Energy saving policies final

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  • Comentar que es una tesis presentada como compendio de artículos y con mención internacional.ComentariosJOrtegaManos cruzadas.Novedoso.Excesivotiempodefiniendoconceptos. Muypedagogico.Da la impresion de que solo hemoshecho un sw.Captura de aplicacionfuncionando video.Mas graficas o dibujos y menostexto.Flechitas con colorrojo y verde.Decirqelaspoliticasahorro grid en Cica.Trabajosfuturos en ambaslineas.Decirregistro de sw.percepcionluzes personal.No explicar formulas ahorroluz.Extrapolardatosahorrodespacho a la escuela.Mas cv sobre estancias.Me alegraque me hayahechoesapregunta...Inversion retorno. Utilizarluminariassensorizadas.
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  • Require previous setup.
  • Para cumplir con los requisitos necesarios para la obtención de la mención internacional del doctorado voy a realizar la última parte de la presentación en el idioma inglés.Sectionfoursummarizesthisthesis, reviewingconclusions, futurework and thecurriculum vitae of thecandidate.
  • Here I concludethispresentation and I’m at yourdisposalforanswering as manyquestions as youmayhave.
  • Energy saving policies final

    1. 1. Alejandro Fernández-Montes GonzálezAdvisors: Juan Antonio OrtegaLuis González AbrilEnergy-Saving Policies inGrid-Computing andSmart Environments
    2. 2. OutlineIntroduction Grid-ComputingSmartEnvironmentsFinalRemarks
    3. 3. IntroductionResearchMotivation, Success Criteria, Energy Efficiency, Energy vs Power
    4. 4. IntroductionMotivation• From 1990 to 2009:38% on CO2 emissions.28% on population.• Residential:17% energy consumption.• IT:3%-5% energy consumption.4
    5. 5. IntroductionResearch Question & Success criteria5Which are the best energy policies to save energy inGrid-Computing and Smart Environments?• Check if both model and supporting algorithmsdefine energy-saving policies indeed.• Demonstrate it by experiments through simulationsoftware.
    6. 6. IntroductionEnergy efficiency6• 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 isimproving.o IT Infrastructures: inputs are energy in kWh andoutputs are some degree of operation of the IThardware.o Smart Environments: inputs are energy in kWhand output as the quantity of light perceived byhumans.
    7. 7. IntroductionEnvironments analyzed7• Energy efficiency has been tackled from twosides:o Grid-Computing. Collaboration with to saveenergy in Grid’5000 infrastructure.o Smart Environments. Study about lightingconditions supported with sensors in order to saveenergy.
    8. 8. IntroductionThesis Outline• Grid-Computingo 2011o 2012• Smart Environmentso 2009• Defended as a set of papers.8
    9. 9. Grid-ComputingEnergy-Saving policies,Efficiency ComparisonGrid’5000, Simulation Software, On-off policies, Data Envelopment Analysis
    10. 10. Grid-ComputingData Center10• IT energy consumption 3%-5% of CO2 emissions.• Manufacturers double electrical efficiency every 1,5years.15216418520121824025172.61161261341441561691811942072180501001502002500%10%20%30%40%50%60%70%80%90%100%Power (Watts)PerformanceComparisonofPowerConsumptionw2kw2k
    11. 11. Grid-ComputingData Center11• Data centers energy consumption growth 16%avg. last decade.19.750.581.567.235.476.2130.2920.53%0.97%1.50%1.12%0501001502002502000 2005 Upper bound 2010 Lower bound 2010BkWh%world totalInfrastructureCommunicationsStorageHigh-end serversMid-range serversVolume Servers
    12. 12. Grid-ComputingPower Management Layers12ComponentPhysicalOperating SystemRackData center• ACPI (low-level).• ACPI (high-level).• Core parking.• Aggregation tools.• Energy Policies.
    13. 13. Grid-ComputingGrid’500013• deployedover 9 Francelocations.• Designed to supportcomputational greedytasks.• 8560 CPU-cores(a.k.a. resources).
    14. 14. Grid-ComputingResources14• Each core of each CPU is considered as onecomputational resource.• Resource states and fixed power required are:IDLE[50W]OFF[5W]BOOTING[110W]SHUTTING[110W]ON[108W]T booting00T shutting0
    15. 15. Grid-ComputingJobs15• Jobs are users’ tasks, deployed over a set ofresources.• Two kinds of jobs:o Submissions.o Reservations.• Three temporal points involved:o Submission time.o Start time.o Stop time.
    16. 16. Grid-ComputingGraphical representation16resourcestimer0t0r6r5r4r3r2r1t8t7t6t5t4t3t2t1 t10t9Job_idStarttimeStoptimeSubmissiontime
    17. 17. Grid-ComputingScheduling Energy Policies17• Establish the managing of the states of gridresources.• What to do with each resource that finishesthe execution of a job:o Leave On (idle).o Shut resource down.• Seven energy policies proposals are analyzedand compared.OffIdle
    18. 18. Grid-Computing1. Always On18• Current Grid’5000 behaviour.• Useful to compute current energyconsumption and to be compared with.
    19. 19. Grid-Computing2. Always Switch Off19• Always switches resources off.• Simplest policy.
