Scale and Performance in the Denali Isolation Kernel University of Washington Abstract

Scale and Performance in the Denali Isolation Kernel University of Washington Abstract
Scale and Performance in the Denali Isolation Kernel
Andrew Whitaker, Marianne Shaw, and Steven D. Gribble
University of Washington
{andrew,mar,gribble}@cs.washington.edu
Abstract
This paper describes the Denali isolation kernel, an
operating system architecture that safely multiplexes a
large number of untrusted Internet services on shared
hardware. Denali’s goal is to allow new Internet services
to be “pushed” into third party infrastructure, relieving
Internet service authors from the burden of acquiring and
maintaining physical infrastructure. Our isolation kernel
exposes a virtual machine abstraction, but unlike conventional virtual machine monitors, Denali does not attempt
to emulate the underlying physical architecture precisely,
and instead modifies the virtual architecture to gain scale,
performance, and simplicity of implementation. In this
paper, we first discuss design principles of isolation kernels, and then we describe the design and implementation
of Denali. Following this, we present a detailed evaluation of Denali, demonstrating that the overhead of virtualization is small, that our architectural choices are warranted, and that we can successfully scale to more than
10,000 virtual machines on commodity hardware.
1
Introduction
Advances in networking and computing technology have accelerated the proliferation of Internet services, an application model in which service code executes in the Internet infrastructure rather than on
client PCs. Many applications fit this model, including web sites, search engines, and wide area platforms such as content distribution networks, caching
systems, and network experimentation testbeds [25].
The Denali project seeks to encourage and enhance
the Internet service model by making it possible for
untrusted software services to be “pushed” safely into
third party hosting infrastructure, thereby separating the deployment of services from the management
of the physical infrastructure on which they run.
While this has clear benefits, it also faces difficult
technical challenges. One challenge is scale: for costefficiency and convenience, infrastructure providers
will need to multiplex many services on each server
machine, as it would be prohibitively expensive to
dedicate a separate machine to each service. A second challenge is security: infrastructure providers
cannot trust hosted services, and services will not
trust each other. There must be strong isolation be-
tween services, both for security and to enforce fair
resource provisioning.
In this paper, we present the design, implementation, and evaluation of the Denali isolation kernel, an x86-based operating system that isolates untrusted software services in separate protection domains. The architecture of Denali is similar to
that of virtual machine monitors such as Disco [6],
VMWare [31], and VM/370 [9]. A virtual machine
monitor carves a physical machine into multiple virtual machines; by virtualizing all hardware resources,
a VMM can prevent one VM from even naming the
resources of another VM, let alone modifying them.
To support unmodified legacy “guest” OSs and
applications, conventional VMMs have the burden
of faithfully emulating the complete architecture of
the physical machine. However, modern physical
architectures were not designed with virtualization
or scale in mind. In Denali, we have reconsidered
the exposed virtual architecture, making substantial
changes to the underlying physical architecture to enhance scalability, performance, and simplicity, while
retaining the strong isolation properties of VMMs.
For example, although Denali exposes virtual
disks and NICs, their interfaces have been redesigned
for simplicity and performance. Similarly, Denali exposes an instruction set architecture which is similar
to x86 (to gain the performance benefits of directly
executing instructions on the host processor), but in
which non-virtualizable aspects have been hidden for
simplicity, and in which the interrupt model has been
changed for scalability.
The cost of Denali’s virtual architecture modifications is backwards compatibility: Denali is not
able to run unmodified legacy guest operating systems. However, the Denali virtual architecture is
complete, in the sense that a legacy operating system could be ported to Denali (although this is still
work in progress). To evaluate Denali in the absence of a ported legacy OS, we implemented our own
lightweight guest OS, called Ilwaco, which contains
a port of the BSD TCP/IP networking stack, thread
support, and support for a subset of the POSIX API.
We have ported several applications to Ilwaco, including a web server, the Quake II game server, tel-
net, and various utilities.
1.1
Contributions
The contributions of this paper are:
1. A case for isolation kernels, an OS structure
for isolating untrusted software services;
2. A set of design principles for isolation kernels,
arguing for a VMM-like structure, but with strategic
modifications to the virtual architecture for scalability, performance, and simplicity;
3. The design, implementation, and evaluation
of the Denali isolation kernel, focusing on the challenges of scale, and demonstrating that Denali can
scale to over 10,000 VMs on commodity hardware.
The rest of this paper is organized as follows. In
Section 2, we describe the various classes of applications we hope to enable, and derive design principles
of isolation kernels. Section 3 discusses the design
and implementation of the Denali isolation kernel.
In Section 4, we evaluate our implementation, focusing on issues of scale. We compare Denali to related
work in Section 5, and we conclude in Section 6.
2
The Case for Isolation Kernels
Many applications and services would benefit
from the ability to push untrusted code into the Internet infrastructure. We outline some of these below,
and use them to motivate the properties required by
an isolation kernel.
Supporting dynamic content in content delivery
systems: a progression of content delivery systems
has been introduced in recent years, including CDNs,
proxy caches [34], and peer-to-peer networks [30]. All
suffer from the limitation that only static content is
supported, whereas a large and increasing fraction
of content is dynamically generated [34]. Dynamic
content distribution requires the ability to execute
and isolate untrusted content generation code.
Pushing Internet services into virtual hosting infrastructure: a “virtual” hosting center would allow
new Internet services to be uploaded into managed
data centers. In addition to supporting commercial
services, we believe virtual hosting centers would encourage the emergence of a grassroots development
community for Internet services, similar to the shareware community that exists for desktop applications.
Internet measurement and experimentation infrastructure: NIMI [25] and CAIRN [2] have sought
to deploy wide-area testbeds to support network
measurement research. Recent projects such as
Chord [30] would benefit from the ability to deploy research prototypes at scale across the Internet. Whether for measurement or prototyping, the
infrastructure must be able to multiplex and isolate
mutually distrusting experiments.
Mobile code: deploying mobile code in routers
and servers has been proposed by both active networks and mobile agent systems [19].
All of these services and applications share several properties. For the sake of cost-efficiency, multiple services will need to be multiplexed on shared
infrastructure. As a result, software infrastructure
must exist to isolate multiplexed services from each
other: a service must not be able to corrupt another
service or the underlying protection system. Additionally, performance isolation is required to bound
each service’s resource consumption. Finally, the
degree of information sharing between these multiplexed services will be small, or entirely non-existent.
Because of this, it is reasonable to strengthen isolation at the cost of high sharing overhead.
As we will argue in detail, no existing software
system has the correct set of properties to support
this emerging class of Internet services. Existing
software protection systems (including operating systems, language-based protection techniques, and virtual machine monitors) suffer from some combination of security vulnerabilities, complexity, insufficient scalability, poor performance, or resource management difficulties. We believe that a new software
architecture called an isolation kernel is required to
address the challenges of hosting untrusted services.
2.1
Isolation Kernel Design Principles
An isolation kernel is a small-kernel operating
system architecture targeted at hosting multiple untrusted applications that require little data sharing.
