Minimizing Average Startup Latency of VMs by Clustering-based Template Caching Scheme in an IaaS System

Nowadays main infrastructure-as-a-service (IaaS) systems have been widely exploiting the template-based VM creation and template caching techniques to reduce the startup latency of user VM and service response time. Because the new VMs created from the templates do not always have the same software system as needed, further reconfigurations, that is, installing the missing software components and removing the undesired ones, must be made before the VMs can be used by users. The reconfiguration time is also an important source of the VM startup latency. However, current template caching solutions based on the traditional caching strategy select some templates which are frequently used recently to cache without considering the reconfiguration time of various user VMs created in IaaS data center. In this paper, we exploit a modified k-means clustering method to optimize the selecting of templates to be cached and theoretically prove the selected ones can minimize the average reconfiguration time and then the average startup latency of all user VMs under the same limits on the number of templates which cache space can accommodate. The correlative simulation experiments also prove the effectiveness of our approach.