Alejandro Fernández-Montes González
Advisors: Juan Antonio Ortega
Luis González Abril
Energy-Saving Policies in
Grid-Computing and
Smart Environments
IntroductionMotivation
• From 1990 to 2009:
38% on CO2 emissions.
28% on population.
• Residential:
17% energy consumption.
• IT:
3%-5% energy consumption.
4
IntroductionResearch Question & Success criteria
5
Which are the best energy policies to save energy in Grid-Computing and Smart Environments?
• Check if both model and supporting algorithms define energy-saving policies indeed.
• Demonstrate it by experiments through simulation software.
IntroductionEnergy efficiency
6
• Energy is the capacity to do work (W)
o S.I.: Joule., but usually reported as kWh.
o Combination of power and time.
• Efficiency is the ratio outputs/inputs :
o If outputs increase faster than inputs, efficiency is improving.
o IT Infrastructures: inputs are energy in kWh and outputs are some degree of operation of the IT hardware.
o Smart Environments: inputs are energy in kWh and output as the quantity of light perceived by humans.
IntroductionEnvironments analyzed
7
• Energy efficiency has been tackled from two sides:
o Grid-Computing. Collaboration with to save energy in Grid’5000 infrastructure.
o Smart Environments. Study about lighting conditions supported with sensors in order to save energy.
IntroductionThesis Outline
• Grid-Computing
o 2011
o 2012
• Smart Environments
o 2009
• Defended as a set of papers.
8
Grid-Computing Energy-Saving policies,Efficiency Comparison
Grid’5000, Simulation Software, On-off policies, Data Envelopment Analysis
Grid-ComputingData Center
10
• IT energy consumption 3%-5% of CO2 emissions.
• Manufacturers double electrical efficiency every 1,5 years.
152164
185201
218
240251
72.6
116126
134144
156169
181194
207218
0 50 100 150 200 250
0%10%
20%30%
40%50%
60%70%
80%90%
100%
Power (Watts)
Performance
Comparison of Power Consumption
w2k3
w2k8
Grid-ComputingData Center
11
• Data centers energy consumption growth 16% avg. last decade.
19.7
50.5
81.567.2
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-ComputingPower Management Layers
12
Component
Physical
Operating System
Rack
Data center
• ACPI (low-level).
• ACPI (high-level).
• Core parking.
• Aggregation tools.
• Energy Policies.
Grid-ComputingGrid’5000
13
• deployed over 9 France locations.
• Designed to support computational greedy tasks.
• 8560 CPU-cores (a.k.a. resources).
Grid-ComputingResources
14
• 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-ComputingJobs
15
• 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-ComputingGraphical representation
16
resources
time
r0
t0
r6
r5
r4
r3
r2
r1
t8t7t6t5t4t3t2t1 t10t9
Job_id
Start
time
Stop
time
Submission
time
Grid-ComputingScheduling Energy Policies
17
• 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 analyzedand compared.
Off
Idle
Grid-Computing1. Always On
18
• Current Grid’5000 behaviour.
• Useful to compute current energy consumption and to be compared with.
Grid-Computing3. Load
20
• ‘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-Computing4. Switch Off TS
21
• 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-Computing4. Switch Off TS
22
• 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-Computing5. Random
23
• Leaves resources on or switch them off randomly.
• If other policy is worse, suspect you are doing something wrong.
Grid-Computing6. Exponential
24
• 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-Computing7. Gamma
25
• 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-ComputingArranging policies
26
• 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-ComputingExperimentation
27
• 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-ComputingExperimentation
28
• 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-ComputingResults
• 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.
Madrid Barcelona
78 Ave Madrid-Barcelona
61,314 Eurozone citizens
34
Grid-Computing
35
JCR 2.203, JCR-5 2.455Q1 in three categories:
Engineering, Electrical & Electronic (41/244)Operations Research & Management Science (5/77)Computer Science, Artificial Intelligence (22/111)
Grid-Computing
Efficiency Analysis. Data Envelopment Analysis (DEA)
36
• Non-parametric method to provide a relative efficiency assessment for a group of decision-making units (DMU) with multiple inputs and outputs.
