Virtualization History and Future Trends

Transcription

1 Virtualization History and Future Trends Christoffer Dall - Candidacy Exam - January 2013 Columbia University - Computer Science Department

2 IBM Mainframe VMs VMware Workstation x86 Hardware Support Virtual Power I/O and Device Assignments 70s Paravirtualization Xen CPU and Memory Optimizations Virtual Interrupts Historical Overview

3 Taxonomy Virtualization Software [Sugerman 01] [Kivity 09] [Bugnion 12] [Barham 03] [Popek 74] Hardware Support [Gum 83] [Adams 06] [Agesen 12] [Colp 11] [Bugnion 97] [Uhlig 05] [Bhargava 08] [Ben-Yehuda 10] [LeVasseur 04] [Wang 11] Optimizations Power [Waldspurger 02] [Liu 06] [Willmann 08] [Gordon 12] [Stoess 07] [Nathuji 07] [Hwang 12] [Amit 11] [Ye 10] [Krishnan 11] [Russell 08] [Ahmad 11] [Tian 10] [Andrus 11]

4 Virtualization Virtualization Software [Sugerman 01] [Kivity 09] [Bugnion 12] [Barham 03] [Popek 74] Hardware Support [Gum 83] [Adams 06] [Agesen 12] [Colp 11] [Bugnion 97] [Uhlig 05] [Bhargava 08] [Ben-Yehuda 10] [LeVasseur 04] [Wang 11] Optimizations Power [Waldspurger 02] [Liu 06] [Willmann 08] [Gordon 12] [Stoess 07] [Nathuji 07] [Hwang 12] [Amit 11] [Ye 10] [Krishnan 11] [Russell 08] [Ahmad 11] [Tian 10] [Andrus 11]

5 Virtualization [Popek 74] The mechanism through which we facilitate Virtual Machines Defined intuitively by Popek and Goldberg [Popek 74] to be:...an efficient, isolated duplicate of the real machine.

6 VMMs [Popek 74] Virtual Machine Monitor (VMM) Three properties VM VM 1. Efficiency 2. Resource control VMM Hardware 3. Equivalence

7 Virtualizable [Popek 74] Theorem: For any conventional third generation computer, a virtual machine monitor may be constructed if the set of sensitive instructions for that computer is a subset of the set of privileged instructions All sensitive instructions trap to the VMM Allows for trap-and-emulate

8 Revitalization in the nineties User vmx VM VMware workstation in 99 [Sugerman 01; Bugnion 12] Kernel vmm driver VMM Hardware

9 New Directions Xen and the art of Virtualization [Barham 03] Kernel-Based VM (KVM) [Kivity 09] Dom0 (QEMU) Dom1 Dom2 User QEMU VM Xen Kernel KVM Hardware Hardware

10 Hardware Support Virtualization Software [Sugerman 01] [Kivity 09] [Bugnion 12] [Barham 03] [Popek 74] Hardware Support [Gum 83] [Adams 06] [Agesen 12] [Colp 11] [Bugnion 97] [Uhlig 05] [Bhargava 08] [Ben-Yehuda 10] [LeVasseur 04] [Wang 11] Optimizations Power [Waldspurger 02] [Liu 06] [Willmann 08] [Gordon 12] [Stoess 07] [Nathuji 07] [Hwang 12] [Amit 11] [Ye 10] [Krishnan 11] [Russell 08] [Ahmad 11] [Tian 10] [Andrus 11]

11 Hardware Support Outline Intel VT-x and AMD-V Hardware vs. Software Hardware Memory Virtualization

12 Intel VT-x Non-Root traps sensitive instructions VM ENTRY VM EXIT Root Non-Root (VM)

13 Intel VT-x Nested Page Tables (NPT) Page Tables Guest Virtual Address MMU Guest Physical Address Nested Page Tables Host Physical Address

14 Hardware Support Not a new idea [Gum 83]

15 Hardware vs. Software Benefits not clear cut Comparison with software [Adams 06] show that especially IO-bound workloads are actually faster. example: in/out instruction sequences rewritten to single exit

16 Hardware vs. Software Hardware extensions have matured But, exits are still expensive... Still uses combination of hardware/software approach in commercial VMware products [Agesen 12]. Software methods still useful for nested virtualization [Ben-Yehuda 10]