We have formulated four principles that govern the
design of isolation kernels.
1. Expose low-level resources rather than
high-level abstractions. In theory, one might hope
to achieve isolation on a conventional OS by confining each untrusted service to its own process (or
process group). However, OSs have proven ineffective at containing insecure code, let alone untrusted
or malicious services. An OS exposes high-level abstractions, such as files and sockets, as opposed to
low-level resources such as disk blocks and network
packets. High-level abstractions entail significant
complexity and typically have a wide API, violating
the security principle of economy of mechanism [29].
They also invite “layer below” attacks, in which an
attacker gains unauthorized access to a resource by
requesting it below the layer of enforcement [18].
An isolation kernel exposes hardware-level resources, displacing the burden of implementing operating systems abstractions to user-level code. In this
respect, an isolation kernel resembles other “small
kernel” architectures such as microkernels [1], virtual machine monitors [6], and Exokernels [20]. Although small kernel architectures were once viewed
as prohibitively inefficient, modern hardware improvements have made performance less of a concern.
2. Prevent direct sharing by exposing only
private, virtualized namespaces. Conventional
OSs facilitate protected data sharing between users
and applications by exposing global namespaces,
such as file systems and shared memory regions. The
presence of these sharing mechanisms introduces the
problem of specifying a complex access control policy
to protect these globally exposed resources.
Little direct sharing is needed across Internet services, and therefore an isolation kernel should prevent direct sharing by confining each application to a
private namespace. Memory pages, disk blocks, and
all other resources should be virtualized, eliminating
the need for a complex access control policy: the only
sharing allowed is through the virtual network.
Both principles 1 and 2 are required to achieve
strong isolation. For example, the UNIX chroot
command discourages direct sharing by confining applications to a private file system name space. However, because chroot is built on top of the file system abstraction, it has been compromised by a layerbelow attack in which the attacker uses a cached file
descriptor to subvert file system access control.
Although our discussion has focused on security isolation, high-level abstractions and direct sharing also reduce performance isolation. High-level
abstractions create contention points where applications compete for resources and synchronization
primitives.
This leads to the effect of “crosstalk” [23], where application resource management
decisions interfere with each other. The presence of
data sharing leads to hidden shared resources like the
file system buffer cache, which complicate precise resource accounting.
3. Zipf ’s Law implies the need for scale. An
isolation kernel must be designed to scale up to a
large number of services. For example, to support dynamic content in web caches and CDNs, each cache
or CDN node will need to store content from hundreds (if not thousands) of dynamic web sites. Similarly, a wide-area research testbed to simulate systems such as peer-to-peer content sharing applications must scale to millions of simulated nodes. A
testbed with thousands of contributing sites would
need to support thousands of virtual nodes per site.
Studies of web documents, DNS names, and
other network services show that popularity tends
to be driven by Zipf distributions [5]. Accordingly,
we anticipate that isolation kernels must be able to
handle Zipf workloads. Zipf distributions have two
defining traits: most requests go to a small set of popular services, but a significant fraction of requests go
to a large set of unpopular services. Unpopular services are accessed infrequently, reinforcing the need
to multiplex many services on a single machine.
To scale, an isolation kernel must employ techniques to minimize the memory footprint of each service, including metadata maintained by the kernel.
Since the set of all unpopular services won’t fit in
memory, the kernel must treat memory as a cache of
popular services, swapping inactive services to disk.
Zipf distributions have a poor cache hit rate [5], implying that we need rapid swapping to reduce the
cache miss penalty of touching disk.
4. Modify the virtualized architecture for simplicity, scale, and performance. Virtual machine
monitors (VMMs), such as Disco [6] and VM/370 [9],
adhere to our first two principles. These systems also
strive to support legacy OSs by precisely emulating
the underlying hardware architecture. In our view,
the two goals of isolation and hardware emulation are
orthogonal. Isolation kernels decouple these goals by
allowing the virtual architecture to deviate from the
underlying physical architecture. By so doing, we can
enhance properties such as performance, simplicity,
and scalability, while achieving the strong isolation
that VMMs provide.
The drawback of this approach is that it gives
up support for unmodified legacy operating systems.
We have chosen to focus on the systems issues of
scalability and performance rather than backwards
compatibility for legacy OSs. However, we are currently implementing a port of the Linux operating
system to the Denali virtual architecture; this port
is still work in progress.
3
The Denali Isolation Kernel
The Denali isolation kernel embodies all of the
principles described in the previous section of this paper. Architecturally, Denali is a thin software layer
that runs directly on x86 hardware. Denali exposes
a virtual machine (VM) architecture that is based on
the x86 hardware, and supports the secure multiplexing of many VMs on an underlying physical machine.
Each VM can run its own “guest” operating system
and applications (Figure 1).
This section of the paper presents the design of
the Denali virtual architecture, and the implementation of an isolation kernel to support it. We also
describe the Ilwaco guest OS, which is tailored for
building Internet services that execute on the Denali
virtual architecture.
app
Denali virtual
machine interface
app
“guest” “guest”
OS
OS
and the kernel. For example, we implemented Denali’s interrupt-enabled flag as a virtual register.
app
•••
“guest”
OS
Denali isolation kernel
3.1.2
x86 arch.
interface
x86 hardware
Figure 1: The Denali architecture: the Denali isolation kernel is a thin software layer that exposes a virtual
machine abstraction that is based on the underlying x86
architecture.
3.1
The Denali Virtual Architecture
The Denali virtual architecture consists of an instruction set, a memory architecture, and an I/O architecture (including an interrupt model). We describe each of these components in turn.
3.1.1
ISA
The Denali virtual instruction set was designed
for both performance and simplicity. The ISA primarily consists of a subset of the x86 instruction set,
so that most virtual instructions execute directly on
the physical processor. The x86 ISA is not strictly
virtualizable, as it contains instructions that behave
differently in user mode and kernel mode [17, 27];
x86 virtual machine monitors must use a combination
of complex binary rewriting and memory protection
techniques to virtualize these instructions. Since Denali is not designed to support legacy OSs, our virtual
architecture simply defines these instructions to have
ambiguous semantics. If a VM executes one of these
instructions, at worst the VM could harm itself. In
practice, they are rarely used; most deal with legacy
architecture features like segmentation, and none are
emitted by C compilers such as gcc (unless they appear in inlined assembly fragments).
Denali defines two purely virtual instructions.
The first is an “idle-with-timeout” instruction that
helps VMs avoid wasting their share of the physical CPU by executing OS idle loops. The idle-withtimeout instruction lets a VM halt its virtual CPU
for either a bounded amount of physical time, or until
an interrupt arrives for the VM.1 The second purely
virtual instruction simply allows a virtual machine to
terminate its own execution.
Denali adds several virtual registers to the x86
register file, to expose system information such as
CPU speed, the size of memory, and the current system time. Virtual registers also provide a lightweight
communication mechanism between virtual machines
1 Denali’s idle instruction is similar to the x86 halt instruction, which is executed to put the system into a low-power
state during idle periods. Denali’s timeout feature allows for
fine-grained CPU sharing.
