Cloud-Based Dynamic Task Scheduling and Load Balancing Using a Variety of Intelligent Schemes

Abstract

One of the main problems with cloud computing is load balancing, which divides tasks into virtual machines (VMs) with varying lengths and completion times. As a result, keeping VMs loaded up during job scheduling and allocation is difficult. In this research, we suggested secure load balancing in a cloud context and resource-aware dynamic job scheduling utilizing intelligent algorithms. The process for the planned work is as follows: (1). The Human Mental Search (HMS) method is used to cluster Real-Time and Non-Real Time jobs into four categories: Real-Time and Short, Real-Time and Long, Non-Real Time and Short, and Non-Real Time and Long. (2). Knowledge of Energy and Delay Combined Tasks Tasks are planned based on task clustering, and the fuzzy VIKOR method is used to schedule them in the order of priority. (3). Security Risks aware VM Clustering, which employs the Hierarchical Agglomerative Clustering algorithm, consists of three distinct entities: a VM's information collector, which compiles data about VMs; a load monitor, which oversees the entire datacenter; and a decision maker, which assigns tasks by choosing the best VM. (4). Co-Resident assaults were stopped by the continuous-Actor-Critical-Algorithm (C- ACAL), which is used to pick the best VM. Using CloudSim, which measures performance in terms of response time, latency, resource utilization, throughput, energy consumption, and task execution success rate, the simulation is carried out.