An Adaptive Neuro-Fuzzy Inference System and Black Widow Optimization Approach for Optimal Resource Utilization and Task Scheduling in a Cloud Environment

With the enhancing demand of the cloud computing products, task scheduling issue has become the hot study topic in this area. The task scheduling issue of the cloud computing method is more difficult than the conventional distributed system. The majority of the previous scheduling schemes use virtual machine (VM) instances, which takes enormous start up time and requires the full resources to perform the tasks. The proposed approach utilizes an Adaptive Neuro-Fuzzy Inference System (ANFIS)-Black Widow Optimization (BWO) (ANFIS-BWO) method for establishing the proper VM for every task with less delay. Resource scheduling is another important objective for optimal usage of resources (servers) in the cloud environment. The BWO algorithm is used to obtain the best solution in the ANFIS scheme. The proposed approach can employ the VMs on the best server by the optimal scheduling scheme. The main aim of the proposed approach is to minimize the computational time, computational cost, and energy consumptions of the tasks with useful resource utilization. We describe that the proposed approach performs better than the existing approach concerning performance metrics such as computational time, makespan, energy consumption, computational cost, and resource utilization.

Automated summary

The paper presents a new method for task scheduling using ANFIS-BWO approach to reduce computational time, cost, and energy consumption, and optimize resource utilization. The study finds that the proposed approach outperforms existing methods in terms of performance metrics.