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This chapter is from the book

This chapter is from the book

3.5 Other Topics

Some additional kernel and operating system topics worth summarizing are PGO kernels, Unikernels, microkernels, hybrid kernels, and distributed operating systems.

3.5.1 PGO Kernels

Profile-guided optimization (PGO), also known as feedback-directed optimization (FDO), uses CPU profile information to improve compiler decisions [Yuan 14a]. This can be applied to kernel builds, where the procedure is:

  1. While in production, take a CPU profile.

  2. Recompile the kernel based on that CPU profile.

  3. Deploy the new kernel in production.

This creates a kernel with improved performance for a specific workload. Runtimes such as the JVM do this automatically, recompiling Java methods based on their runtime performance, in conjunction with just-in-time (JIT) compilation. The process for creating a PGO kernel instead involves manual steps.

A related compile optimization is link-time optimization (LTO), where an entire binary is compiled at once to allow optimizations across the entire program. The Microsoft Windows kernel makes heavy use of both LTO and PGO, seeing 5 to 20% improvements from PGO [Bearman 20]. Google also use LTO and PGO kernels to improve performance [Tolvanen 20].

The gcc and clang compilers, and the Linux kernel, all have support for PGO. Kernel PGO typically involves running a specially instrumented kernel to collect profile data. Google has released an AutoFDO tool that bypasses the need for such a special kernel: AutoFDO allows a profile to be collected from a normal kernel using perf(1), which is then converted to the correct format for compilers to use [Google 20a].

The only recent documentation on building a Linux kernel with PGO or AutoFDO is two talks from Linux Plumber’s Conference 2020 by Microsoft [Bearman 20] and Google [Tolvanen 20].15

3.5.2 Unikernels

A unikernel is a single-application machine image that combines kernel, library, and application software together, and can typically run this in a single address space in either a hardware VM or on bare metal. This potentially has performance and security benefits: less instruction text means higher CPU cache hit ratios and fewer security vulnerabilities. This also creates a problem: there may be no SSH, shells, or performance tools available for you to log in and debug the system, nor any way to add them.

For unikernels to be performance tuned in production, new performance tooling and metrics must be built to support them. As a proof of concept, I built a rudimentary CPU profiler that ran from Xen dom0 to profile a domU unikernel guest and then built a CPU flame graph, just to show that it was possible [Gregg 16a].

Examples of unikernels include MirageOS [MirageOS 20].

3.5.3 Microkernels and Hybrid Kernels

Most of this chapter discusses Unix-like kernels, also described as monolithic kernels, where all the code that manages devices runs together as a single large kernel program. For the microkernel model, kernel software is kept to a minimum. A microkernel supports essentials such as memory management, thread management, and inter-process communication (IPC). File systems, the network stack, and drivers are implemented as user-mode software, which allows those user-mode components to be more easily modified and replaced. Imagine not only choosing which database or web server to install, but also choosing which network stack to install. The microkernel is also more fault-tolerant: a crash in a driver does not crash the entire kernel. Examples of microkernels include QNX and Minix 3.

A disadvantage with microkernels is that there are additional IPC steps for performing I/O and other functions, reducing performance. One solution for this is hybrid kernels, which combine the benefits of microkernels and monolithic kernels. Hybrid kernels move performance-critical services back into kernel space (with direct function calls instead of IPC) as they are with a monolithic kernel, but retains the modular design and fault tolerance of a micro kernel. Examples of hybrid kernels include the Windows NT kernel and the Plan 9 kernel.

3.5.4 Distributed Operating Systems

A distributed operating system runs a single operating system instance across a set of separate computer nodes, networked together. A microkernel is commonly used on each of the nodes. Examples of distributed operating systems include Plan 9 from Bell Labs, and the Inferno operating system.

While an innovative design, this model has not seen widespread use. Rob Pike, co-creator of Plan 9 and Inferno, has described various reasons for this, including [Pike 00]:

“There was a claim in the late 1970s and early 1980s that Unix had killed operating systems research because no one would try anything else. At the time, I didn’t believe it. Today, I grudgingly accept that the claim may be true (Microsoft notwithstanding).”

On the cloud, today’s common model for scaling compute nodes is to load-balance across a group of identical OS instances, which may scale in response to load (see Chapter 11, Cloud Computing, Section 11.1.3, Capacity Planning).

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