1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
|
% Registration Enhancements to Linux I/O Scheduling
% [2]Register/Submit Proposal Seetharami R Seelam (seelam@cs.utep.edu)
The Linux 2.6 release provides four I/O
schedulers: deadline, anticipatory, noop, and
completely fair queuing (CFQ), along with an
option to select one of these four at boot
time. The selection is based on \textit{a priori}
knowledge of the workload, hardware
configuration of the system, and the file
system, among other factors. The anticipatory
scheduler (AS) is the default. Although the AS
performs well under many situations, we have
identified cases, under certain combinations
of workloads, where the AS leads to starvation
of processes. To mitigate this problem, we
implemented an extension to the AS (called
Cooperative AS or CAS) and compared its
performance with the other four schedulers.
This paper briefly describes the AS and the
related deadline scheduler, highlighting their
shortcomings; in addition, it gives a detailed
description of the CAS. We report performance
of all five schedulers on a set of workloads,
which represent a wide range of I/O behavior.
The study shows that (1) the CAS has an order
of magnitude improvement in performance in
cases where the AS leads to starvation and (2)
in several cases the CAS has performance
comparable to that of the other schedulers.
But, as the literature and this study reports,
no one scheduler can provide the best possible
performance for all workloads; accordingly,
Linux provides four from which to select. Even
when dealing with just four I/O schedulers, in
systems that service concurrent workloads with
different I/O behaviors, \textit{a priori} selection of
the scheduler with the best possible
performance can be an intricate task. Dynamic
selection based on workload needs, system
configuration, and other parameters can
address this challenge. Accordingly, we are
developing metrics and heuristics that can be
used for this purpose. The paper concludes
with a description of our efforts in this
direction, in particular, we present a
characterization function based on metrics
related to system behavior and I/O requests
that can be used to measure and compare
scheduling algorithm performance. This
characterization function can be used to
dynamically select an appropriate scheduler
based on observed system behavior.
|
