[Nexus S General] stempox:
The Scheduler is an algorithm that, given a set of requests for access to a resource, establishing a temporal order for the execution of such requests, favoring those that meet certain criteria in order to optimize access to that resource.

The difference between the various scheduler is the focus on certain criteria rather than on others.

The choice of a given scheduler does not produce visible changes so as to the choice of the governor, but still provides some improvements.

As usual schedulers are personally tested to find one that best suits your needs.

DEADLINE

It aims to provide a deadline, a deadline for all requests in order to avoid undesirable phenomena such as the "starvation" or the eternal waiting for some requests that occurs when one or more background processes are left indefinitely in the queue the ready, because there is always at least one of the highest priority ready process.

VR

The next request is performed according to the distance from the last request. In the network running good opinions about this scheduler.

NOOP

Push all requests in a single queue simply by their arrival order, grouping together those contiguous.

SIO

E 'the scheduler simpler, does not make any type of sort, is intended only for the purpose of obtaining a low-latency, ie to reduce the amount of time that elapses between the instant at which the request is generated and that in which the request is satisfied.

CFQ

Order requests of different processes in queues for each queue type and assigns a specific interval of time whose duration depends on the priorities assigned to processes. Can be considered the Ondemand the scheduler, the scheduler is in fact more balanced, doing its job in an honest manner.

BFQ

It 's based on CFQ but, instead of the intervals of time, assigns a part of the bandwidth of the disc to each process running in a proportional manner.

Anticipatory

Order requests based on criteria predictive, that puts the demands paused for a short period of time in anticipation that more of this to come to aggregate them.

ADAPTIVE ANTICIPATORY SCHEDULER

For the anticipatory scheduler, we scale up the anticipation timeout (antic expire) using the latency scaling factor over time. When the virtual disk latencies are low a small scaling of the timeout is sucient to prevent deceptive idleness, whereas when the latencies are high a larger scaling of the timeout value may be required to achieve the same. Note that such dynamic setting of the timeout value ensures that we attain a good trade-o between throughput (lost due to idling) and deceptive idleness mitigation. Setting a high value for the scaling factor (increasing idling time) only happens when the disk service latencies themselves are higher. This may not necessarily cause a signicant loss in throughput, because submitting a request from another process instead of idling is not going to improve throughput if the virtual disk itself does not get any faster than it is at the current period. A higher anticipation timeout might also be capable of absorbing process scheduling eects inside the VM. The results for the adaptive anticipatory scheduler are shown in Figure 2. The read time with our modied implementation (third bar in the dierent scheduler combinations) shows that it is possible to mitigate the eects of deceptive idleness by adapting the timeout. An interesting related observation is that the level to which the improve- ment is possible varies for dierent Domain-0 schedulers; noop - 39%, anticipatory - 67% and cfq - 36%. This again points to the fact that the I/O scheduler used in Domain-0 is important for the VM's ability in enforcing I/O scheduling guarantees. Dierent Domain-0 I/O schedulers likely have a dierent service latency footprint inside the VMs, contributing to dierent levels of improvement.

FIOS

Flash-based solid-state drives (SSDs) have the potential to eliminate the I/O bottlenecks in data-intensive applications However the large performance discrepancy between Flash reads and writes introduces challenges for fair resource usage. Further, existing fair queuing and quanta-based I/O schedulers poorly manage the I/O anticipation for Flash I/O fairness and efficiency. Some also suppress the I/O parallelism which causes substantial performance degradation on Flash. This paper develops FIOS, a new Flash I/O scheduler that attains fairness and high efficiency at the same time. FIOS employs a fair I/O time-slice management with mechanisms for read preference, parallelism, and fairness-oriented I/O anticipation. Evaluation demonstrates that FIOS achieves substantially better fairness and efficiency compared to the Linux CFQ scheduler, the SFQ(D) fair queuing scheduler, and the Argon quanta-based scheduler on several Flash-based storage devices (including a CompactFlash card in a low-power wimpy node). In particular, FIOS reduces the worst-case slowdown bya factor of 2.3 or more when the read-only SPECweb workload runs together with the write-intensive TPC

ROW

Row stands for READ Over WRITE which is the main requests dispatch policy of this algorithm. The ROW IO scheduler was developed with the mobile devices needs in mind. In mobile devices we favor user experience upon everything else, thus we want to give READ IO requests as much priority as possible. In mobile devices we won't have as much parallel threads as on desktops. Usually it's a single thread or at most 2 simultaneous working threads for read & write. Favoring READ requests over WRITEs decreases the READ latency greatly.

The main idea of the ROW scheduling policy is: If there are READ requests in pipe - dispatch them but don't starve the WRITE requests too much. Bellow you'll find a small comparison of ROW to existing schedulers. The test that was run for these measurements is parallel read and write.