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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.
On the other hand, if a new user starts a process on the system, the scheduler will reapportion the available CPU cycles such that each user gets 20% of the whole (100% / 5 = 20%). Another layer of abstraction allows us to partition users into groups, and apply the fair share algorithm to the groups as well.
The Completely Fair Scheduler (CFS) was a process scheduler that was merged into the 2.6.23 (October 2007) release of the Linux kernel. It was the default scheduler of the tasks of the SCHED_NORMAL class (i.e., tasks that have no real-time execution constraints) and handled CPU resource allocation for executing processes , aiming to maximize ...
A WFQ scheduler where all weights are equal is a FQ scheduler. Like all fair-queuing schedulers, each flow is protected from the others, and it can be proved that if a data flow is leaky bucket constrained, an end-to-end delay bound can be guaranteed. [2] The algorithm of WFQ is very similar to the one of FQ. For each packet, a virtual ...
Maximum throughput scheduling is a procedure for scheduling data packets in a packet-switched best-effort network, typically a wireless network, in view to maximize the total throughput of the network, or the system spectral efficiency in a wireless network. This is achieved by giving scheduling priority to the least "expensive" data flows in ...
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).
An M/M/∞ queue is a stochastic process whose state space is the set {0,1,2,3,...} where the value corresponds to the number of customers currently being served. Since, the number of servers in parallel is infinite, there is no queue and the number of customers in the systems coincides with the number of customers being served at any moment.
The objective of the stochastic scheduling problems can be regular objectives such as minimizing the total flowtime, the makespan, or the total tardiness cost of missing the due dates; or can be irregular objectives such as minimizing both earliness and tardiness costs of completing the jobs, or the total cost of scheduling tasks under likely arrival of a disastrous event such as a severe typhoon.