User Task Priority Based Resource Allocation with Multi Class Task Scheduling Strategy and Load Balancing in Cloud Environment

An effective task scheduling method can accommodate user needs, boost resource usage, and boost cloud computing's overall efficiency. However, the unchanging task needs are generally the focus of grid computing's job scheduling, leading to low resource usage. Distributing the dynamic user tasks fairly among all cloud nodes is the goal of load balancing, a relatively new field of study. The primary difficulty with cloud computing is load balancing. By making better use of available resources, load balancing methods improve cloud performance. Load balancing primary goal is to lessen the burden on the environment by cutting down on energy use and carbon emissions. The most crucial characteristics that can both satisfy user needs and maximize resource utilization are used to determine the order of priorities. Existing systems often ignore user priority suggestions in favor of optimal scheduling to improve load balancing. Scheduling that takes into account user-guided priorities uses a data-driven strategy, which helps improve load balancing. Scheduling algorithms that take user priorities into account can optimize load distribution more effectively. The primary objective of this research is to provide a priority based randomized load balancing technique that assigns tasks to virtual machines in a random fashion based on criteria such as the number of users, the amount of time the task takes to run, the type of software being used, the cost of the software, and the amount of available resources. This method maximizes system performance by decreasing response time and resource consumption while increasing metrics like fault tolerance and scalability. This system for scheduling tasks not only accommodates user needs but also achieves excellent resource usage. This research proposes a User Task Priority based Resource Allocation with Multi Class Task Scheduling Strategy and Load Balancing (UPRA-MCTSS-LB) Model for enhancing the cloud service quality. The proposed method reschedules heavily utilized resources and tasks to enhance cloud computing load balancing efficiency. The proposed model when contrasted with the traditional models performs better task scheduling and load balancing. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.