Memory Architecture
Each Denali VM is given its own (virtualized)
physical 32-bit address space. A VM may only access a subset of this 32-bit address space, the size
and range of which is chosen by the isolation kernel when the VM is instantiated. The kernel itself
is mapped into a portion of the address space that
the VM cannot access; because of this, we can avoid
physical TLB flushes on VM/VMM crossings.
By default, a VM cannot virtualize its own (virtualized) physical address space: in other words, by
default, there is no virtual MMU. In this configuration, a VM’s OS shares its address space with applications, similar to a libOS in Exokernel [20]. Exposing a single address space to each VM improves
performance, by avoiding TLB flushes during context
switches between applications in the same VM [31].
We have recently added support for an optional, virtual MMU to Denali; this virtual MMU
looks nothing like the underlying x86-based physical
MMU, but instead is modeled after a simple softwareloaded TLB, similar to those of modern RISC architectures. A software-loaded TLB has the advantage
that the VM itself gets to define its own page-table
structure, and the software TLB interface between
the VMM and the VM is substantially simpler than
the more complex page table interface mandated by
the x86 hardware-loaded TLB architecture.
3.1.3
I/O Devices and Interrupt Model
Denali exposes several virtual I/O devices, including an Ethernet NIC, a disk, a keyboard, a console, and a timer. Denali’s virtual devices have similar functionality to their physical counterparts, but
they expose a simpler interface. Physical devices often have “chatty” interfaces, requiring many programmed I/O instructions per operation. VMMs
that emulate real hardware devices suffer high overhead, since each PIO must be emulated [31]. Another
benefit of simplification is portability: our virtual
device interfaces are independent of the underlying
physical devices.
Denali has chosen to omit many x86 architectural
features. Virtual devices have been simplified to begin in a well-known, pre-initialized state when a VM
boots. This simplifies both the Denali isolation kernel and guest OSs by eliminating the need to probe
virtual devices on boot. Denali also does not expose
the BIOS2 or segmentation hardware, because these
features are rarely used outside of system boot.
2 The BIOS is also involved in power management; Denali
does not expose this to VMs.
Denali exposes virtual interrupts to VMs, much
in the same way that the physical x86 architecture
exposes real interrupts to the host processor. Virtual interrupts are typically triggered by physical interrupts, such as when an Ethernet packet arrives
that is destined for a particular VM. However, not
all physical interrupts cause virtual interrupts; for
example, a packet may arrive that is not destined
for any of the running VMs, in which case the isolation kernel simply drops the packet without raising
a virtual interrupt.
Denali’s interrupt dispatch model differs significantly from the underlying x86 hardware to better support the multiplexing of many virtual machines. As the number of simultaneously running
VMs grows, it becomes increasingly unlikely that the
VM which is the ultimate recipient of a physical interrupt is executing when the interrupt is raised. In
some cases, the target VM could even be swapped
out to disk. Rather than preserving the immediate
interrupt semantics of x86, Denali delays and batches
interrupts destined for non-running VMs. A VM receives pending interrupts once it begins its normal
scheduler quantum, and if multiple interrupts are
pending for a VM, all interrupts are delivered in a single VMM/VM crossing. This is similar to the Mach
3.0 user-level I/O interface [15].
Denali’s asynchronous, batched interrupt model
changes the semantics of timing-related interrupts.
For example, a conventional timer interrupt implies
that a fixed-size time interval has just passed. In
Denali, a virtual timer interrupt implies that some
amount of physical time has passed, but the duration
may depend on how many other VMs are contending for the CPU. As a result, the interpretation of
timer interrupts in the implementation of guest OS
software timers must be altered.
3.2
Isolation Kernel Implementation
The Denali isolation kernel runs directly on x86
hardware. The core of the kernel, including multiprogramming, paging, and virtual device emulation,
was implemented from scratch; we used the Flux OSKit [14] for device drivers and other hardware support routines, and some support libraries such as libc.
The isolation kernel serves two roles: it implements the Denali virtual architecture, and it multiplexes physical resources across competing VMs. We
have maintained a strict separation between resource
management policy and mechanism, so that we could
implement different policies without affecting other
aspects of the isolation kernel.
3.2.1
CPU Virtualization
Denali uses standard multiprogramming techniques to multiplex the CPU across VMs. The isolation kernel maintains a per-VM thread structure,
which contains a kernel stack, space for the register
file, and the thread status. The policy for multiplexing the CPU is split into two components: a gatekeeper policy enforces admission control by choosing
a subset of active machines to admit into the system; the rest are swapped to disk, as we will describe
later. The scheduler policy controls context switching among the set of admitted machines.
The gatekeeper admits machines in FIFO order
as long as there are a minimum number of physical backing pages available. The scheduler uses
round-robin scheduling among the set of admitted
machines. These policies were chosen because they
are simple and starvation-free. When a VM issues
an idle-with-timeout instruction, it is removed from
scheduler consideration until its timer fires, or a virtual interrupt arrives. As compensation for idling,
a VM receives higher scheduler priority for its next
quantum.
Virtual registers are stored in a page at the beginning of a VM’s (virtual) physical address space.
This page is shared between the VM and the isolation kernel, avoiding the overhead of kernel traps for
register modifications. In other respects, the virtual
registers behave like normal memory (for example,
they can be paged out to disk).
Because Denali’s ISA is based on x86, we can use
existing x86 compilers and linkers when authoring
OS or application code to run in a Denali VM. In
particular, we have been primarily using the gcc C
compiler and the ld linker on Linux, although we
did need to change the link map used by ld to take
Denali’s memory architecture into account.
3.2.2
Memory Management
The (virtual) physical address space exposed to a
VM has two components: a portion that is accessible
to the VM, and a protected portion accessible only to
the isolation kernel. Each VM also has a swap region
allocated on behalf of it by the isolation kernel; this
swap region is striped across local disks. The swap
region is used by the isolation kernel to swap or page
out portions of the VM’s address space. Swap regions are statically allocated at VM creation time,
and are large enough to hold the entire VM-visible
address space. Static allocation drastically reduces
the amount of bookkeeping metadata in the isolation
kernel: each swap region is completely described by
20 bytes of kernel memory. Static allocation wastes
disk capacity in return for performance and scalabil-
ity, but the decreasing cost of storage capacity makes
this trade-off worthwhile.
The isolation kernel is pinned in physical memory, but VMs are paged in on demand. Upon taking
a page fault, the kernel verifies that the faulting VM
hasn’t accessed an illegal virtual address, allocates
necessary page tables, and initiates a read from the
VM’s swap region.
The system periodically redistributes physical
memory from inactive VMs to active VMs. We use
the WSClock [7] page replacement algorithm, which
attempts to maintain each VM’s working set in memory by maintaining a virtual time stamp along with a
clock reference bit. This helps reduce thrashing, and
is more fair to machines that experience heavy paging (such as reactivated machines that are entirely
swapped out). To encourage good disk locality, all
memory buffers for a given VM are clustered together
in the clock circular list.