• Useful to answer questions like:o Which are the most efficient franchises of a company?o What parameters should be changed in a franchise to
be more efficient?
• Establishes the efficient frontier to check if a DMU is efficient or not, and provides the actions that should be applied.
Grid-ComputingEfficiency Analysis. DEA
37
CRSVRS
• Models:
o CRS.
o VRS.
• Orientations:
o Input.
o Output.
Grid-ComputingEfficiency Analysis. DEA
38
• Selection of inputs and outputs.o Inputs:
- Number resources of locations.- #bootings + shuttings.
o Outputs:- Energy saved using a given policy.- #jobs.
• Input-Output orientation.Since locations are able to modify its inputs.
• VRS hypothesis.More realistic model.
Grid-ComputingEfficiency Analysis. DEA
39
• DEA was applied to a couple of scenarios:
o Comparing locations = DMUs:
Bordeaux, Lille, Lyon, Nancy, Orsay, Rennes, Sophia, Toulouse.
o Comparing energy policies = DMUs:
Always switch off, Random, Load, Switch off Ts, Gamma, Exponential.
Grid-ComputingDEA Results
40
Bordeaux Lille Lyon Nancy Orsay Rennes Sophia Toulouse St. deviation Mean
Always Off
CRSTE 1.000 1.000 1.000 0.516 0.583 0.581 0.303 0.908 0.255 0.736
VRSTE 1.000 1.000 1.000 0.667 0.650 0.670 0.567 0.938 0.177 0.812
SCALE 1.000 1.000 1.000 0.773 0.897 0.868 0.535 0.968 0.151 0.880
Random
CRSTE 1.000 1.000 1.000 0.427 0.521 0.581 0.284 0.889 0.273 0.713
VRSTE 1.000 1.000 1.000 0.600 0.608 0.671 0.567 0.906 0.187 0.794
SCALE 1.000 1.000 1.000 0.712 0.858 0.866 0.500 0.981 0.168 0.865
Load
CRSTE 1.000 1.000 1.000 0.464 0.561 0.581 0.297 0.904 0.264 0.726
VRSTE 1.000 1.000 1.000 0.675 0.634 0.670 0.601 0.937 0.172 0.815
SCALE 1.000 1.000 1.000 0.687 0.885 0.868 0.495 0.965 0.171 0.862
Ts
CRSTE 1.000 1.000 1.000 0.502 0.581 0.582 0.294 0.936 0.261 0.737
VRSTE 1.000 1.000 1.000 0.657 0.648 0.670 0.567 0.937 0.178 0.810
SCALE 1.000 1.000 1.000 0.763 0.896 0.868 0.519 0.999 0.159 0.881
Exponential
CRSTE 1.000 1.000 0,944 0.511 0.572 0.581 0.307 1.000 0.259 0.739
VRSTE 1.000 1.000 1.000 0.663 0.640 0.670 0.567 1.000 0.185 0.817
SCALE 1.000 1.000 0,944 0.771 0.893 0.868 0.541 1.000 0.148 0.877
Gamma
CRSTE 1.000 1.000 1.000 0.406 0.465 0.653 0.257 1.000 0.295 0.723
VRSTE 1.000 1.000 1.000 0.667 0.573 0.726 0.567 1.000 0.189 0.817
SCALE 1.000 1.000 1.000 0.608 0.812 0.899 0.453 1.000 0.197 0.847
St. deviation 0.000 0.000 0.000 0.025 0.027 0.021 0.013 0.035
Mean 1.000 1.000 1.000 0.655 0.626 0.680 0.573 0.953 0.811
Grid-ComputingDEA Results
41
Bordeaux100%
Lille100%
Lyon100%
Nancy65% Orsay
63%
Rennes68%
Sophia57%
Toulouse95%
40%
50%
60%
70%
80%
90%
100%
110%
Scal
e e
ffic
ien
cy
VR Scale Efficiency Comparison by Locations
Always Off 81.20%
Random 79.40%
Load 81.50%
Ts 81.00%
Exponential 81.70%
Gamma 81.70%
78.00%
78.50%
79.00%
79.50%
80.00%
80.50%
81.00%
81.50%
82.00%
Scal
e E
ffic
ien
cy
VR Scale Efficiency Comparison by Energy policy
Grid-ComputingCorrections needed
42
VariableOriginal
ValueRadial
MovementSlack
movementProjected
value
Output Saved energy 152,141 0 0 152,141
Output #jobs 57,987 0 38,556 96,543
Input #resources 714 -235 0 478
Input #bootings 1,770,858 -584,429 -832,549 353,879
Rennes under the energy policy Exponential.
