A Hybrid Reinforcement Learning Approach for Coordinated Configuration of Virtual Machines and Appliances
Cloud computing has a key requirement for resource configuration in a real-time manner. In such virtualized environments, both Virtual Machines (VMs) and hosted applications need to be configured on-the-fly to adapt to system dynamics. The interplay between the layers of VMs and applications further complicates the problem of cloud configuration. Independent tuning of each aspect may not lead to optimal system wide performance. In this paper, the authors propose a framework, namely CoTuner, for coordinated configuration of VMs and resident applications. At the heart of the framework is a model-free hybrid Reinforcement Learning (RL) approach, which combines the advantages of Simplex method and RL method and is further enhanced by the use of system knowledge guided exploration policies.