Performance Analysis of Cloud Computing Task Scheduling Using Metaheuristic Algorithms in DDoS and Normal Environments

Abstract

Cloud computing has emerged as a fundamental pillar of modern technology, enabling large-scale data management, computational efficiency, and operational flexibility. However, critical challenges persist, particularly concerning security and performance. DDoS attacks severely impact cloud infrastructure by degrading system performance and causing service disruptions. These persistent threats raise concerns about cloud system reliability and underscore the necessity for advanced security measures. This study investigates the cloud computing task scheduling problem, recognized as NP-hard, and explores the impact of adversarial conditions such as DDoS attacks on system performance. To address this challenge, metaheuristic algorithms are employed. The research evaluates the effectiveness of traditional approaches, including genetic algorithms (GAs), particle swarm optimization (PSO), and artificial bee colony (ABC), while also introducing a GA–PSO algorithm designed to enhance task scheduling efficiency. The proposed method aims to minimize makespan by optimizing task allocation across virtual machines (VMs) within cloud environments. A comparative analysis of scheduling performance under both normal and DDoS-affected conditions reveals that metaheuristic techniques contribute significantly to system resilience. Furthermore, the GA–PSO algorithm demonstrates notable improvements at specific iteration levels. The findings underscore the potential of advanced scheduling methods to enhance cloud computing sustainability while offering practical solutions to mitigate real-world security threats.