A Joint Adaptive Neuro-Fuzzy Inference System and Binary Quantum-Based Avian Navigation Algorithm for Optimal Resource Monitoring, Task Scheduling and Migration in Cloud-based System

A vast amount of data is generated daily from all aspects of individual lives due to the increasing proliferation of devices that are connected to the internet. These internet-connected gadgets lack the necessary capacity, resources, and storage to effectively process and retain large volumes of data with accuracy and reliability. Therefore, it is vital to regulate and process the unpredictable data created by a computing paradigm with robust resource specifications. Cloud computing has been considered an appealing solution for effectively analyzing and storing this data within a specific timeframe. Furthermore, the existence of multiple conflicting factors and the classification of the problem as NP-hard make resource management and task consolidation major obstacles in Cloud-based systems. This research suggests a hybrid Resource Monitoring, Task Scheduling and Migration technique that combines an Adaptive Neuro-Fuzzy Inference System (ANFIS) with a Binary Quantum-based Avian Navigation Optimizer technique (BQANA) to tackle these challenges. The BQANA algorithm is utilized to enhance the control parameters of the ANFIS system. Additionally, a load balancing technique is proposed to provide an even distribution of workloads among Cloud Virtual Machines (VMs) to optimize resource management. Furthermore, a task migration strategy has also been adopted to offload the overloaded tasks to under-utilized VMs. The proposed approach presented in this study has been thoroughly validated using extensive simulations on real-world benchmark datasets, specifically for Quality of Service (QoS) characteristics. The simulation results demonstrate that the proposed methodology outperforms previous methods with regard to makespan, resource utilization, response time, energy consumption, and load balancing, with respective enhancements of 24.7%, 15.4%, 16.9%, 4.51%, and 23.4%.