Capstone: virtualization, containers and the operating system end to end

This final lesson draws the course together in two ways. First, it follows a single command from the shell through the kernel and back, touching processes, system calls, pipes, scheduling, memory and files along the way. Second, it looks at virtualization and containers, the technologies that let one physical machine run many isolated operating systems or applications and that underpin every cloud platform. Both build directly on the mechanisms of earlier lessons: privilege levels and traps (Lesson 1), processes (Lesson 2), scheduling (Lesson 4), page tables (Lessons 8 and 9) and file systems (Lessons 10 and 11).

One command, end to end

Type echo hi | wc -c into a shell and press Enter. The terminal driver delivers the line to the shell's read call, waking the shell, which the scheduler had blocked. The shell parses the line and finds a pipeline. It calls pipe to get two file descriptors, then forks twice. The first child points its standard output at the pipe's write end with dup2 and runs echo, a shell built-in, so this child never calls exec. The second child points its standard input at the read end and calls execve on /usr/bin/wc. The kernel replaces that child's address space: it maps wc's code and data from the page cache, with page faults loading pages lazily (Lesson 9). wc reads from the pipe, blocking until data arrives, counts three bytes (h, i and the newline), writes the count, and exits. The parent reaps both children with wait4, and the kernel frees their memory and descriptors.

strace -f -o raw.txt -e trace=pipe2,clone,clone3,execve,dup2,wait4,exit_group sh -c "echo hi | wc -c"
cat trace.txt

$ strace -f -o raw.txt -e trace=pipe2,clone,clone3,execve,dup2,wait4,exit_group sh -c "echo hi | wc -c"
3
$ cat trace.txt
100 execve("/usr/bin/sh", ["sh", "-c", "echo hi | wc -c"], 0x... /* environment */) = 0
100 pipe2([3, 4], 0)                  = 0
100 clone(child_stack=NULL, flags=CLONE_CHILD_CLEARTID|CLONE_CHILD_SETTID|SIGCHLD, child_tidptr=0x...) = 101
101 dup2(4, 1)                        = 1
101 exit_group(0)                     = ?
101 +++ exited with 0 +++
100 --- SIGCHLD {si_signo=SIGCHLD, si_code=CLD_EXITED, si_pid=101} ---
100 clone(child_stack=NULL, flags=CLONE_CHILD_CLEARTID|CLONE_CHILD_SETTID|SIGCHLD, child_tidptr=0x...) = 102
102 dup2(3, 0)                        = 0
100 wait4(-1, [{WIFEXITED(s) && WEXITSTATUS(s) == 0}], 0, NULL) = 101
100 wait4(-1,  <unfinished ...>
102 execve("/usr/bin/wc", ["wc", "-c"], 0x... /* environment */) = 0
102 exit_group(0)                     = ?
102 +++ exited with 0 +++
100 <... wait4 resumed>[{WIFEXITED(s) && WEXITSTATUS(s) == 0}], 0, NULL) = 102
100 --- SIGCHLD {si_signo=SIGCHLD, si_code=CLD_EXITED, si_pid=102} ---
100 wait4(-1, 0x..., WNOHANG, NULL) = -1 ECHILD (No child processes)
100 exit_group(0)                     = ?
100 +++ exited with 0 +++

Every concept from the course appears in these few lines: processes created with clone (the system call behind fork), file descriptors redirected with dup2, a program loaded with execve, blocking on a pipe until the scheduler runs the other process, signals (SIGCHLD) reporting each child's exit, and the parent reaping zombies with wait4. Behind the scenes, each process got its own page tables, copy-on-write pages after the fork, scheduling by CFS or EEVDF on whichever CPU was free, and file reads served from the page cache. The order of lines can differ from run to run because the two children run concurrently, a reminder of Lesson 5.

Virtual machines

A virtual machine (VM) is a software-defined computer that runs a complete operating system, the guest, as if it had real hardware. The software that creates and runs VMs is a hypervisor or virtual machine monitor (VMM). A type 1 (bare-metal) hypervisor runs directly on the hardware and is itself a small operating system: examples are VMware ESXi, Microsoft Hyper-V and Xen, and Linux KVM, which turns the Linux kernel into a hypervisor. A type 2 (hosted) hypervisor, such as VirtualBox or VMware Workstation, runs as an application on a conventional host operating system. Public clouds run type 1 hypervisors: Amazon EC2's Nitro is based on KVM.