For the remainder of this paper, we configured
the system to expose only 16MB of accessible (virtual) physical address space to each VM. This models
the challenging scenario of having many small services multiplexed on the same hardware. Because
virtual MMUs are such a recent addition and are still
being performance optimized, we did not turn on virtual MMU support for the experiments presented in
Section 4. Although we hope that enabling virtual
MMU support will not affect our overall performance
results, we have not yet demonstrated this.
3.2.3
I/O Devices and Interrupt Model
Denali emulates a switched Ethernet LAN connecting all VMs. Each VM is assigned a virtual Ethernet NIC; from the perspective of external physical
hosts, it appears as though each VM has its own
physical Ethernet card. VMs interact with the virtual NIC using standard programmed I/O instructions, although the interface to the virtual NIC is
drastically simpler than physical NICs, consisting
only of a packet send and a packet receive operation. On the reception path, the isolation kernel emulates an Ethernet switch by demultiplexing incoming packets into a receive queue for the destination
virtual machine. VMs can only process packets during their scheduler quantum, effectively implementing a form of lazy-receiver processing [11]. On the
transmit path, the kernel maintains a per-machine
queue of outbound packets which a VM can fill during its scheduler quantum. These transmit queues
are drained according to a packet scheduler policy;
Denali currently processes packets in round-robin order from the set of actively sending VMs.
Denali provides virtual disk support to VMs.
The isolation kernel contains a simple file system in
which it manages persistent, fixed-sized virtual disks.
When a VM is instantiated, the isolation kernel exposes a set of virtual disks to it; the VM can initiate asynchronous reads and writes of 4KB blocks
from the disks to which it has been given access. Because virtual disks exist independently of virtual machines, Denali trivially supports optimizations such
as the read-only sharing of virtual disks across VMs.
For example, if multiple VMs all use the same kernel boot image, that boot image can be stored on a
single read-only virtual disk and shared by the VMs.
Denali also emulates a keyboard, console, and
timer devices. These virtual devices do not differ
significantly from physical hardware devices, and we
do not describe them further.
Denali’s batched interrupt model is implemented
by maintaining a bitmask of pending interrupts for
each VM. When a virtual interrupt arrives, the kernel
posts the interrupt to the bitmask, activates the VM
if it is idle, and clears any pending timeouts. When a
VM begins its next quantum, the kernel uploads the
bitmask to a virtual register, and transfers control
to an interrupt handler. A VM can disable virtual
interrupts by setting a virtual register value; VMs
can never directly disable physical interrupts.
3.2.4
Supervisor Virtual Machine
Denali gives special privileges to a supervisor
VM, including the ability to create and destroy other
VMs. Because complexity is a source of security
vulnerabilities, wherever possible we have displaced
complexity from the isolation kernel to the supervisor
VM. For example, the isolation kernel does not have
a network stack: if a remote VM image needs to be
downloaded for execution, this is done by the supervisor VM. Similarly, the supervisor VM keeps track
of the association between virtual disks and VMs,
and is responsible for initializing or loading initial
disk images into virtual disks. The supervisor VM
can be accessed via the console, or through a simple
telnet interface. In a production system, the security
of the supervisor VM should be enhanced by using a
secure login protocol such as ssh.
3.3
Ilwaco Guest OS
Although the Denali virtual machine interface is
functionally complete, it is not a convenient interface
for developing applications. Accordingly, we have
developed the Ilwaco guest operating system which
presents customary high-level abstractions. Ilwaco
is implemented as a library, in much the same fashion as a Exokernel libOS. Applications directly link
against the OS; there is no hardware-enforced protection boundary.
Ilwaco contains the Alpine user-level TCP
stack [12], a port of the FreeBSD 3.3 stack. We modified Alpine to utilize Denali’s virtual interrupt and
timer mechanisms, and linked the stack against a device driver for the Denali virtual Ethernet NIC.
Ilwaco contains a thread package that supports
typical thread primitives, locks, and condition variables. If there are no runnable threads, the thread
scheduler invokes the idle-with-timeout virtual instruction to yield the CPU. Ilwaco also contains a
subset of libc, including basic console I/O, string routines, pseudo-random number generation, and memory management. Most of these routines were ported
from OSKit libraries; some functions needed to be
modified to interact with Denali’s virtual hardware.
For example, malloc reads the size of (virtual) physical memory from a virtual register.
4
Evaluation
This section presents a quantitative evaluation
of the Denali isolation kernel. We ran microbenchmarks to (1) quantify the performance of Denali’s
primitive operations, (2) validate our claim that our
virtual architecture modifications result in enhanced
scale, performance, and simplicity, and (3) characterize how our system performs at scale, and why.
As previously mentioned, none of these experiments
were run with the virtual MMU enabled. Additionally, in all experiments, our VMs ran with their data
in virtual core, and as such, they did not exercise the
virtual disks.3
In our experiments, Denali ran on a 1700MHz
Pentium 4 with 256KB of L2 cache, 1GB of RAM,
an Intel PRO/1000 PCI gigabit Ethernet card connected to an Intel 470T Ethernet switch, and three 80
GB 7200 RPM Maxtor DiamondMax Plus IDE drives
with 2 MB of buffering each. For any experiment involving the network, we used a 1500 byte MTU. To
generate workloads for network benchmarks, we used
a mixture of 1700MHz Pentium 4 and 930MHz Pentium III machines.
4.1
Basic System Performance
To characterize Denali’s performance, we measured the context switching overhead between VMs,
and the swap disk subsystem performance. We
also characterized virtualization overhead by analyzing packet dispatch latency, and by comparing the
application-level TCP and HTTP throughput of Denali with that of BSD.
3 Of course, our scaling experiments do stress the swapping
functionality in the isolation kernel itself.
4.1.1
VM Context Switching Overhead
To measure context-switching overhead, we considered two workloads: a “worst-case” that cycles
through a large memory buffer between switches,
and a “best-case” that does not touch memory between switches. For the worst-case workload, context
switch time starts at 3.9 µs for a single virtual machine, and increases to 9 µs for two or more VMs.
For the best-case workload, the context switch time
starts at 1.4 µs for a single virtual machine, and it increases slightly as the number of VMs increases; the
slight increases coincide with the points at which the
capacity of the L1 and L2 caches become exhausted.
These results are commensurate with process context
switching overheads in modern OSs.
4.1.2
Swap Disk Microbenchmarks
Denali stripes VM swap regions across physical disks. To better understand factors that influence swap performance at scale, we benchmarked Denali’s disk latency and throughput for up to three
attached physical disks. The results are presented
in Table 1; all measured throughputs and latencies
were observed to be limited by the performance of
the physical disks, but not the Denali isolation kernel. For three disks, a shared PCI bus became the
bottleneck, limiting sequential throughput.
