Energy profiling and performance optimization for network-related transactions in virtualized cloud

Networking and machine virtualization play critical roles in the success of modern cloud computing. The energy consumption of physical machines has been carefully examined in the past, including the impact from network traffic. When it comes to virtual machines (VMs) in cloud data centers, it remains unexplored how the highly dynamic traffic affects the energy consumption in virtualized environments. In this thesis, we first present an empirical study on the interplay between energy consumption and network transactions in virtualized environments. Through the real-world measurement on both Xen- and KVM-based platforms, we show that these state-of-the-art designs bring significant overhead on virtualizing network devices and noticeably increase the demand of CPU resources when handling network traffic. Furthermore, the energy consumption varies significantly with traffic allocation strategies and virtual CPU affinity conditions, which was not seen in conventional physical machines. Next, we study the performance and energy efficiency issues when CPU intensive tasks and I/O intensive tasks are co-located inside a VM. A combined effect from device virtualization overhead and VM scheduling latency can cause severe interference in the presence of such hybrid workloads. To this end, we propose Hylics, a novel solution that enables an efficient data traverse path for both I/O and computation operations, and decouples the costly interference. Several important design issues are pinpointed and addressed during our implementation, including efficient intermediate data sharing, network service offloading, and QoS-aware memory usage management. Based on our real-world deployment in KVM, Hylics can improve computation and networking performance with a moderate amount of memory usage. Moreover, this design also sheds new light on optimizing the energy efficiency for virtualized systems.