Virtual Machine Performance

Qian LinDec. 9th, 2010

Trusted Computing Review (TCR) 2010, section 2

Related topics• Optimization for VM performance improvement

• Measurement: tools & methods

• High performance computing in virtual machines

Background• Performance is a permanent issue!– no best, but better– global optimization -> infrastructure, architecture, ...– local optimization -> CPU, memory, I/O, storage, ...

• How to arbitrate the performance?– principles & standards vs. feasibility– tools & methods vs. implementation

• Various applications focus on different aspects– application deployment– case study

Related conferences• First-tier– SOSP, OSDI, ASPLOS, ISCA, USENIX ATC, EuroSys– PPoPP, HPDC, ICDCS, NSDI

• Second-tier– VEE, HPCA, PACT, SC, ICS, IPDPS, IISWC, Euro-Par, CLUSTER

• Others– GCC, HiPC, SAC, ICPADS– HPCVirt

Virtualization infrastructure• Operating system support for virtual machines.

USENIX ATC’03– examine and reduce the large overhead for Type II VMMs

(e.g., SimOS, UML, UMLinux)

Virtualization infrastructure• Xen and the art of virtualization. SOSP’03

• Xen and the art of repeated research. USENIX ATC’04

Virtualization infrastructure• A comparison of software and hardware techniques

for x86 virtualization. ASPLOS’06– conclusion: the hardware VMM suffers lower performance

than the pure software VMM– defect of hardware VMM

• no support for MMU virtualization• fails to co-exist with existing software techniques for MMU

virtualization

Look ahead for nested paging hardware

Virtualization infrastructure• Accelerating two dimensional page walks for

virtualized systems. ASPLOS’08– present an in-depth examination of the 2D page table walk

overhead and options for decreasing it

Virtualization infrastructure• Virtualizing I/O devices on VMware workstation’s

hosted virtual machine monitor. USENIX ATC’01– architecture design– performance evaluation

Optimization• Satori: Enlightened page sharing. USENIX ATC’09– system for sharing memory in virtualized systems– detect sharing opportunities and manage the surplus

memory

Optimization• High performance VMM-Bypass I/O in virtual

machines. USENIX ATC’06– allows time-critical I/O operations to be carried out

directly in guest VMs without involvement of the VMM and/or a privileged VM

Optimization• Optimizing network virtualization in Xen.

USENIX ATC’06– redefine the virtual network interfaces of guest domains to

incorporate high-level network offload features– optimize the implementation of the data transfer path

between guest and driver domains– provide support for guest operating systems to effectively

utilize advanced virtual memory features such as superpages and global page mappings

Optimization• High performance and scalable I/O virtualization via

self-virtualized devices. HPDC’07– self-virtualized devices, which offload selected

virtualization functionality from the hypervisor– self-virtualized network interface (SV-NIC)

Optimization• Bridging the gap between software and hardware

techniques for I/O virtualization. USENIX ATC’08– Problem 1: paravirtualized I/O causes high CPU overhead.– problem 2: direct I/O removes the benefits of the driver

domain model.– Solution: bridge the performance gap between the driver

domain model and direct I/O

Optimization• XenLoop: a transparent high performance inter-VM

network loopback. HPDC’08– a fully transparent and high performance– intercept outgoing network packets and shepherds the

packets destined to co-resident VMs through a high-speed inter-VM shared memory channel

Optimization• Virtualization Polling Engine (VPE): Using dedicated

CPU cores to accelerate I/O virtualization. ICS’09– takes advantage of dedicated CPU cores to help with the

virtualization of I/O devices by using an event-driven execution model with dedicated polling threads.

Optimization• High performance network virtualization with SR-IOV.

HPCA’09

Optimization• I/O scheduling model of virtual machine based on

multi-core dynamic partitioning. HPDC’10– Problem: scheduling of I/O missions was now treated as a

secondary concern when compared with scheduling of processor resources. • This would cause serious degradation of I/O performance and

make virtualization less desirable for I/O-intensive applications.

– Solution: monitor I/O operations, divide processor cores into 3 subsets which take different missions respectively.

Measurement• Measuring CPU overhead for I/O processing in the

Xen virtual machine monitor. USENIX ATC’05– a light weight monitoring system– measure the CPU usage of different virtual machines

caused by I/O processing– “page-flipping” technique of Xen

• the memory page containing the I/O data in the driver domain is exchanged with an unused page provided by the guest OS.

Measurement• Diagnosing performance overheads in the Xen virtual

machine environment. VEE’05– Xenoprof: a system-wide statistical profiling toolkit

implemented for Xen• enable coordinated profiling of multiple VMs in a system to obtain

the distribution of hardware events (e.g., clock cycles, cache and TLB misses)

– use the toolkit to analyze performance overheads incurred by networking applications running in Xen VMs

Measurement• Xenprobes, a lightweight user-space probing

framework for Xen virtual machine. USENIX ATC’07– a lightweight framework to probe the guest kernels– be useful for various purposes

• monitor real-time status of production systems• analyze performance bottlenecks• log specific events tracing problems

– introduce some unique advantages• put the breakpoint handlers in user-space => easy use• allow to probe multiple guests at the same time• support all kind of OS supported by Xen

Measurement• An analysis of HPC benchmarks in virtual machine

environments. Euro-Par’08– Problem: predicting performance for applications is

toughly difficult in virtual environments.– Research: investigate the behavior and identify patterns of

various overheads for HPC benchmark applications.

Measurement• Application performance modeling in a virtualized

environment. HPCA’09– build performance models for applications in virtualized

environments– propose an iterative model training technique based on

artificial neural networks which is found to be accurate across a range of applications

Measurement• Performance comparison of two virtual machine

scenarios using an HPC application. HPCVirt’09– compare the performance implications using HPC

application– two VM node configuration

• 2 VMs with 1 process/VM• 1 VM with 2 processes/VM

– the difference in overall performance impact is around 3%

HPC• A case for high performance computing with virtual

machines. ICS’06– Two key ideas: VMM bypass I/O and scalable VM image

management.

HPC• Virtualization for high-performance computing.

OSR 2006(vol.40)– discuss the trends, motivations, and issues in hardware

virtualization with emphasis on their value in HPC environments

HPC• Improving performance by embedding HPC

applications in lightweight Xen domains. HPCVirt’08– HPC application and its execution environment can be

embedded within a lightweight guest domain

Summary: research areas• Reduce virtualization overhead– infrastructure

• Xen vs. KVM vs. VMware• cloud computing related

– CPU and memory• On the low-level, software strategies are becoming less important,

but hardware. • On the high level, optimization is increasingly derived from

algorithm rather than architecture.

– I/O • continue to be hot topics!• network, disk, filesystem, ...

Summary: research areas• Measurement and tools– benchmark– diagnosis and performance bottleneck– implementation of practical tools

• Application driven performance improvement– behavior analysis of specific applications, especially with

respect to that triggering virtualization overhead– local optimize and customize VM for definite application

scenario

Our past work• Optimizing virtual machines using hybrid

virtualization. SAC’11

TCR: to be expected ...• VM security

• Virtualization technology and platform

• Novel memory architecture

• Cloud computing

• App. case study under virtualization environment

• VM miscellaneous (e.g., migration, time keeping)