experimental simulations show that proposed meets objectives: effective energy-efficient IoT data, reduces cost, minimization time, average resource utilization, energy consumption rate. DAS-DGS shows promising result compared methods.