ML-driven classification scheme for dynamic interference-aware resource scheduling in cloud infrastructures

• Published in: Journal of systems architecture 2021-06, Vol.116, p.102064, Article 102064
• Main Authors: Meyer, Vinícius, Kirchoff, Dionatrã F., Da Silva, Matheus L., De Rose, Cesar A.F.
• Format: Article
• Language: English
• Subjects: Cloud computing; Cross-application interference; Dynamic resource scheduling; Interference-aware classification; Machine learning; Resource contention; Workload analysis
• ISSN: 1383-7621; 1873-6165
• DOI: 10.1016/j.sysarc.2021.102064

Computing systems continue to evolve, resulting in increased performance when processing workloads in large data centers due to the virtualization benefits. This technology is the key factor that allows multiple applications to share resources, thereby enhancing the overall hardware utilization of cloud computing environments. However, multiple cloud-services contending for shared resources are susceptible to cross-application interference, which can lead to significant performance degradation and, consequently, an increase in Service Level Agreements violations. Nevertheless, state-of-the-art resource scheduling still relies mainly on resource capacity, adopting heuristics such as bin-packing and overlooking this source of overhead. But in recent years, interference-aware scheduling has gained traction, with the investigation of ways to classify applications regarding their interference levels and the proposal of static interference models and policies for scheduling co-hosted cloud applications. The preliminary results already show a considerable improvement in resource utilization and can be considered as the first steps toward a dynamic scheduling strategy. In this scenario, this paper proposes a machine learning-driven classification scheme for dynamic interference-aware resource scheduling in cloud computing environments. The main goal is to present how a classification approach, that better represents the workload variations, affects resource scheduling. In the first place, we analyze how hardware resources react to different applications with dynamic workloads. Then, we explore distinct interference classification formats and evaluate their efficiency, taking the dynamic nature of cloud workloads into account. Lastly, we present an interference-aware application classifier based on machine learning techniques and compare it with related work, adopting a variety of workload patterns. Preliminary results revealed an improvement in resource utilization efficiency by 27%, on average, when applying our classification approach in cloud infrastructures.