1. Trusted Computing Review (TCR) 2010, section 2
Virtual Machine Performance
Qian Lin
Dec. 9th, 2010

2. Related topics
• Optimization for VM performance improvement
• Measurement: tools & methods
• High performance computing in virtual machines

3. 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

4. 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

5. 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)

6. Virtualization infrastructure
• Xen and the art of virtualization. SOSP’03
• Xen and the art of repeated research. USENIX ATC’04

7. 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

8. 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

9. Virtualization infrastructure
• Virtualizing I/O devices on VMware workstation’s
hosted virtual machine monitor. USENIX ATC’01
– architecture design
– performance evaluation

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

11. 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

12. 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

13. 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)

14. 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

15. 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

16. 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.

17. Optimization
• High performance network virtualization with SR-
IOV. HPCA’09

18. 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.

19. 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.

20. 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

21. 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

22. 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.

23. 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

24. 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%

25. HPC
• A case for high performance computing with virtual
machines. ICS’06
– Two key ideas: VMM bypass I/O and scalable VM image
management.

26. 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

27. 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

28. 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, ...

29. 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

30. Our past work
• Optimizing virtual machines using hybrid
virtualization. SAC’11

31. 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)