A-Who: Stagnation-Based Adaptive Metaheuristic for Cloud Task Scheduling Resilient to DDoS Attacks

Abstract

Task scheduling in cloud computing becomes significantly more challenging under Distributed Denial-of-Service (DDoS) attacks, as malicious workload injection disrupts resource availability and degrades Quality of Service (QoS). To address this issue, this study proposes an improved Wild Horse Optimizer (A-WHO) that incorporates a stagnation detection mechanism and a stagnation-driven adaptive leader perturbation strategy. The proposed mechanism dynamically applies a noise-guided perturbation into the stallion position only when no improvement is observed over a predefined threshold, enabling A-WHO to escape local optima without modifying the standard behavior of WHO in normal iterations. In addition, a DDoS-aware CloudSim environment is developed by generating attacker virtual machines and high-MI malicious cloudlets to emulate realistic resource exhaustion scenarios. A-WHO's performance is assessed using makespan, SLA violation rate, each of the QoS metrics, and energy consumption on normal and DDoS conditions. The experimental results indicate that A-WHO achieves the best absolute makespan and QoS metrics during an attack and competitive results under normal conditions. In comparison with the WHO, PSO, ABC, GA, SCA, and CSOA, the proposed approach demonstrates improved robustness and greater resilience to resource degradation attacks. These findings indicate that integrating stagnation-aware diversification into metaheuristic schedulers represents a promising direction for securing cloud task scheduling frameworks.