The traditional "trap and emulate" I/O paravirtualization model conveniently allows for I/O interposition, yet it inherently incurs costly guest-host context switches. The newer "sidecore" model eliminates this overhead by dedicating host (side)cores to poll the relevant guest memory regions and react accordingly without context switching. But the dedication of sidecores on each host might be wasteful when I/O activity is low, or it might not provide enough computational power when I/O activity is high. We propose to alleviate this problem at rack scale by consolidating the dedicated sidecores spread across several hosts onto one server. The hypervisor is then effectively split into two parts: the local hypervisor that hosts the VMs, and the remote hypervisor that processes their paravirtual I/O. We call this model vRIO---paraVirtual Remote I/O. We find that by increasing the latency somewhat, it provides comparable throughput with fewer sidecores and superior throughput with the same number of sidecores as compared to the state of the art. vRIO additionally constitutes a new, cost-effective way to consolidate I/O devices (on the remote hypervisor) while supporting efficient programmable I/O interposition.

Para-virtualization is the leading approach in IO device virtualization. It allows the hypervisor to interpose on and inspect a virtual machine's I/O traffic at run-time. Examples of such interfaces are KVM's virtio [6] and VMWare's VMXNET [7]. Current implementations of virtual I/O in the hypervisor have been shown to have performance and scalability limitations [2, 3, 5].

Direct device assignment enhances the performance of guest virtual machines by allowing them to communicate with I/O devices without host involvement. But even with device assignment, guests are still unable to approach bare-metal performance, because the host intercepts all interrupts, including those interrupts generated by assigned devices to signal to guests the completion of their I/O requests. The host involvement induces multiple unwarranted guest/host context switches, which significantly hamper the performance of I/O intensive workloads. To solve this problem, we present ELI (ExitLess Interrupts), a software-only approach for handling interrupts within guest virtual machines directly and securely. By removing the host from the interrupt handling path, ELI manages to improve the throughput and latency of unmodified, untrusted guests by 1.3x-1.6x, allowing them to reach 97%-100% of bare-metal performance even for the most demanding I/O-intensive workloads.

Virtualization is a prominent technology used in data centers around the world. While many kinds of workloads can run at near-native performance even when virtualized, I/O intensive workloads still suffer from high overhead precluding the use of virtualization in many applications. In this paper we tackle the problem of improving the performance of paravirtual I/O. We propose an exitless paravirtual I/O model, under which guests and the hypervisor, running on distinct cores, exchange exitless notifications instead of costly exit-based notifications. Our initial proof of concept improved throughput by 45% and latency by 25μsec compared to a traditional network paravirtual I/O model. We show that a single hypervisor I/O core can become saturated when serving multiple I/O intensive guests, and further research is required to improve scalability in this scenario.

Memory over commitment enables cloud providers to host more virtual machines on a single physical server, exploiting spare CPU and I/O capacity when physical memory becomes the bottleneck for virtual machine deployment. However, over commiting memory can also cause noticeable application performance degradation. We present Ginkgo, a policy framework for over omitting memory in an informed and automated fashion. By directly correlating application-level performance to memory, Ginkgo automates the redistribution of scarce memory across all virtual machines, satisfying performance and capacity constraints. Ginkgo also achieves memory gains for traditionally fixed-size Java applications by coordinating the redistribution of available memory with the activities of the Java Virtual Machine heap. When compared to a non-over commited system, Ginkgo runs the Day Trader 2.0 and SPEC Web 2009 benchmarks with the same number of virtual machines while saving up to 73% (50% omitting free space) of a physical server's memory while keeping application performance degradation within 7%.