Using Nature-Inspired Optimization Algorithms and Machine Learning to Optimize Hadoop Resource Scheduling

This paper investigates the ability of utilizing nature-inspired optimization techniques alongside machine learning (ML) to enhance resource adjustment in Hadoop clusters. The use of ML models with adaptive optimization methodology is aimed at minimizing the time incurred to complete a task in addition to enhancing the efficiency of resource utilization. It reviews the existing Hadoop scheduling approaches and focusing on machine learning and bio-inspired techniques. For the categorization of workloads and prediction of resource needs for a specific future time, the suggested approach includes using ML algorithms like supervised/unsupervised or reinforcement learning.Moreover, new efficient work schedules and resources distribution will be used due to the nature-inspired optimization algorithms, for example, the GA, PSO. This research framework for scheduling in Hadoop will be created that will incorporate elements of artificial intelligence by using machine learning to provide expectations along with bio-inspired techniques. The tests will indicate how much resources will be utilised, how long certain jobs take, how well and scalable or the system is using actual or simulated data sets. Comprehensive comparative assessment with the traditional and improved approaches will reveal the efficiency of the proposed methodology. The results part consist of optimised approach for resource distribution, efficient flexibility in handling the workloads, and increased Hadoop specialised cluster reinforcement for scalability and heterogeneously challenging data processing activities.