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A Round Robin preemptive scheduling example with quantum=3. Round-robin (RR) is one of the algorithms employed by process and network schedulers in computing. [1] [2] As the term is generally used, time slices (also known as time quanta) [3] are assigned to each process in equal portions and in circular order, handling all processes without priority (also known as cyclic executive).
The time taken to swap out and swap in a process is 50 ms respectively. With straightforward Round-robin scheduling, every time a context switch occurs, a process would need to be swapped in (because only the 10 least recently used processes are swapped in). Choosing randomly among the processes would diminish the probability to 80% (40/50).
The algorithm used may be as simple as round-robin in which each process is given equal time (for instance 1 ms, usually between 1 ms and 100 ms) in a cycling list. So, process A executes for 1 ms, then process B, then process C, then back to process A.
One common method of logically implementing the fair-share scheduling strategy is to recursively apply the round-robin scheduling strategy at each level of abstraction (processes, users, groups, etc.) The time quantum required by round-robin is arbitrary, as any equal division of time will produce the same results.
Weighted round robin (WRR) is a network scheduler for data flows, but also used to schedule processes. Weighted round robin [ 1 ] is a generalisation of round-robin scheduling . It serves a set of queues or tasks.
In computer science, rate-monotonic scheduling (RMS) [1] is a priority assignment algorithm used in real-time operating systems (RTOS) with a static-priority scheduling class. [2] The static priorities are assigned according to the cycle duration of the job, so a shorter cycle duration results in a higher job priority.
In weighted round robin scheduling, the fraction of bandwidth used depend on the packet's sizes. Compared with WFQ scheduler that has complexity of O(log(n)) ( n is the number of active flows/queues ), the complexity of DRR is O(1) , if the quantum Q i {\displaystyle Q_{i}} is larger than the maximum packet size of this flow.
Hence such tasks do not get less processor time than the tasks that are constantly running. The complexity of the algorithm that inserts nodes into the cfs_rq runqueue of the CFS scheduler is O(log N), where N is the total number of entities. Choosing the next entity to run is made in constant time because the leftmost node is always cached.