Research on Detection Strategies and Algorithms of the Virtual Machine Exception Under a Cloud Platform

Abstract

The runtime is dynamicly change under cloud environment, which lead to the detection domain varies durning time. The real-time performance of detection on visual machines in detection domain will be influenced by the efficiency of partitioning detection domains. This paper present a detection domain partition algorithm based on the optimized k-medoids clustering, which improved k-medoids clustering algorithm in initial medoids selection and medoids replacement strategy during the iterative procedure, improved the partition efficiency of detection domain and the real-time performance of anomaly detection.