VariableOriginal
ValueRadial
MovementSlack
movementProjected
value
Output Saved energy 85,250 0 0 152,141
Output #jobs 165,995 0 0 165,995
Input #resources 434 -27 0 406
Input #bootings 876,026 -55,393 0 820,632
Toulouse under the energy policy TS.
Grid-ComputingCorrections needed II
Locations Policy PeersCorrections
Jobs Resources Bootings
Bordeaux Summary Bordeaux ↔ ↔ ↔
Lille Summary Lille ↔ ↔ ↔
Lyon Summary Lyon ↔ ↔ ↔
Toulouse Summary B, Li, Ly and T ↑ ↓ ↓
Locations Policy PeersCorrections
Jobs Resources Bootings
Bordeaux
Alwz. Off B (1.000) ↔ ↔ ↔
Random B (1.000) ↔ ↔ ↔
Load B (1.000) ↔ ↔ ↔
Ts B (1.000) ↔ ↔ ↔
Exp. B (1.000) ↔ ↔ ↔
Gamma B (1.000) ↔ ↔ ↔
Summary Bordeaux ↔ ↔ ↔
Lille
Alwz. Off Li (1.000) ↔ ↔ ↔
Random Li (1.000) ↔ ↔ ↔
Load Li (1.000) ↔ ↔ ↔
Ts Li (1.000) ↔ ↔ ↔
Exp. Li (1.000) ↔ ↔ ↔
Gamma Li (1.000) ↔ ↔ ↔
Summary Lille ↔ ↔ ↔
Lyon
Alwz. Off Ly (1.000) ↔ ↔ ↔
Random Ly (1.000) ↔ ↔ ↔
Load Ly (1.000) ↔ ↔ ↔
Ts Ly (1.000) ↔ ↔ ↔
Exp. Ly (1.000) ↔ ↔ ↔
Gamma Ly (1.000) ↔ ↔ ↔
Summary Lyon ↔ ↔ ↔
Toulouse
Alwz. Off B (0.179), Li (0.089), Ly (0.732) ↑ ↓ ↓
Random B (0.167), Li (0.055), Ly (0.777) ↑ ↓ ↓
Load B (0.179), Li (0.088), Ly (0.733) ↑ ↓ ↓
Ts B (0.178), Li (0.086), Ly (0.735) ↑ ↓ ↓
Exp. T (1.000) ↔ ↔ ↔
Gamma T (1.000) ↔ ↔ ↔
Summary B, Li, Ly and T ↑ ↓ ↓43
Grid-ComputingCorrections needed III
44
Locations Policy PeersCorrections
Jobs Resources Bootings
Nancy
Alwz. Off Li (0.206), Ly (0.794) ↑ ↓ ↓
Random Li (0.075), Ly (0.925) ↑ ↓ ↓
Load Li (0.221), Ly (0.779) ↑ ↓ ↓
Ts Li (0.186), Ly (0.814) ↑ ↓ ↓
Exp. Li (0.198), Ly (0.802) ↑ ↓ ↓
Gamma Li (0.207), Ly (0.793) ↑ ↓ ↓
Summary Lille and Lyon ↑ ↓ ↓
Orsay
Alwz. Off Li (0.415), Ly (0.585) ↑ ↓ ↓
Random Li (0.316), Ly (0.684) ↑ ↓ ↓
Load Li (0.377), Ly (0.623) ↑ ↓ ↓
Ts Li (0.410), Ly (0.590) ↑ ↓ ↓
Exp. Li (0.391), Ly (0.609) ↑ ↓ ↓
Gamma Li (0.235), Ly (0.765) ↑ ↓ ↓
Summary Lille and Lyon ↑ ↓ ↓
Rennes
Alwz. Off Li (0.527), Ly (0.473) ↑ ↓ ↓
Random Li (0.531), Ly (0.469) ↑ ↓ ↓
Load Li (0.527), Ly (0.473) ↑ ↓ ↓
Ts Li (0.529), Ly (0.471) ↑ ↓ ↓
Exp. Li (0.528), Ly (0.472) ↑ ↓ ↓
Gamma Li (0.663), Ly (0.337) ↑ ↓ ↓
Summary Lille and Lyon ↑ ↓ ↓
Sophia
Alwz. Off Ly (1.000) ↑ ↓ ↓
Random Ly (1.000) ↑ ↓ ↓
Load Li (0.065), Ly (0.935) ↑ ↓ ↓
Ts Ly (1.000) ↑ ↓ ↓
Exp. Ly (1.000) ↑ ↓ ↓
Gamma Ly (1.000) ↑ ↓ ↓
Summary Lille and Lyon ↑ ↓ ↓