4.1.3
Packet Dispatch Latency
Figure 2 shows packet processing costs for
application-level UDP packets, for both 100 and 1400
byte packets. A transmitted packet first traverses
the Alpine TCP/IP stack and then is processed by
the guest OS’s Ethernet device driver. This driver
signals the virtual NIC using a PIO, resulting in a
trap to the isolation kernel. Inside the kernel, the
virtual NIC implementation copies the packet out of
the guest OS into a transmit FIFO. Once the network scheduler has decided to transmit the packet,
the physical device driver is invoked. Packet reception essentially follows the same path in reverse.
On the transmission path, our measurement ends
when the physical device driver signals to the NIC
that a new packet is ready for transmission; packet
transmission costs therefore do not include the time it
takes the packet to be DMA’ed into the NIC, the time
it takes the NIC to transmit the packet on the wire, or
the interrupt that the NIC generates to indicate that
the packet has been transmitted successfully. On the
reception path, our measurement starts when a physical interrupt arrives from the NIC; packet reception
costs therefore include interrupt processing and interacting with the PIC.
The physical device driver and VM’s TCP/IP
stack incur significantly more cost than the isolation
TCP/ IP Stack
VM’s device driver
12251 / 16415
405 / 825
VM / kernel crossing
VNIC FIFOs
ethernet driver
1246 / 2040
1543 / 1504
write( )
1351 / 1366
ethernet
packet
transmit
TCP/ IP Stack
VM’s device driver
VM / kernel crossing
VNIC FIFOs
VNIC demux
ethernet driver
16255 / 20409
358 / 377
1112 / 1115
5026 / 6144
1975 / 2048
18909 / 18751
read( )
ethernet
packet
arrival
Figure 2: Packet processing overhead: these two timelines illustrate the cost (in cycles) of processing a packet,
broken down across various functional stages, for both packet reception and packet transmission. Each pair of numbers
represents the number of cycles executed in that stage for 100 byte and 1400 byte packets, respectively.
random
throughput
sequential
throughput
1 disk
7.1 / 5.9
2.20 / 2.66
38.2 / 31.5
2 disks
7.0 / 5.8
4.45 / 5.41
75.6 / 63.5
3 disks
7.0 / 5.8
6.71 / 8.10
91.3 / 67.1
Table 1: Swap disk microbenchmarks: latency (ms),
random throughput (MB/s), and sequential throughput (MB/s) versus the number of disks. Numbers separated by a slash are for reads and writes, respectively.
kernel, confirming that the cost of network virtualization is low. The physical driver consumes 43.3% and
38.4% of the total packet reception costs for small
and large packets, respectively. Much of this cost is
due to the Flux OSKit’s interaction with the 8259A
PIC; we plan on modifying the OSKit to use the more
efficient APIC in the future. The TCP stack consumes 37.3% and 41.8% of a small and large packet
processing time, respectively.
The transmit path incurs two packet copies and
one VM/kernel boundary crossing; it may be possible to eliminate these copies using copy-on-write
techniques. The receive path incurs the cost of a
packet copy, a buffer deallocation in the kernel, and
a VM/kernel crossing. The buffer deallocation procedure attempts to coalesce memory back into a global
pool and is therefore fairly costly; with additional
optimization, we believe we could eliminate this.
4.1.4
TCP and HTTP Throughput
As a second measurement of networking performance on Denali, we compared the TCP-level
throughput of BSD and a Denali VM running Ilwaco.
To do this, we compiled a benchmark application on
both Denali and BSD, and had each application run a
TCP throughput test to a remote machine. We configured the TCP stacks in all machines to use large
socket buffers. The BSD-Linux connection was able
to attain a maximum throughput of 607 Mb/s, while
Denali-Linux achieved 569 Mb/s, a difference of 5%.
As further evaluation, we measured the performance of a single web server VM running on Denali.
Our home-grown web server serves static content out
of (virtual) physical memory. For comparison, we
600
500
throughput (Mb/s)
latency
400
300
BSD Mb/sec
200
BSD-syscall Mb/sec
Denali Mb/sec
100
0
1
10
100
1000
document size (KB)
Figure 3: Comparing web server performance on
Denali and BSD: performance is comparable, confirming that virtualization overhead is low. The “BSDsyscall” line corresponds to a version of the BSD web
server in which an extra system call was added per packet,
to approximate user-level packet delivery in Denali.
ported our web server to BSD by compiling and linking the unmodified source code against a BSD library
implementation of the Ilwaco system call API. Figure 3 shows the results.
Denali’s application-level performance closely
tracks that of BSD, although for medium-sized documents (50-100KB), BSD outperforms Denali by up
to 40%. This difference in performance is due to the
fact that Denali’s TCP/IP stack runs at the userlevel, implying that all network packets must cross
the user/kernel boundary. In contrast, in BSD, most
packets are handled by the kernel, and only data
destined for the application crosses the user-kernel
boundary. A countervailing force is system calls: in
Denali, system calls are handled within the user-level
by the Ilwaco guest OS; in BSD, system calls must
cross the user-kernel boundary.
For small documents, there are about as many
system calls per connection in BSD (accept, reads,
writes, and close) as there are user/kernel packet
crossings in Denali. For large documents, the system bottleneck becomes the Intel PRO/1000 Ethernet card. Therefore, it is only for medium-sized documents that the packet delivery to the user-level networking stack in Denali induces a noticeable penalty;
we confirmed this effect by adding a system call
per packet to the BSD web server, observing that
with this additional overhead, the BSD performance
450
served load (req/sec)
350
300
250
200
150
100
50
0
0
100
200
300
400
500
600
700
800
900
Served load (req/sec)
500
batched
sync
400
400
100
1000
0
0
Figure 4: Benefits of batched, asynchronous interrupts: Denali’s interrupt model leads to a 30% performance improvement in the web server when compared
to synchronous interrupts, but at large scale (over 800
VMs), paging costs dominate.
closely matched that of Denali even for medium-sized
documents (Figure 3).
Scale, Performance, and Simplicity
and the Denali Virtual Architecture
The virtual architecture exposed by Denali was
designed to enhance scalability, performance, and
simplicity. In this section, we provide quantitative evidence to back these claims. Specifically, we
demonstrate that batched asynchronous interrupts
have performance and scalability benefits, that Denali’s idle-with-timeout instruction is crucial for scalability, that Denali’s simplified virtual NIC has performance advantages over an emulated real NIC, and
that the source code complexity of Denali is substantially less than that of even a minimal Linux kernel.
4.2.1
Batched, Asynchronous Interrupts
Denali utilizes a batched, asynchronous model
for virtual interrupt delivery. In Figure 4, we quantify the performance gain of Denali’s batched, asynchronous interrupt model, relative to the performance of synchronous interrupts. To gather the synchronous interrupt data, we modified Denali’s scheduler to immediately context switch into a VM when
an interrupt arrives for it. We then measured the
aggregate performance of our web server application
serving a 100KB document, as a function of the number of simultaneously running VMs. For a small
number of VMs, there was no apparent benefit, but
up to a 30% gain was achieved with batched interrupts for up to 800 VMs. Most of this gain is attributable to a reduction in context switching frequency (and therefore overhead). For a very large
number of VMs (over 800), performance was dominated by the costs of the isolation kernel paging VMs
in and out of core.
