Real-Time and Performance Improvements in the 2.6 Linux Kernel
The Linux kernel, the core of any Linux distribution, constantly is evolving to incorporate new technologies and to improve performance, scalability and usability. Every new kernel release adds support for new hardware, but major version upgrades of the kernel, such as the 2.6 Linux kernel, go beyond incremental improvements by introducing fundamental changes in kernel internals. Many of the changes to the internals of the 2.6 Linux kernel have a significant impact on the overall performance of Linux systems across the board, independent of hardware improvements. The 2.6 kernel provides substantial improvements in system responsiveness, a significant reduction in process- and thread-related kernel overhead and a commensurate reduction in the time between when a task is scheduled and when it begins execution.
Released in late 2003, the 2.6 kernel now is the core of Linux distributions from almost every major Linux vendor in the enterprise, desktop and embedded arenas. Kernel and system performance are critical to focused markets such as embedded computing, where high-priority tasks often must execute and complete in real time, without being interrupted by the system. However, system performance and throughput in general equally are important to the increasing adoption of Linux on the desktop and the continuing success of Linux in the enterprise server market.
This article discusses the nature of real-time and system parameters that affect performance and highlights the core improvements in performance and responsiveness provided by the 2.6 kernel. Performance and responsiveness remain active development areas, and this article discusses several current approaches to improving Linux system performance and responsiveness as well as to achieving real-time behavior. Kernel and task execution performance for various Linux kernels and projects is illustrated by graphed benchmark results that show the behavior of different kernel versions under equivalent loads.
Higher performance often can be realized by using more and better hardware resources, such as faster processors, larger amounts of memory and so on. Although this may be an adequate solution in the data center, it certainly is not the right approach for many environments. Embedded Linux projects, in particular, are sensitive to the cost of the underlying hardware. Similarly, throwing faster hardware and additional memory at performance and execution problems only masks the problems until software requirements grow to exceed the current resources, at which time the problems resurface.
It therefore is important to achieve high performance in Linux systems through improvements to the core operating system, in a hardware-agnostic fashion. This article focuses on such intrinsic Linux performance measurements.
A real-time system is one in which the correctness of the system depends not only on performing a desired function but also on meeting a set of associated timing constraints. There are two basic classes of real-time systems, soft and hard. Hard real-time systems are those in which critical tasks must execute within a specific time frame or the entire system fails. A classic example of this is a computer-controlled automotive ignition system—if your cylinders don't fire at exactly the right times, your car isn't going to work. Soft real-time systems are those in which timing deadlines can be missed without necessarily causing system failure; the system can recover from a temporary lack of responsiveness.
In both of these cases, a real-time operating system executes high-priority tasks first, within known, predictable time frames. This means that the operating system cannot impose undue overhead in task scheduling, execution and management. If the overhead of tasks increases substantially as the number of tasks grows, overall system performance degrades as additional time is required for task scheduling, switching and rescheduling. Predictability therefore is a key concept in a real-time operating system. If you cannot predict the overall performance of a system at any given time, you cannot guarantee that tasks will start or resume with predictable latencies when you need them or that they will finish within a mandatory time frame.
The 2.6 Linux kernel introduced a new task scheduler whose execution time is not affected by the number of tasks being scheduled. This is known as an O(1) scheduler in big-O algorithmic notation, where O stands for order and the number in parentheses gives the upper bound of worst-case performance based on the number of elements involved in the algorithm. O(N) would mean that the efficiency of the algorithm is dependent on the number of items involved, and O(1) means that the behavior of the algorithm and therefore the scheduler, in this case, is the same in every case and is independent of the number of items scheduled.
The time between the point at which the system is asked to execute a task and the time when that task actually begins execution is known as latency. Task execution obviously is dependent on the priority of a given task, but assuming equal priorities, the amount of time that an operating system requires in order to schedule and begin executing a task is determined both by the overhead of the system's task scheduler and by what else the system is doing. When you schedule a task to be executed by putting it on the system's run queue, the system checks to see if the priority of that task is higher than that of the task currently running. If so, the kernel interrupts the current task and switches context to the new task. Interrupting a current task within the kernel and switching to a new task is known as kernel preemption.
Unfortunately, the kernel cannot always be preempted. An operating system kernel often requires exclusive access to resources and internal data structures in order to maintain their consistency. In older versions of the Linux kernel, guaranteeing exclusive access to resources often was done through spin-locks. This meant the kernel would enter a tight loop until a specific resource was available or while it was being accessed, increasing the latency of any other task while the kernel did its work.
The granularity of kernel preemption has been improving steadily in the last few major kernel versions. For example, the GPL 2.4 Linux kernel from TimeSys, an embedded Linux and tools vendor, provided both an earlier low-latency scheduler and a fully preemptible kernel. During the 2.4 Linux kernel series, Robert Love of Novell/Ximian fame released a well-known kernel patch that enabled higher preemption and that could be applied to the standard Linux kernel source. Other patches, such as a low-latency patch from Ingo Molnar, a core Linux kernel contributor since 1995, further extended the capabilities of this patch by reducing latency throughout the kernel. A key concept for the TimeSys products and these patches was to replace spin-locks with mutexes (mutual exclusion mechanisms) whenever possible. These provide the resource security and integrity required by the kernel without causing the kernel to block and wait. The core concepts pioneered by these patches now are integral parts of the 2.6 Linux kernel.
Practical Task Scheduling Deployment
July 20, 2016 12:00 pm CDT
One of the best things about the UNIX environment (aside from being stable and efficient) is the vast array of software tools available to help you do your job. Traditionally, a UNIX tool does only one thing, but does that one thing very well. For example, grep is very easy to use and can search vast amounts of data quickly. The find tool can find a particular file or files based on all kinds of criteria. It's pretty easy to string these tools together to build even more powerful tools, such as a tool that finds all of the .log files in the /home directory and searches each one for a particular entry. This erector-set mentality allows UNIX system administrators to seem to always have the right tool for the job.
Cron traditionally has been considered another such a tool for job scheduling, but is it enough? This webinar considers that very question. The first part builds on a previous Geek Guide, Beyond Cron, and briefly describes how to know when it might be time to consider upgrading your job scheduling infrastructure. The second part presents an actual planning and implementation framework.
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