Logistic Migration Resource Shortest Distribution using Fairness Flashing Firefly Approach for Efficient Task Scheduling and Resource Allocation in the Cloud Environment

Cloud Computing (CC) is a large-scale platform supporting a variety of services, including computing, storage, and analysis over the Internet. It provides greater agility, scalability, and flexibility to users and organizations. The rapid growth of cloud users presents challenges in efficiently scheduling demanding workloads across available resources. Therefore, Task Scheduling (TS) is a key issue when establishing a CC system. The TS problem is crucial in CC environments, as the performance of the cloud relies primarily on efficient task scheduling. Also, it is a dynamic environment, and allocating resources efficiently and accurately can be a difficult process. Most of the previous methods don't meet efficient Resource Allocation (RA). Motivated by this, the paper presents the Logistic Migration Resource Shortest Distribution (LMRSD) approach based on the Fairness Flashing Firefly Approach (F3A) for effective task preparation and Cloud Environment (CE) in optimal resource. In this approach, the Minimum Maximum Task Processing Rate (M2TPR) method is employed to split the incoming task in the cloud. Based on the divided tasks fed into the Maximum Correlation Ranking analytics (MCRA), the algorithm utilizes ordering the tasks. Afterward, the proposed Fairness Flashing Firefly Approach (F3A) is used to schedule the task from ordered jobs. Finally, the LMRSD technique proficiently assigns the scheduled tasks to the Virtual Machines (VMs) with least response time in the CE. Thus, the proposed approach improves the efficiency of scheduling and resource scheduling performance with the least migration time performance than conventional methods.