Packet aggregation based network I/O virtualization for cloud computing
Computer Communications
Understanding scheduling implications for scientific applications in clouds
Proceedings of the 9th International Workshop on Middleware for Grids, Clouds and e-Science
| Hi-index | 0.00 |
In this paper, we analyze virtual machine (VM) scalability on multi-core systems for compute-, memory-, and network I/O-intensive workloads. The VM scalability evaluation under these three workloads will help cloud users to understand the performance impact of underlying system and network architectures. We demonstrate that VMs on the state-of-the-art multi-core processor based systems scale as well as multiple threads on native SMP kernel for CPU and memory intensive workloads. Intra-VM communication of network I/O intensive TCP message workload has a lower overhead compared to multiple threads when VMs are pinned to specific cores. However, VM scalability is severely limited for such workloads for across-VM communication on a single host due to virtual bridges. For across local and wide area network communication, the network bandwidth is the limiting factor. Unlike previous studies that use workload mixes, we apply a single workload type at a time to clearly attribute VM scalability bottlenecks to system and network architectures or virtualization itself.