IMPROVEMENT OF PARTICLE SWARM ALGORITHM WITH NAME-ENTITY RECOGNITION TECHNIQUE FOR OPTIMISED TASK SCHEDULING IN PUBLIC CLOUD SERVICES

Abstract

Public cloud services have transformed IT infrastructure by offering scalable, on-demand resources, yet efficient task scheduling remains a critical NP-hard challenge, often leading to latency, energy inefficiency, and poor resource utilization. Traditional Particle Swarm Optimization (PSO) algorithms, while effective, suffer from premature convergence and limited adaptability in dynamic environments. This study proposes an enhanced PSO model, named NERPSO, by integrating Name-entity Recognition (NER) techniques to process unstructured task data, enabling intelligent and adaptive scheduling in public cloud services. This study utilized generated task datasets from CloudSim Plus to simulate real-world cloud workloads, employing middleware to interface the NER module with the PSO load balancer. Methodologies include training NER models on historical task annotations using SpaCy, leveraging Word2Vec for semantic enhancement, and conducting comparative tests across 100, 200, 300, …1000 tasks scenarios. The approach evaluates performance through simulation phases, comparing baseline PSO, Reinforcement Learning PSO, and the proposed NERPSO. Results demonstrate that NERPSO significantly outperforms traditional PSO and Reinforcement Learning PSO, achieving lower average response times (2364.60 ms for 300 cloudlets), higher throughput (123.0821 tasks/sec), and improved load balance (85.5% for 100 cloudlets). Precision and F1 scores also improved (92% and 84.83% for 100 and 300 cloudlets, respectively), validating the efficacy of NER integration. This study claims that NERPSO offers a robust solution for optimized task scheduling in public cloud environments, with its context-aware capabilities reducing latency and enhancing response time, throughput, better than baseline PSO. The findings support its potential for adoption, suggesting further research into energy-efficient enhancements based on these promising results.