BAQoS: A Burst I/O Aware Quality of Service Optimization for Cloud Storage Service

In cloud storage services, burst I/O workloads from data analytics and artificial intelligence/machine learning (AI/ML) applications present significant challenges to Quality of Service (QoS) management. Existing scheduling models like dmClock ensure fair and stable I/O bandwidth allocation in typical scenarios. However, they falter under frequent burst traffic, leading to lower resource utilization and higher task latency. To address this, we propose BAQoS, a Burst I/O Aware Quality of Service Optimization for Cloud Storage Service. BAQoS employs refined request classification, a burst-aware hierarchical scheduling algorithm, and a high-performance scheduler architecture (HPSA). These features enable dynamic resource allocation and efficient scheduling for both burst and regular requests. Experiments show that BAQoS markedly enhances performance under burst workloads, accelerating burst request processing by up to 7.09 and improving overall system performance by 48.86%. Furthermore, BAQoS ensures superior performance for non-burst users, achieving a 51.15% performance boost for high-priority users, a 5.34% increase in system throughput, and an over 50% reduction in IOPS standard deviation among same-priority users.