Dynamic Scheduling Strategies for Load Balancing in Parallel and Distributed Systems

Actual load balancing in parallel and distributed systems ruins a serious task owing to the dynamic nature of workloads and the availability of resources. Existing scheduling procedures continually fail to regulate real-time alterations, leading to suboptimal performance and resource underutilization. Our study validates dynamic and effective load distribution by combining novel systems and optimization techniques to handle these issues. To provide efficient load balancing in distributed and parallel systems, we utilize a comprehensive dynamic scheduling approach in this work. In this example, we start by using Round-Robin Allocation with Sunflower Whale Optimization (RRA-SWO) to perform an allocation procedure. The allocation step is followed by the Hybrid Ant Genetic Algorithm (HAGA), which is used to schedule tasks in parallel. The Least Response Time (LRT) technique for the Load Monitoring procedures will be developed once the job scheduling is complete. The Harmony Search Algorithm with Linear Regression (LR-HSA) is then used to do Distributed Computing-based Load Prediction and Adjustment. Alongside ongoing observation, this is carried out. Finally, we use the Least Recently Used (LRU) technique to do dynamic load balancing. We build and test our methods CloudSim and NetBeans 12.3 are used on a Windows 11 64-bit. Throughput, Packet Delivery Ratio, Average Response Time, Task Success Rate, Memory Utilization Rate, and Throughput are all analyzed to validate our strategy.