4.2.2
idle-with-timeout
200
# virtual machines
4.2
no-idle-with-timeout
300
Idle-with-timeout Instruction
To measure the benefit of the idle-with-timeout
virtual instruction, we compared the performance of
50
100
150
200
250
300
# virtual machines
Figure 5: Idle-with-timeout benefits: idle-withtimeout leads to higher performance at scale, when compared to an idle instruction with no timeout feature.
web server VMs serving 100KB documents across in
two scenarios. In the first scenario, the VMs exploited the timeout feature: a guest OS with no
schedulable threads invokes the idle-with-timeout instruction with a timeout value set to the smallest
pending TCP retransmission timer. In the second
scenario, VMs did not use the timeout feature, idling
only when there were no schedulable threads and no
pending TCP retransmission timers.
The performance difference was substantial: Figure 5 shows that as the number of VMs scales up,
overall performance drops by more than a factor of
two without the timeout feature. The precipitous
drop in performance for small numbers of VMs happens because the entire offered load is focused on
those few VMs, ensuring that all of them have active connections; an active connection means that
retransmission timers are likely pending, preventing
the VM from idling. As the number of VMs grows,
the same aggregate workload is spread over more
VMs, meaning that any individual VM is less likely
to have active connections preventing it from idling.
This results in an easing off of additional overhead
as the system scales.
In general, a timeout serves as a hint to the isolation kernel; without this hint, the kernel cannot determine whether any software timers inside a VM are
pending, and hence will not know when to reschedule
the VM. As a result, without the timeout feature, a
VM has no choice but to spin inside its idle loop to
ensure that any pending software timers fire.
4.2.3
Simplified Virtual Ethernet
Denali’s virtual Ethernet has been streamlined
for simplicity and performance. Real hardware network adapters often require multiple programmed
I/O instructions to transmit or receive a single
packet. For example, the Linux pcnet32 driver used
by VMWare workstation [31] issues 10 PIOs to receive a packet and 12 PIOs to transmit a packet.
80000
50000
Source lines
40000
20000
Source lines
core kernel
arch. dependent
drivers
60000
storage
networking
virtual memory
other
40000
30000
20000
10000
0
0
Linux
Linux
Denali
(a) Total lines
Denali
(b) Core kernel breakdown
Figure 6: Source code complexity: number of source
lines in Linux 2.4.16 and Denali. Denali is roughly half
the size of Linux in total source lines. Denali’s core kernel
(without device drivers and platform-dependent code) is
an order-of-magnitude smaller than Linux.
VMMs which support unmodified legacy OSs must
trap and emulate these PIOs, resulting in additional
overhead. By contrast, Denali’s virtual Ethernet requires only a single PIO to send or receive a packet.
To estimate the benefit of Denali’s simple virtual
Ethernet, we modified the guest OS device driver to
perform as many PIOs as the pcnet32 driver. Doing
so increased the packet reception cost by 18,381 cycles (10.9 ms) and the packet transmission cost by
22,955 cycles (13.7 ms). This increases the overhead
of receiving a single 100-byte UDP packet by 42%.
4.2.4
Source Code Complexity
As a final measure of the impact of Denali’s virtual architecture, we quantify the size of the Denali
source tree relative to Linux. This comparison therefore gives an indication of how complex it is to implement an isolation kernel that exposes Denali’s architecture, as compared to the complexity of implementing an OS that exports high-level abstractions.
Code size is important because it serves as an indication of the size of the trusted computing base, and
it also impacts how easily the system can be maintained, modified, and debugged over time.
We compared Denali against Linux 2.4.16. For
fairness of comparison, we choose a subset of Linux
files that comprise a “bare-bones” kernel: no module
support, no SMP support, no power management,
only the ext2 file system, limited driver support, and
so on. We use semicolon count as the metric of source
lines, to account for different coding conventions.
Denali contains 26,634 source lines, while Linux
has 66,326 source lines (Figure 6a). Only a small
fraction (18%) of the Denali source is consumed by
the “core kernel”; the remainder is dedicated to device drivers and architecture-dependent routines. Although drivers are known to be more buggy than core
kernel code [8], the drivers used by Denali and “barebones” Linux consist of mature source code that has
not changed substantially over time, e.g., the IDE
driver, terminal support, and PCI bus probing.
In Figure 6b, we present a breakdown of the core
kernel sizes of Denali and Linux. The Linux core kernel is an order-of-magnitude larger than Denali. The
majority of the difference is attributable to Linux’s
implementation of stable storage (the ext2 file system) and networking (TCP/IP) abstractions. By deferring the implementation of complex abstractions
to guest operating systems, Denali realizes a substantial reduction in core kernel source tree size.
4.3
Denali at Scale
In this section, we characterize Denali’s performance at scale. We first analyze two scaling
bottlenecks, which we removed before performing
application-level scaling experiments. We then analyze two applications with fairly different performance requirements and characteristics: a web server
and the Quake II game server.
4.3.1
Scaling Bottlenecks
The number of virtual machines to which our isolation kernel can scale is limited by two factors: permachine metadata maintained by the kernel when a
VM has been completely paged out, and the working
set size of active VMs.
Per-VM kernel metadata: To minimize the
amount of metadata the isolation kernel must maintain for each paged-out VM, wherever possible we allocate kernel resources on demand, rather than statically on VM creation. For example, page tables and
packet buffers are not allocated to inactive VMs. Table 2 breaks down the memory dedicated to each VM
in the system. Each VM requires 8,472 bytes, of
which 97% are dedicated to a kernel thread stack. Although we could use continuations [10] to bundle up
the kernel stack after paging a VM out, per-VM kernel stacks have simplified our implementation. Given
the growing size of physical memory, we feel this is an
acceptable tradeoff: supporting 10,000 VMs requires
81 MB of kernel metadata, which is less than 4% of
memory on a machine with 2GB of RAM.
VM working set size: The kernel cannot control the size of a VM’s working set, and the kernel’s paging mechanism may cause a VM to perform poorly if the VM scatters small memory objects across its pages. One instance where memory
locality is especially important is the management of
the mbuf packet buffer pool inside the BSD TCP/IP
stack of our Ilwaco guest OS. Initially, mbufs are
allocated from a large contiguous byte array; this
“low entropy” initial state means that a request that
touches a small number of mbufs would only touch a
single page in memory. After many allocations and
Size (bytes)
thread stack
8192
register file
24
swap region metadata
20
paging metadata
40
virtual Ethernet structure
80
pending alarms
8
VM boot command line
64
other
72
Total
8472
6000
served load (request/sec)
Component
1712 KB total
linked list
2000
1000
0
256
512
768
1024
400
600
800
1000
1200
1400
1600
served load (requests/sec)
100
80
60
40
20
0
0
2000
4000
6000
# virtual machines
8000
10000
(b) small document; large # virtual machines
1120 KB
0
200
(a) small document; small # virtual machines
pseudoregisters
code
static data
web object
dynamic data (incl. mbufs)
352 KB
3000
# virtual machines
scribes the residual kernel footprint of each VM, assuming the VM has been swapped out.
hash table
4000
0
Table 2: Per-VM kernel metadata: this table de-
944 KB total
5000
1280
1536
1792
2048
working set size (kilobytes)
Figure 8: In-core vs. out-of-core: (a) shows aggregate performance up to the “cliff” at approximately 1000
VMs; (b) shows aggregate performance beyond the cliff.
