Workload Classification Model for Specializing Virtual Machine Operating System

Abstract

There is growing demand on strategies to help cloud computing utilize its scale adaptiveness and cost effectiveness advantages. Previous operating systems(OS) are designed to suit all, leading to that virtual machines with different workloads use indiscriminate processing platform. However, there are conflicts between generality and performance, limited resource utilization and low processing efficiency of common OS penalize system performance. Therefore, we design four kinds of OS optimization strategies corresponding to four primary classes of workloads: CPU-Intensive, Memory-Intensive, I/O-Intensive and Network-Intensive. In this paper, we propose a Feedback-Based Workload Classification(FBWC) model which contains metrics collector, data preprocessor, Training Set Refresh Support Vector Machine(TSRSVM) classifier, decision maker and operating system tuner to classify workloads into appropriate class. TSRSVM combines support vectors of origin training set and correctly classified testing set together as new training set to get higher classification accuracy and efficiency. Comprehensive experiments compared with K Nearest Neighbors(KNN) and SVM demonstrate effectiveness of FBWC model and TSRSVM classification algorithm. Performance comparison between common virtual machine and the tuned one shows high degree performance improvement by OS specialization.