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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).
Weighted round robin [1] is a generalisation of round-robin scheduling. It serves a set of queues or tasks. Whereas round-robin cycles over the queues or tasks and gives one service opportunity per cycle, weighted round robin offers to each a fixed number of opportunities, as specified by the configured weight which serves to influence the ...
Round Robin: This is similar to the AIX Version 3 scheduler round-robin scheme based on 10 ms time slices. When a RR thread has control at the end of the time slice, it moves to the tail of the queue of dispatchable threads of its priority. Only fixed-priority threads can have a Round Robin scheduling policy.
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.
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.
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Example of a round-robin tournament with 10 participants. A round-robin tournament or all-play-all tournament is a competition format in which each contestant meets every other participant, usually in turn. [1] [2] A round-robin contrasts with an elimination tournament, wherein participants are eliminated after a certain number of wins or losses.
Fair queuing is an example of a max-min fair packet scheduling algorithm for statistical multiplexing and best-effort networks, since it gives scheduling priority to users that have achieved lowest data rate since they became active. In case of equally sized data packets, round-robin scheduling is max-min fair.