Figure 7: Mbuf entropy and memory footprint:
eliminating mbuf entropy with a hash table can halve
memory footprint.
deallocations from the mbuf pool, the default BSD
implementation of the mbuf pool scatters back-toback mbuf allocations across pages: in the worst case,
as many pages are necessary as referenced mbufs, increasing the memory footprint of a VM.
We have observed the effects of mbuf entropy in
practice, especially if a VM is subjected to a burst
of high load. Figure 7 shows the effect of increasing
the offered load on a web server inside a VM. The
memory footprint of the VM using the default, linked
list BSD implementation of the mbuf pool increases
by 83% as the system reaches overload. We improved
memory locality by replacing the linked list with a
hash table that hashes mbufs to buckets based on
the memory address of the mbufs; by allocating from
hash buckets, the number of memory pages used is
reduced. With this improvement, the VM’s memory
footprint remained constant across all offered loads.
The savings in memory footprint resulted in nearly
a factor of two performance improvement for large
numbers of concurrent web server VMs.
More generally, the mbuf entropy problem is indicative of two larger issues inherent in the design
of a scalable isolation kernel. First, the paging behavior of guest operating systems is a crucial component of overall performance; most existing OSs are
pinned in memory and have little regard for memory
locality. Second, memory allocation and deallocation
routines (e.g., garbage collection) may need to be reexamined to promote memory locality; existing work
on improving paging performance in object-oriented
languages could prove useful.
4.3.2
Web server performance
To understand the factors that influence scalability for a throughput-centric workload, we analyzed
Denali’s performance when running many web server
VMs. We found that three factors strongly influenced
scalability: disk transfer block size, the popularity
distribution of requests across VMs, and the object
size transferred by each web server.
To evaluate these factors, we used a modified
version of the httperf HTTP measurement tool to
generate requests across a parameterizable number
of VMs. We modified the tool to generate requests
according to a Zipf distribution with parameter α.
We present results for repeated requests to a small
object of 2,258 bytes (approximately the median web
object size). Requests of a larger web object (134,007
bytes) were qualitatively similar.
The performance of Denali at scale falls into two
regimes. In the in-core regime, all VMs fit in memory, and the system can sustain nearly constant aggregate throughput independent of scale. When the
number of active VMs grows to a point that their
combined working sets exceed the main memory capacity, the system enters the disk-bound regime. Figure 8 demonstrates the sharp performance cliff separating these regimes.
In-core regime: To better understand the performance cliff, we evaluated the effect of two variables: disk block transfer size, and object popularity
distribution. Reducing the block size used during
80
6000
4k blocks, random
16k blocks, random
16k blocks, heavy-tailed
60
req/sec
served load (req/sec)
5000
4000
40
20
3000
0
0
0.5
2000
1
1.5
alpha
1000
0
0
500
1000
1500
2000
# virtual machines
Figure 10: Out-of-core performance vs. α: increasingly skewed popularity distributions have better out-ofcore performance; this data was gathered for 4,000 VMs
serving the small web object, and a block size of 32KB.
25
Figure 9: Block size and popularity distribution:
paging can improve performance by reducing internal fragmentation, and as a consequence, reducing a
VM’s in-core footprint. This has the side-effect of delaying the onset of the performance cliff (Figure 9):
by using a small block size, we can push the cliff to
beyond 1000 VMs.
Disk-bound regime: To illustrate Denali’s
performance in the disk-bound regime, we examined
web server throughput for 4,000 VMs serving the
“small” document; the footprint of 4,000 VMs easily
exceeds the size of main memory. Once again, we
considered the impact of block size and object popularity on system performance.
To explore the effect of heavy-tailed distributions, we fixed the disk block transfer size at 32 kilobytes, and varied the Zipf popularity parameter α.
As α increases, the distribution becomes more concentrated on the popular VMs. Unlike the CPU and
the network, Denali’s paging policy is purely demand
driven; as a result, Denali is able to capitalize on the
skewed distribution, as shown in Figure 10.
Figure 11 illustrates the effect of increased block
size on throughput. As a point of comparison, we
include results from a performance model that predicts how much performance our three disk subsystem should support, given microbenchmarks of its
read and write throughput, assuming that each VM’s
working set is read in using random reads and written out using a single sequential write. Denali’s performance for random requests tracks the modeled
throughput, differing by less than 35% over the range
of block sizes considered.
This suggests that Denali is utilizing most of
the available raw disk bandwidth, given our choice
of paging policy. For heavy-tailed requests, Denali
is able to outperform the raw disk bandwidth by
caching popular virtual machines in main memory.
To improve performance beyond that which we have
reported, the random disk reads induced by paging
20
req/sec
this graph shows the effect of varying block size and popularity distribution on the “cliff”; the web servers were
serving a 2,258 byte document.
15
10
heavy-tailed requests
disk model
random requests
5
0
0
20
40
60
80
100
120
140
block size (kb)
Figure 11: Out-of-core performance vs. block size:
increased block size leads to increased performance in the
out-of-core regime.
would need to be converted into sequential reads; this
could be accomplished by reorganizing the swap disk
layout so that the working sets of VMs are laid out
sequentially, and swapped in rather than paged in.
4.3.3
Quake II server performance
As a second workload to test the scalability of
Denali, we ported the GPL’ed Quake II game server
to Ilwaco. Quake II is a latency-sensitive multiplayer
game. Each server maintains state for a single game
session; clients participating in the session send and
receive coordinate updates from the server. We use
two metrics as a measure of the quality of the game
experience: the latency between a client sending an
update to the server and receiving back a causally dependent update, and the throughput of updates sent
from the server. Steady latency and throughput are
necessary for a smooth, lag-free game experience.
To generate load, we used modified Linux Quake
II clients to play back a recorded game session to a
server; for each server, we ran a session with four
clients. As a test of scalability, we measured the
throughput and latency of a Quake server as a function of the number of concurrently active Quake
VMs. Figure 12 shows our results.
As we scaled up the number of VMs (and
the number of clients generating load), the average
throughput and latency of each server VM remained
essentially constant. At 32 VMs, we ran out of client
throughput (rounds/s)
12
10
8
6
4
2
0
0
5
10
15
20
25
30
# VMs
(a) Quake II server throughput (per VM)
120
latency (ms)
100
80
60
40
20
0
0
5
10
15
20
25
30
# VMs
(b) Quake II server latency
Figure 12: Quake II server scaling benchmarks:
even with 32 concurrent active Quake II server VMs, the
throughput and latency to each server remained constant.
