vm reservation plan adaptation using machine learning in cloud computing

In this paper we propose a novel reservation plan adaptation system based on machine learning. in the context of cloud auto-scaling, an important issue is the ability to define and use a resource reservation plan, which enables efficient resource scheduling. if necessary, the plan allocate new resources upon reservation where a sufficient amount of resources is available. our solution allows the updating of a reservation plan initially prepared by an administrator. it makes it possible to adapt reservation plans one or more weeks ahead. hence, it allows time for the administrator to analyze the plan and discover potential problems with resource under-provisioning or over-provisioning, which prevent server overload in the former case and unnecessary expenses in the latter. it also makes it possible to extract and analyze the knowledge learned, which provide useful information about resource usage characteristics. the proposed solution is tested on openstack using real wikipedia server traffic data. experimental results demonstrate that machine learning enables an improvement in resource usage.