Smart scheduling for saving energy in grid computing final

Alejandro Fernández-Montes González
University of Sevilla. Spain
afdez@us.es
Energy-Saving Policies in
Grid-Computing

Grid-Computing
Energy-Saving policies,
Efficiency Comparison
Grid’5000, Simulation Software, On-off policies, Data Envelopment Analysis

Grid-
Computing
Smart scheduling for saving energy in grid computing
Fernández-Montes et. Al.
Expert Systems with applications.
http://dx.doi.org/10.1016/j.eswa.2012.02.115
152
164
185
201
218
240
251
72.6
116
126
134
144
156
169
181
194
207
218
0
50
100
150
200
250
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Power(Watts)
Performance
Comparison of Power Consumption
w2
w2
Data Center
3
• IT energy consumption 3%-5% of CO2 emissions.
• Manufacturers double electrical efficiency every 1,5
years.

Grid-
Computing
http://dx.doi.org/10.1016/j.eswa.2012.02.115Data Center
4
• Data centers energy consumption growth 16%
avg. last decade.
19.7
50.5
81.5
67.2
35.4
76.2
130.2
92
0.53%
0.97%
1.50%
1.12%
0
50
100
150
200
250
2000 2005 Upper bound 2010 Lower bound 2010
BkWh
%world total
Infrastructure
Communications
Storage
High-end servers
Mid-range servers
Volume Servers

Grid-
Computing
http://dx.doi.org/10.1016/j.eswa.2012.02.115Power Management Layers
5
Component
Physical
Operating System
Rack
Data center
• ACPI (low-level).
• ACPI (high-level).
• Core parking.
• Aggregation tools.
• Energy Policies.

Grid-
Computing
http://dx.doi.org/10.1016/j.eswa.2012.02.115Grid’5000
6
• deployed
over 9 France
locations.
• Designed to support
computational greedy
tasks.
• 8560 CPU-cores
(a.k.a. resources).

Grid-
Computing
http://dx.doi.org/10.1016/j.eswa.2012.02.115Resources
7
• Each core of each CPU is considered as one
computational resource.
• Resource states and fixed power required are:
IDLE
[50W]
OFF
[5W]
BOOTING
[110W]
SHUTTING
[110W]
ON
[108W]
T booting
0
0
T shutting0

Grid-
Computing
http://dx.doi.org/10.1016/j.eswa.2012.02.115Jobs
8
• Jobs are users’ tasks, deployed over a set of
resources.
• Two kinds of jobs:
o Submissions.
o Reservations.
• Three temporal points involved:
o Submission time.
o Start time.
o Stop time.

Grid-
Computing
http://dx.doi.org/10.1016/j.eswa.2012.02.115Graphical representation
9
resources
time
r0
t0
r6
r5
r4
r3
r2
r1
t8t7t6t5t4t3t2t1 t10t9
Job_id
Start
time
Stop
time
Submission
time

Grid-
Computing
http://dx.doi.org/10.1016/j.eswa.2012.02.115Scheduling Energy Policies
10
• Establish the managing of the states of grid
resources.
• What to do with each resource that finishes
the execution of a job:
o Leave On (idle).
o Shut resource down.
• Seven energy policies proposals are analyzed
and compared.
Off
Idle

Grid-
Computing
http://dx.doi.org/10.1016/j.eswa.2012.02.1151. Always On
11
• Current Grid’5000 behaviour.
• Useful to compute current energy
consumption and to be compared with.

Grid-
Computing
http://dx.doi.org/10.1016/j.eswa.2012.02.1152. Always Switch Off
12
• Always switches resources off.
• Simplest policy.

Grid-
Computing
http://dx.doi.org/10.1016/j.eswa.2012.02.1153. Load
13
• ‘Load’ is defined as the
percentage of
resources executing a
job.
• Depending on current
Grid’5000 load, leave
them on, or switch
them off.
• The threshold
percentage is
parameterized.

Grid-
Computing
http://dx.doi.org/10.1016/j.eswa.2012.02.1154. Switch Off TS
14
• TS is defined as the minimum time that
ensures energy saving if a resource is switched
off between two jobs.
Ts =
Es - Poff *dtot + EOn®Off + EOff ®On
PIdle - Poff
[A.C. Orgerie, et. al, 2009]

Grid-
Computing
http://dx.doi.org/10.1016/j.eswa.2012.02.1154. Switch Off TS
15
• Looks in the agenda for jobs that are going to
be run in a period less than TS.
• Computes number of resources that are going
to be needed and acts on resources.
• Only this energy policy looks up the agenda
for reservations already made.

Grid-
Computing
http://dx.doi.org/10.1016/j.eswa.2012.02.1155. Random
16
• Leaves resources on or switch them off
randomly.
• If other policy is worse, suspect you are doing
something wrong.

Grid-
Computing
http://dx.doi.org/10.1016/j.eswa.2012.02.1156. Exponential
17
• The exponential model describes time
between consecutive events.
• Every time a job finishes, the parameter (μ) of
the model is computed from the mean
duration between jobs .
• Hence, probability of arrival of new job in a
time less than Ts is given by
1-e
-Ts
m

Grid-
Computing
http://dx.doi.org/10.1016/j.eswa.2012.02.1157. Gamma
18
• The gamma model describes time between
events
• The mean duration between jobs (Θ), and the
ratio of available resources and mean
resources (κ) are computed.
• Hence, probability of arrival of new job in a
time less than Ts is given by
g(k -1,q·Ts )
G(k -1)

Grid-
Computing
http://dx.doi.org/10.1016/j.eswa.2012.02.115Arranging policies
19
• Decides what to do
when a new job arrives.
• Two simple policies:
o Do nothing: executes the
job in the resources
originally assigned.
o Simple aggregation (SA):
looks for idle resources
and move jobs to these
resources.

Grid-
Computing
http://dx.doi.org/10.1016/j.eswa.2012.02.115Experimentation
20
• Tested all combinations of Energy and
Arranging policies.
• Computed results:
o Energy consumed.
o Energy saved.
o Number of bootings and shuttings.
o Comparison between minimal and actual.
o Saved energy by booting-shutting.

Grid-
Computing
http://dx.doi.org/10.1016/j.eswa.2012.02.115Experimentation
21
• Two periods of six months.
• Seven energy policies.
Configurable energy policies have been used with
various values.
• Two arranging policies.
• Add up to a total of 324 simulations.

Grid-
Computing
http://dx.doi.org/10.1016/j.eswa.2012.02.115Grid’5000 Toolbox. Simulation software
22

Grid-
Computing
http://dx.doi.org/10.1016/j.eswa.2012.02.115Grid’5000 Toolbox. Simulation software
23

Grid-
Computing
http://dx.doi.org/10.1016/j.eswa.2012.02.115Results
• Best energy saving policy could save up to:
o 162,000€ per year for the whole Grid’5000
infrastructure.
o 318 tons of CO2.
o 1,163,286 kWh.
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Smart scheduling for saving energy in grid computing final

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