Task-aware virtual machine scheduling for I/O performance

The use of virtualization is progressively accommodating diverse and unpredictable workloads as being adopted in virtual desktop and cloud computing environments. Since a virtual machine monitor lacks knowledge of each virtual machine, the unpredictableness of workloads makes resource allocation difficult. Particularly, virtual machine scheduling has a critical impact on I/O performance in cases where the virtual machine monitor is agnostic about the internal workloads of virtual machines. This paper presents a task-aware virtual machine scheduling mechanism based on inference techniques using gray-box knowledge. The proposed mechanism infers the I/O-boundness of guest-level tasks and correlates incoming events with I/O-bound tasks. With this information, we introduce partial boosting, which is a priority boosting mechanism with tasklevel granularity, so that an I/O-bound task is selectively scheduled to handle its incoming events promptly. Our technique focuses on improving the performance of I/O-bound tasks within heterogeneous workloads by lightweight mechanisms with complete CPU fairness among virtual machines. All implementation is confined to the virtualization layer based on the Xen virtual machine monitor and the credit scheduler. We evaluate our prototype in terms of I/O performance and CPU fairness over synthetic mixed workloads and realistic applications.