17 Nested Page Tables gva gcr3 Nested page table gl 4 gva[47:39] GPA nl 4 1 SPA nl 3 SPA nl 2 SPA nl 1 SPA gl 4 GPA gl 3 gl 2 gva[38:30] gva[29:21] nl 4 6 nl 4 11 nl 3 7 nl 3 12 nl 2 8 nl 2 13 nl 1 9 nl 1 14 gl 3 10 gl 2 15 Guest page table gl 1 gva[20:12] nl 4 16 nl 3 17 nl 2 18 nl 1 19 gl 1 20 gpa gva[11:0] ncr3 nl 4 21 nl 3 22 nl 2 23 nl 1 24 TLB Entry Value nl 4 nl 3 nl 2 nl 1 G SPA (b) Some concerning results [Bhargava 08]

18 Hardware Support Virtualization Software [Sugerman 01] [Kivity 09] [Bugnion 12] [Barham 03] [Popek 74] Hardware Support [Gum 83] [Adams 06] [Agesen 12] [Colp 11] [Bugnion 97] [Uhlig 05] [Bhargava 08] [Ben-Yehuda 10] [LeVasseur 04] [Wang 11] Optimizations Power [Waldspurger 02] [Liu 06] [Willmann 08] [Gordon 12] [Stoess 07] [Nathuji 07] [Hwang 12] [Amit 11] [Ye 10] [Krishnan 11] [Russell 08] [Ahmad 11] [Tian 10] [Andrus 11]

19 Optimizations Memory Consumption Paravirtualized drivers Direct Device Assignment Virtual Interrupts

20 Direct Device Assignment One major bottleneck is I/O Still far from bare metal Direct Device Assignment is a potential solution

21 Direct Device Assignment Liu et al. [Liu 06] propose to simply assign a device directly to a VM VM Driver VM Requires device support VMM Control messages trap to VMM IOMMUs can help [Willmann 08; Amit 11] Device Hardware

22 Virtual Interrupts Interrupts can limit CPU performance and I/O throughput Worse in VMs due to VMEXITs Especially bad on systems with high IRQ frequency

23 Virtual Interrupts IRQ Device NP Handler Host Device 1 Host Device 1 Host Device 1 Guest Device 0 handle_disk_irq() Host Device 1 Guest Device 0 handle_nic_irq() ELI [Gordon 12]: VMs handle interrupts for certain devices ELI uses a shadow Interrupt Descriptor Table (IDT) to let guest handle interrupts directly, while remaining in control of host devices

24 Virtual Interrupts

25 Hardware Support Virtualization Software [Sugerman 01] [Kivity 09] [Bugnion 12] [Barham 03] [Popek 74] Hardware Support [Gum 83] [Adams 06] [Agesen 12] [Colp 11] [Bugnion 97] [Uhlig 05] [Bhargava 08] [Ben-Yehuda 10] [LeVasseur 04] [Wang 11] Optimizations Power [Waldspurger 02] [Liu 06] [Willmann 08] [Gordon 12] [Stoess 07] [Nathuji 07] [Hwang 12] [Amit 11] [Ye 10] [Krishnan 11] [Russell 08] [Ahmad 11] [Tian 10] [Andrus 11]

26 Data Centers Amazon AWS hosts 46,000 servers 8 MW power consumption at $88 million / year

27 Power Architecture for VMs with different scopes. energy accounting Client Client Client Energy manager energy allocation Resource driver 1 Energy consuming resource 1 Resource driver 2 Energy consuming resource 2 Accounting model [Stoess 07] Problem is that VMMs are uninformed about VMs and VMs don t know their real power consumption (VMEXITs) Accounting model suggests passing allocation hints up the stack, and statistics down the stack

28 VirtualPower [Nathuji 07] Main idea: Present virtual ACPI states to VMs How does virtual ACPI states map to hardware states? Soft Scaling Information from ACPI can be aggregated in data centers with heterogenous hardware

29 CPU Consolidation The Effectiveness of CPU Consolidation [Hwang 12] Interesting related observation about exclusive caches in [Krishnan 2011]

30 Conclusion IBM Mainframe VMs VMware Workstation x86 Hardware Support Virtual Power I/O and Device Assignments?? 70s Paravirtualization Xen CPU and Memory Optimizations Virtual Interrupts ARM?

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32 VM Power Metering Model: Upper and lower bounds on CPU and on Memory [Krishnan 11] Power consumption depends on how memory bound Depends on cache structures, L1 snooping wakes up other cores

33 I/O Power Savings Prolong sleep periods of mechanical disks [Ye10] Early flushes, and buffering writes Expose virtual per-device power state agents to VMs [Tian 10]

34 vic [Ahmad 11] Virtual Interrupt Coalescing Balance coalescing with latency Define a ratio R = Virtual IRQs / Hardware IRQs The lower R, the more coalescing Dynamically determine R based on Commands-In-Flight (CIF) and estimated IOPS