BA-HGA: A burst-aware hybrid genetic algorithm for adaptive resource allocation in multi-tenant clouds
2026, vol.18 , no.2, pp. 11-22
Article [2026-02-02]
Multi-tenant cloud environments face unpredictable demand surges that challenge traditional scheduling algorithms. While recent AI-driven schedulers show promise, their behaviour under extreme burst conditions remains underexplored. This paper introduces BA-HGA (Burst-Aware Hybrid Genetic Algorithm), which integrates real-time burst detection through a Burst Intensity Coefficient (BIC), dual adaptive deadline policies, and dynamic fitness weight adjustment to maintain service-level agreement (SLA) compliance during workload volatility. We evaluate BA-HGA through 9,000 simulation experiments across varying burst intensities (0.1–0.9), comparing performance against classical schedulers (FCFS, Round-Robin) and state-of-the-art genetic algorithms. Results demonstrate that BA-HGA achieves 15–23% higher SLA success rates under high-burst conditions (probability ≥ 0.5) while maintaining comparable make span and load balance to conventional methods under stable workloads. The study contributes empirical evidence for burst-adaptive scheduling design and provides a practical framework for multi-tenant cloud resource management.
burst-aware scheduling, hybrid genetic algorithm, cloud resource scheduling, SLA compliance, bursty workloads
https://doi.org/10.59035/YQAN9290
Alda Myzeqari, Jaumin Ajdari, Xhemal Zenuni. BA-HGA: A burst-aware hybrid genetic algorithm for adaptive resource allocation in multi-tenant clouds. International Journal on Information Technologies and Security, vol.18 , no.2, 2026, pp. 11-22. https://doi.org/10.59035/YQAN9290