Enhanced Time Stamp Virtualized Load Balancer in Cloud Computing Web Server

Cloud computing on the Internet is significant because it allows for managing data and applications over the Internet without needing personal devices. Users’ jobs are scheduled to run on cloud resources to improve efficiency. The range of energy consumption and efficiency relative to the allocated resources should be considered. Allocating resources, such as CPU, memory, and storage, to various tasks or processes is done through task scheduling. Load balancing divides incoming traffic or workload among several servers or resources to prevent any resource from becoming overburdened. Existing approaches have drawbacks, including difficulty distributing workloads evenly among servers, workload latency, and time-consuming task scheduling. To overcome the issue, the proposed method called the Demand Aware Elastic Load-Priority Queuing Algorithm (DAEL-PQA), allocates the task based on the workload priority. Focuses on task scheduling using a Time Stamp Virtualized load balancer to optimize energy and scheduling time. First Timeline Job Completion Behaviour Rate (TJCBR) allocates the resource task based on the user behavior and feedback rate for the processing timeline. The second step is the Multi-Tenant Spider-Ant Colony Feature Scaling Rate (MT_SACFSR), which selects feature weight and estimates the scaling feature rate for multiple tasks. Demand Aware Elastic Load-Priority Queuing Algorithm is used to analyze a Load Request priority allocation. The final step is the Energy Efficient Distributed Task Scheduling Technique (EEDTST) to optimize the task scheduling level energy in the cloud web server and respond to cloud user requests. The outcomes were finally contrasted to discovered that the proposed algorithm (DAEL-PQA) provides an optimal balance result.