Locations Policy PeersCorrections
Jobs Resources Bootings
Nancy Summary Lille and Lyon ↑ ↓ ↓
Orsay Summary Lille and Lyon ↑ ↓ ↓
Rennes Summary Lille and Lyon ↑ ↓ ↓
Sophia Summary Lille and Lyon ↑ ↓ ↓
Locations Policy PeersCorrections
Jobs Resources Bootings
Bordeaux Summary Bordeaux ↔ ↔ ↔
Lille Summary Lille ↔ ↔ ↔
Lyon Summary Lyon ↔ ↔ ↔
Toulouse Summary B, Li, Ly and T ↑ ↓ ↓
Nancy Summary Lille and Lyon ↑ ↓ ↓
Orsay Summary Lille and Lyon ↑ ↓ ↓
Rennes Summary Lille and Lyon ↑ ↓ ↓
Sophia Summary Lille and Lyon ↑ ↓ ↓
Grid-ComputingConclusions
45
• DEA enables Grid managers to compare:
o grid locations.
o energy policies.
• Thanks to DEA methodology, system managers can detect which locations are underused and hence to carry out decisions.
Grid-Computing
46
JCR 2.203, JCR-5 2.455Q1 in three categories:
Engineering, Electrical & Electronic (41/244)Operations Research & Management Science (5/77)Computer Science, Artificial Intelligence (22/111)
Lighting, Wireless Sensor Networks, User preferences
Smart environments Lighting adjustment,
User preferences
SmartEnvironmentsIntroduction
• Residential consumption 20% of Spanish total energy usage.
20%
25%55%
Spanish Energy Usage
Residential
Transport
Other
48
• Saving energy in smart environments is an important researching area.
SmartEnvironmentsIntroduction
• Previous approaches adjust lighting to a constant value and do not maintain knowledge of inhabitants’ preferences.
• Use of Wireless Sensor Networks to retrieve information about lighting conditions.
49
SmartEnvironmentsMotivation
50
• Spanish technical building code establishes 400 lumens as the optimal quantity of light for a standard office.
• Measures may vary depending on:
o sensors location.
o type of lighting appliances.
o windows orientation, size, etc.
SmartEnvironmentsTheoretical Analysis
• y=f(x)
relates illumination between lights on and off.
• µ+α -> inhabitant threshold.
• ymin ->min luxes lights on.
• xmax -> max luxes.
• I -> zone of regulation.
• II -> zone of swichting off.
51
Luxes
Luxes
SmartEnvironmentsExperimental Environment
• Lighting:
o Four groups of fluorescent lights.
o Window faces South.
• Sensors:
o Indoor and outdoor mote.
o Occupancy.
52
SmartEnvironmentsExperimental Environment
Capabilities:
• Sensors:o Photodiodes:
- PAR.