The classic technique is trap and emulate. The guest kernel runs in user mode, so whenever it executes a privileged instruction, such as loading a page-table base register or disabling interrupts, the processor traps to the hypervisor, which emulates the instruction's effect on the virtual machine's state. Popek and Goldberg showed in 1974 that this works only if every sensitive instruction, one that reads or changes machine state, is also privileged and so traps. Classic x86 broke this rule.

# Popek and Goldberg: trap-and-emulate works only if every sensitive instruction is privileged
instructions = {
    # name: (sensitive: reads or changes machine state, privileged: traps in user mode)
    "load control register (mov cr3)": (True, True),
    "halt (hlt)":                       (True, True),
    "read flags (pushf)":               (True, False),   # reveals the interrupt flag
    "write flags (popf)":               (True, False),   # silently ignores the interrupt flag in user mode
    "store descriptor table (sgdt)":    (True, False),
    "add":                              (False, False),
}
problems = [n for n, (sens, priv) in instructions.items() if sens and not priv]
for n, (sens, priv) in instructions.items():
    verdict = "trap and emulate" if sens and priv else "runs directly" if not sens else "BREAKS virtualization"
    print(f"{n:<33} sensitive={str(sens):<5} privileged={str(priv):<5} -> {verdict}")
print(f"classic x86 is not virtualizable by trap-and-emulate: {len(problems)} sensitive, unprivileged instructions")

load control register (mov cr3)   sensitive=True  privileged=True  -> trap and emulate
halt (hlt)                        sensitive=True  privileged=True  -> trap and emulate
read flags (pushf)                sensitive=True  privileged=False -> BREAKS virtualization
write flags (popf)                sensitive=True  privileged=False -> BREAKS virtualization
store descriptor table (sgdt)     sensitive=True  privileged=False -> BREAKS virtualization
add                               sensitive=False privileged=False -> runs directly
classic x86 is not virtualizable by trap-and-emulate: 3 sensitive, unprivileged instructions

Early x86 hypervisors worked around the problem in two ways. VMware used binary translation, rewriting the guest kernel's problem instructions on the fly. Xen used paravirtualization, modifying the guest kernel to call the hypervisor explicitly (hypercalls). Since 2005-2006, Intel VT-x and AMD-V have added hardware support: a new root mode for the hypervisor, in which the guest kernel runs in its normal ring 0 while sensitive events cause VM exits to the hypervisor. Paravirtualized device drivers such as virtio remain common, because emulating real network cards and disks instruction by instruction is slow.

Virtualizing memory

A guest kernel manages its own page tables mapping guest-virtual to guest-physical addresses, but guest-physical memory is itself virtual: the hypervisor must map it to host-physical memory. Before hardware support, hypervisors kept shadow page tables that combined both mappings and had to trap every guest page-table update. Intel EPT and AMD NPT now do nested paging in hardware: the processor walks the guest's tables and the hypervisor's tables together. The cost appears on a TLB miss, because each of the guest's page-table references needs its own host walk.

# Address translation cost: native paging vs a virtual machine with nested (two-dimensional) paging
def native(levels):
    return levels                                   # one memory reference per page-table level
def nested(guest, host):
    # every guest page-table reference is itself a guest-physical address that needs a host walk,
    # and the final guest-physical address needs one more host walk
    return (guest + 1) * (host + 1) - 1
for g, h in ((4, 4), (5, 5)):
    print(f"{g}-level guest on {h}-level host: native walk {native(g)} references, nested walk {nested(g, h)}")
tlb_hit = 0.99
for name, walk in (("native", native(4)), ("nested", nested(4, 4))):
    avg = 1 + (1 - tlb_hit) * walk                  # memory references per access, TLB lookup free
    print(f"{name:<6}: {avg:.2f} memory references per access at a {tlb_hit:.0%} TLB hit rate")

4-level guest on 4-level host: native walk 4 references, nested walk 24
5-level guest on 5-level host: native walk 5 references, nested walk 35
native: 1.04 memory references per access at a 99% TLB hit rate
nested: 1.24 memory references per access at a 99% TLB hit rate

This is why large pages (Lesson 9) are especially valuable in virtual machines: they shorten both walks and multiply TLB reach. Hypervisors also overcommit memory using techniques from Lesson 9: ballooning, in which a driver in the guest allocates memory to hand pages back to the host; page sharing, which merges identical pages across VMs as copy-on-write (Linux's KSM); and host-level swapping as a last resort. Live migration copies a running VM's memory to another host while it runs, re-sending dirtied pages, then pauses it for a fraction of a second to move the rest, which lets clouds patch hardware without stopping customers' workloads.

