An Interference-Aware Application Classifier Based on Machine Learning to Improve Scheduling in Clouds

• Main Authors: Meyer, Vinicius, Kirchoff, Dionatra F., da Silva, Matheus L., De Rose, Cesar A.F.
• Format: Conference Proceeding
• Language: English
• Subjects: Cloud computing; Degradation; Dynamic Workloads; Interference; Interference-Aware Application Classifier; Machine learning; Measurement; Processor scheduling; Resource Management; Support vector machines
• ISSN: 2377-5750
• DOI: 10.1109/PDP50117.2020.00019

To maximize resource utilization and system throughput in cloud platforms, hardware resources are often shared across multiple virtualized services or applications. In such a consolidated scenario, performance of applications running concurrently in the same physical host can be negatively affected due to interference caused by resource contention. This should be taken into account for efficient scheduling of such applications and performance prediction at user level. Nevertheless, resource scheduling in cloud computing is usually based solely on resource capacity, implemented by heuristics such as bin-packing. Our previous work has introduced an interference-aware scheduling model for web-applications considering their resource utilization profile, and to classify applications we applied fixed interference intervals based on common utilization patters. Although this resulted in placements with better overall results, we observed that some applications with more dynamic workload patterns were wrongly classified with intervals. In this paper, we propose an alternative to the use of intervals and present an interference-aware application classifier for cloud-based applications that deals better with dynamic workloads. Our classifier defines automatically interference levels ranges combining two well-known machine learning techniques: Support Vector Machines and K-Means. Preliminary experiments evaluated the applied machine learning techniques in three quality metrics: Accuracy, F1-Score and Rand Index, observing rates over 80%. The proposed solution creates a workload-aware fine-grained classification that was compared with previous work over different workload scenarios. The results demonstrate that our classification approach improves the placement efficiency by 23% on average.