MUMBAI, India, June 26 -- Intellectual Property India has published a patent application (202641051106 A) filed by Nandha Engineering College on April 22, 2026, for An Efficient Load Balancing Framework For Deploying Resource Scheduling In Cloud Based Communication.
Inventors include Dr. S. Nandagopal; S. Dhanush Balaji; E. Pradeep; and T. Sivasni.
The application for the patent was published on June 19, 2026, under issue no. 25/2026.
Abstract: The present invention relates to an energy-aware load balancing framework fo fficient resource scheduling in cloud computing environments, particularly within nfrastructure as a Service (laaS) models. Cloud data centers host multiple virtual machines VMs) on physical servers to support dynamic workloads. However, improper allocatio f virtual machines often leads to workload imbalance, inefficient resource utilization, · ncreased energy consumption, and degraded system performance. These challenges are ore critical in performance-sensitive applications such as cloud-based healthcar ommunication systems, where reliability and response time are essential. o address these issues, the present invention proposes an intelligent and adaptive virtual achine allocation mechanism that ensures balanced workload distribution across clou osts. The system continuously monitors resource utilization parameters such as CPU emory, and bandwidth to detect overload and underutilization conditions. Based on these arameters, the framework dynamically allocates and migrates virtual machines to aintain optimal system performance. In addition, an energy-aware scheduling mechanis · s incorporated to evaluate and minimize power consumption in cloud data centers using on-linear power model. he invention further integrates a hybrid metaheuristic optimization approach by ombining Particle Swarm Optimization (PSO) and Ant Lion Optimization (ALO) lgorithms. PSO is used for initial exploration of possible VM allocation solutions, while LO refines these solutions to achieve optimal resource mapping with improved onvergence accuracy. The hybrid optimization technique enhances decision-making i esource allocation, leading to improved load balancing, reduced energy consumption, and etter utilization of cloud resources. Experimental results obtained through simulatio emonstrate that the proposed framework outperforms conventional methods in tenns o efficiency, scalability, and energy savings. The invention thus provides a reliable, cost effective, and sustainable solution for cloud resource management in modem computing environments
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