At 32 servers, we ran out of client machines to drive load.
machines to generate load. Even with this degree of
multiplexing, both throughput and latency remained
constant, suggesting that the clients’ game experiences would still be good.
Although the Quake server is latency-sensitive, it
is in many ways an ideal application for Denali. The
default server configuration self-imposes a delay of
approximately 100 ms between update packets, in effect introducing a sizable “latency buffer” that masks
queuing and scheduling effects in Denali. Additionally, because the server-side of Quake is much less
computationally intensive than the client-side, multiplexing large numbers of servers is quite reasonable.
5
Related Work
We consider related work along two axes: operating system architectures, and techniques for isolating
untrusted code.
5.1
OS architectures
The idea of hosting multiple isolated protection
contexts on a single machine is not new: Rushby’s
separation kernel [28] is an instance of this idea. Denali puts these ideas into practice, and explores the
systems issues when scaling to a large number of protection domains.
Exokernels [20] eliminate high-level abstractions
to enable OS extensibility. Denali differs from Exok-
ernels in its approach to naming: Denali exposes virtual, private name spaces, whereas Exokernels expose
the physical names of disk, memory, and network
resources. The Exokernel’s global namespace allow
resources to be shared freely, necessitating complex
kernel mechanisms to regulate sharing.
Denali is similar to microkernel operating systems like Mach [1]. Indeed, Denali’s VMs could be
viewed as single-threaded applications on a low-level
microkernel. However, the focus of microkernel research has been to push OS functionality into shared
servers, which are themselves susceptible to the problems of high-level abstractions and data sharing. Denali emphasizes scaling to many untrusted applications, which was never an emphasis of microkernels.
Nemesis [23] shares our goal of isolating performance between competing applications. Nemesis
adopts a similar approach, pushing most kernel functionality to user-level. Nemesis was not designed to
sandbox untrusted code; Nemesis applications share
a global file system and a single virtual address space.
The Fluke OS [13] proposes a recursive virtual
machine model, in which a parent can re-implement
OS functionality on behalf of its children. Like Denali, Fluke exposes private namespaces through its
“state-encapsulation” property. The primary motivation for this is to support checkpointing and migration, though the security benefits are alluded to
in [22]. Denali exposes a virtual hardware API,
whereas Fluke virtualizes at the level of OS API.
By virtualizing below abstractions, Denali’s kernel
is simple, and we avoid layer-below vulnerabilities.
Virtual machine monitors have served as the
foundation of several “security kernels” [21]. More
recently, the NetTop proposal aims to create secure
virtual workstations running on VMWare [24]. Denali differs from these efforts in that we aim to provide scalability as well as isolation. We assume a
weaker threat model; for example, we are not concerned with covert channels between VMs.
VMMs like Disco [6] and VM/370 [9] have the
goal of supporting legacy systems, and therefore minimize architectural modifications to maintain compatibility. In comparison, isolation kernels rely on
virtualization for isolation: backwards compatibility
is not their primary goal. As a result, isolation kernels have the freedom to make strategic changes to
the exposed virtual architecture for scalability, performance, and simplicity.
5.2
Enforcing isolation
Many projects provide OS support for isolating untrusted code, including system call interposition [16] and restricted execution contexts [32].
These proposals provide mechanisms for enforcing
the principle of least privilege. However, expressing
an appropriate access control policy requires a security expert to reason about access permissions to
grant applications; this is a difficult task on modern
systems with thousands of files and hundreds of devices. Denali imposes a simple security policy: complete isolation of VMs. This obviates the policy problem, and provides robust isolation for applications
with few sharing requirements.
WindowBox [3] confines applications to a virtual
desktop, imposing a private namespace for files. Because it is implemented inside a conventional OS,
WindowBox’s security is limited by high-level abstractions and global namespaces. For example, all
applications have access to the Windows registry,
which has been involved in many vulnerabilities.
Software VMs (like Java) have been touted as
platforms for isolating untrusted code. Experience
with these systems has demonstrated a tradeoff between security and flexibility. The Java sandbox was
simple and reasonably secure, but lacked the flexibility to construct complex applications. Extensible
security architectures [33] allow more flexibility, but
reintroduce the problem of expressing an appropriate access control policy. Denali avoids this tradeoff
by exposing a raw hardware API, complete with I/O
devices, which allows VMs to build up arbitrary abstractions inside a guest OS. In addition, Denali’s
virtual architecture closely mirrors the underlying
physical architecture, avoiding the need for a complex runtime engine or just-in-time compiler.
The problem of performance isolation has been
addressed by server and multimedia systems [4, 26,
23]. Resource containers demonstrate that OS abstractions for resource management (processes and
threads) are poorly suited to applications’ needs. Denali’s VMs provide a comparable resource management mechanism. We believe that isolation kernels
can provide more robust performance isolation by operating beneath OS abstractions and data sharing.
As an example, Reumann et al. conclude that there
is no simple way to account for the resources in the
file system buffer cache [26].
Finally, numerous commercial and open-source
products provide support for virtual hosting, including freeVSD, Apache virtual hosts, the Solaris resource manager, and Ensim’s ServerXchange. All
work within a conventional OS or application, and
therefore cannot provide the same degree of isolation
as an isolation kernel. Commercial VMMs provide
virtual hosting services, including VMWare’s ESX
server and IBM’s z/VM system. By selectively modifying the underlying physical architecture, Denali
can scale to many more machines for a given hardware base. We are not aware of detailed studies of
the scalability of these systems.
6
Conclusions
This paper presented the design and implementation of the Denali isolation kernel, a virtualization layer that supports the secure multiplexing of a
large number of untrusted Internet services on shared
infrastructure. We have argued that isolation kernels are necessary to provide adequate isolation between untrusted services, and to support scaling to
a large number of Internet services, as required by
cost-efficiency. Quantitative evaluation of our isolation kernel has demonstrated that the performance
overhead of virtualization is reasonable, that our design choices were both necessary and reasonable, and
that our design and implementation can successfully
scale to over 10,000 services on commodity hardware.
We believe that isolation kernels have the potential to dramatically change how Internet services
are deployed. An isolation kernel allows a service to
be “pushed” into third party infrastructure, thereby
separating the management of physical infrastructure
from the management of software services and lowering the barrier to deploying a new service.
7
Acknowledgments
We are grateful for the help of our shepherd,
Robert Morris, whose suggestions have greatly improved the final draft of this paper. We would
also like to thank Brian Bershad, David Wetherall, Neil Spring, Tom Anderson, John Zahorjan, and
Hank Levy for their valuable feedback. This work
also benefitted greatly from many discussions with
the Systems and Networking group at Microsoft Research, particularly Bill Bolosky, Marvin Theimer,
Mike Jones, and John Douceur. This work was supported in part by NSF Career award ANI-0132817,
funding from Intel Corporation, and a gift from Nortel Networks.
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