- TSR.
o Humidity.
o Temperature.
• Zigbee.
• Programmable:Java IDE.
53
SmartEnvironmentsExperimental Environment
• Mote Dashboard software developed.
• Data retrieved and stored for several months including:
o Indoor lighting (PAR).
o Indoor light state.
o Motion.
o ...
54
SmartEnvironmentsUser Preference Threshold
• Questionnaire every two hours for ten working days:
o Is this quantity of light enough for you?
- Yes (the lowest is considered to be μ+α) .
- No (the highest is considered to be μ-α).
AFM JAN IN JAA
Yes 90 95 89 101
No 83 88 75 92
0
20
40
60
80
100
120
s e x u L
Luminance threshold analysis
55
SmartEnvironmentsEmpirical Analysis
• Comparing lighting conditions when ligths are off and on.
• 50+ tests pairing lighting conditions, various moments of day and night.
56
SmartEnvironmentsEmpirical Analysis
• Linear modelo y = f(x) = 1.1039 x + 97.15
o ymin= 97.15 lux
• Linear regression:
o x = f*(y) = 0.873 y – 77.15
o Given a PARON
value we can estimate PAROFF
57
SmartEnvironmentsSaving Energy
• μ+α = μPAR guarantees the user preferences are satisfied.
TotalPAR
PAR>µPAR PAR<µPAR
#instances % #instances % #instances %
lights On 13810 18.84 13740 52.58 70 0.15
lights Off 59490 81.16 12390 47.42 47100 99.85
TOTAL 73300 100 26130 100 47170 100
58
SmartEnvironmentsSaving Energy
59
•
)(ONlights
edOffEstimatON PARPAR
)]max([ ,
ONlights
edOffEstimatPARON PARPAR
• Computation of luxes wasted:
I -> zone of regulation.II -> zone of switching off.
SmartEnvironmentsSaving Energy
60
• Computation of energy consumed by lights:
totalKWh = totalTimeOn * # tubes * (wattsPerTube / 1000)
totalKgOfCO2 = totalKWh * 0.274Kg / KWh
total€ = totalKWh * 0.14€ / KWh
• Computation of kgs of CO2:
• Computation of euros:
SmartEnvironmentsSaving Energy
Interval Per year
Days computed 101.8 365
Days with lights on 19.2 68.77
Total luxes generated 167,010,000 598,775,280
Total luxes wasted 119,715,110 429,210,527
KWh CO2 (kg) € KWh CO2 (kg) €
Total Consumption 132.6 36.3 17 475.3 130.2 64.2
Total Waste 95.0 26.0 12 340.7 93.36 46.0
• Total savings of about 74%, 46€ and 93.36kgs of CO2 per year for a single standard office.
61
SmartEnvironments
62
JCR 2.148, JCR-5 3.529Q1 in four categories:
Computer Science, Hardware & Architecture (9/49) Computer Science, Information Systems (25/116) Engineering, Electrical & Electronic (37/246) Telecommunications (8/77)
FinalRemarksConclusions
64
• Various energy saving policies have been designed and tested
– These energy policies can save up to 40% of energy on Grid-Computing infrastructures.
• DEA is a useful tool for comparing efficiency of locations and suggesting improvements.
– measures relative efficiency between locations, useful to take corrective decisions.
FinalRemarksConclusions
65
• Energy and economic costs savings can be carried out by means of cheap devices such as sensors and control appliances
• Energy policies applied to lighting conditions and based on user preferences can save up to 74% of energy.
FinalRemarksFuture work
66
• Grid-Computing:
o Adapting energy policies to take into account the variety of energy consumptions of resources.
o Adapting models to data centers issues.
o Development of new policies and combination of policies.
• Smart Environments:
o Collaboration with U. Reutlingen in order to extend Smart Environments model where biometric information is considered.
FinalRemarksStages
68
o 2007
oUC3M
o ENTI Group –Natividad Martínez
o 2008-2009
o ENS - Lyon.
oRESO Group –Laurent Lefevre
o 2012
oUPC - CETpD
oCecilo Angulo
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