O(1) scheduler

An O(1) scheduler is a kernel scheduling design that can schedule processes within a constant amount of time, regardless of how many processes are running on the operating system. In Linux, it has replaced the previously used O(n) scheduler. One of the major goals of operating system designers is to minimize overhead and jitter of OS services, so that application programmers who use them endure less of a performance impact. O(1) scheduler providing "constant time" scheduling services has helped in this regard.

In the realm of real-time operating systems, deterministic execution is key, and an O(1) scheduler is able to provide scheduling services with a fixed upper-bound on execution times. In versions of Linux kernel 2.6 prior to 2.6.23, the scheduler used is an O(1) scheduler by Ingo Molnár. The scheduler used thereafter is the Completely Fair Scheduler (CFS), also by Ingo Molnár, which runs in O(log N) time.

The Linux scheduler was overhauled completely with the release of kernel 2.6. This new scheduler is called the O(1) scheduler. The algorithm used by the O(1) scheduler relies on active and expired arrays of processes to achieve constant scheduling time. Each process is given a fixed time quantum, after which it is preempted and moved to the expired array. Once all the tasks from the active array have exhausted their time quantum and have been moved to the expired array, an array switch takes place. Because the arrays are accessed only via pointer, switching them is as fast as swapping two pointers. This switch makes the active array the new empty expired array, while the expired array becomes the active array.

An algorithm operates on input, and the size of that input usually determines its running time. Big O notation is used to denote the growth rate of an algorithm's execution time based on the amount of input. For example, the running time of an O(n) algorithm increases linearly as the input size n grows. The running time of an O(nˆ2) algorithm grows quadratically. If it is possible to establish a constant upper bound on the running time of an algorithm, it is considered to be O(1) (one might say it runs in "constant time"). That is, an O(1) algorithm is guaranteed to complete in a certain amount of time regardless of the size of the input.

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