Containers

A container isolates a group of processes while they share the host's kernel. There is no guest operating system: a containerized web server is an ordinary Linux process that the kernel shows a restricted view of the system. Two kernel features make this possible. Namespaces give a process its own view of a global resource: process IDs, mount points, network interfaces, host name, users, inter-process communication and more. Control groups (cgroups) limit and account for the resources a group of processes may use: CPU time (through CFS quotas, Lesson 4), memory (with a per-group out-of-memory killer, Lesson 9), input and output bandwidth and the number of processes.

ls /proc/self/ns
hostname
unshare --user --map-root-user --uts sh -c 'hostname lab379; hostname'
hostname
unshare --user --map-root-user --pid --fork --mount-proc ps -o pid,comm
unshare --user --map-root-user --net sh -c 'tail -n +3 /proc/net/dev | cut -c1-30'

$ ls /proc/self/ns
cgroup
ipc
mnt
net
pid
pid_for_children
time
time_for_children
user
uts
$ hostname
vm
$ unshare --user --map-root-user --uts sh -c 'hostname lab379; hostname'
lab379
$ hostname
vm
$ unshare --user --map-root-user --pid --fork --mount-proc ps -o pid,comm
    PID COMMAND
      1 ps
$ unshare --user --map-root-user --net sh -c 'tail -n +3 /proc/net/dev | cut -c1-30'
    lo:       0       0    0

The transcript shows each namespace type the process belongs to, then creates new ones without any special container software. In a new UTS namespace, changing the host name does not affect the real machine. In a new PID namespace, ps sees itself as process 1, because the namespace has its own numbering, and the first process in it acts as that namespace's init. In a new network namespace the only interface is the loopback device lo, with no connection to the outside until one is configured. The user namespace lets an ordinary user be root inside the namespace without root rights outside, which is how rootless containers work.

Container engines such as Docker and Podman combine namespaces and cgroups with layered images (stacked read-only file system layers with copy-on-write on top, using the overlay file system) and security filters: seccomp restricts which system calls a container may make, and capabilities split root's powers into pieces. Kubernetes orchestrates containers across many machines, scheduling them onto nodes much as an operating system schedules threads onto CPUs, a topic for CMPUT-450.

The two are often combined. Cloud providers run each customer's containers inside VMs, and lightweight microVM hypervisors such as AWS Firecracker (used by Lambda) boot a minimal Linux guest in about 125 ms, giving VM-strength isolation with nearly container-like speed. gVisor takes another route, intercepting a container's system calls with a user-space kernel written in Go.

The operating system as a security boundary

Everything in this course is also a security mechanism. Dual-mode execution keeps applications away from hardware; virtual memory keeps processes out of each other's memory; file permissions guard data; namespaces and hypervisors separate tenants. Each boundary has been attacked. Meltdown (2018) let user programs read kernel memory through speculative execution, and the fix, kernel page-table isolation, made every system call more expensive by switching page tables on kernel entry. Container escapes exploit kernel bugs reachable through the shared system call interface. CMPUT-331 covers exploitation and defences such as address space layout randomization (ASLR), stack canaries and non-executable memory in depth, and CMPUT-450 covers the distributed systems that run across many machines.

Where operating systems are going

Several trends are reshaping operating systems. Memory-safe languages are entering kernels: Linux has accepted Rust for drivers since version 6.1, and Android and Windows are rewriting components in Rust, aiming at the memory-safety bugs behind most serious vulnerabilities. eBPF lets verified programs run safely inside the Linux kernel for tracing, networking and security, without kernel modules. io_uring offers shared-memory queues for submitting input and output without a system call per operation. Heterogeneous processors with performance and efficiency cores make scheduling harder, and persistent memory and disaggregated memory blur the line between memory and storage. The core ideas of this course, protection, abstraction, concurrency, resource management and persistence, will remain.

Worked examples