    20. 20. Grid-Computing3. Load20• ‘Load’ is defined as thepercentage ofresources executing ajob.• Depending on currentGrid’5000 load, leavethem on, or switchthem off.• The thresholdpercentage isparameterized.
    21. 21. Grid-Computing4. Switch Off TS21• TS is defined as the minimum time thatensures energy saving if a resource is switchedoff between two jobs.Ts =Es - Poff *dtot + EOn®Off + EOff ®OnPIdle - Poff[A.C. Orgerie, et. al, 2009]
    22. 22. Grid-Computing4. Switch Off TS22• Looks in the agenda for jobs that are going tobe run in a period less than TS.• Computes number of resources that are goingto be needed and acts on resources.• Only this energy policy looks up the agendafor reservations already made.
    23. 23. Grid-Computing5. Random23• Leaves resources on or switch them offrandomly.• If other policy is worse, suspect you are doingsomething wrong.
    24. 24. Grid-Computing6. Exponential24• The exponential model describes timebetween consecutive events.• Every time a job finishes, the parameter (μ) ofthe model is computed from the meanduration between jobs .• Hence, probability of arrival of new job in atime less than Ts is given by1-e-Tsm
    25. 25. Grid-Computing7. Gamma25• The gamma model describes time betweenevents• The mean duration between jobs (Θ), and theratio of available resources and meanresources (κ) are computed.• Hence, probability of arrival of new job in atime less than Ts is given byg(k -1,q·Ts )G(k -1)
    26. 26. Grid-ComputingArranging policies26• Decides what to dowhen a new job arrives.• Two simple policies:o Do nothing: executes thejob in the resourcesoriginally assigned.o Simple aggregation (SA):looks for idle resourcesand move jobs to theseresources.
    27. 27. Grid-ComputingExperimentation27• Tested all combinations of Energy andArranging 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.
    28. 28. Grid-ComputingExperimentation28• Two periods of six months.• Seven energy policies.Configurable energy policies have been used withvarious values.• Two arranging policies.• Add up to a total of 324 simulations.
    29. 29. Grid-ComputingGrid’5000 Toolbox. Simulation software29
    30. 30. Grid-ComputingGrid’5000 Toolbox. Simulation software30
    31. 31. ‹#›
    32. 32. ‹#›
    33. 33. Grid-ComputingResults• Best energy saving policy could save up to:o 162,000€ per year for the whole Grid’5000infrastructure.o 318 tons of CO2.o 1,163,286 kWh.Madrid Barcelona78 AveMadrid-Barcelona61,314 Eurozone citizens34
    34. 34. Grid-Computing35JCR 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)
    35. 35. Grid-ComputingEfficiency Analysis.Data Envelopment Analysis (DEA)36• Non-parametric method to provide a relativeefficiency assessment for a group of decision-making units (DMU) with multiple inputs andoutputs.• Useful to answer questions like:o Which are the most efficient franchises of a company?o What parameters should be changed in a franchise tobe more efficient?• Establishes the efficient frontier to check if aDMU is efficient or not, and provides the actionsthat should be applied.
    36. 36. Grid-ComputingEfficiency Analysis. DEA37CRSVRS• Models:o CRS.o VRS.• Orientations:o Input.o Output.
    37. 37. Grid-ComputingEfficiency Analysis. DEA38• 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.
    38. 38. Grid-ComputingEfficiency Analysis. DEA39• 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 offTs, Gamma, Exponential.
    39. 39. Grid-ComputingDEA Results40Bordeaux Lille Lyon Nancy Orsay Rennes Sophia Toulouse St. deviation MeanAlways OffCRSTE 1.000 1.000 1.000 0.516 0.583 0.581 0.303 0.908 0.255 0.736VRSTE 1.000 1.000 1.000 0.667 0.650 0.670 0.567 0.938 0.177 0.812SCALE 1.000 1.000 1.000 0.773 0.897 0.868 0.535 0.968 0.151 0.880RandomCRSTE 1.000 1.000 1.000 0.427 0.521 0.581 0.284 0.889 0.273 0.713VRSTE 1.000 1.000 1.000 0.600 0.608 0.671 0.567 0.906 0.187 0.794SCALE 1.000 1.000 1.000 0.712 0.858 0.866 0.500 0.981 0.168 0.865LoadCRSTE 1.000 1.000 1.000 0.464 0.561 0.581 0.297 0.904 0.264 0.726VRSTE 1.000 1.000 1.000 0.675 0.634 0.670 0.601 0.937 0.172 0.815SCALE 1.000 1.000 1.000 0.687 0.885 0.868 0.495 0.965 0.171 0.862TsCRSTE 1.000 1.000 1.000 0.502 0.581 0.582 0.294 0.936 0.261 0.737VRSTE 1.000 1.000 1.000 0.657 0.648 0.670 0.567 0.937 0.178 0.810SCALE 1.000 1.000 1.000 0.763 0.896 0.868 0.519 0.999 0.159 0.881ExponentialCRSTE 1.000 1.000 0,944 0.511 0.572 0.581 0.307 1.000 0.259 0.739VRSTE 1.000 1.000 1.000 0.663 0.640 0.670 0.567 1.000 0.185 0.817SCALE 1.000 1.000 0,944 0.771 0.893 0.868 0.541 1.000 0.148 0.877GammaCRSTE 1.000 1.000 1.000 0.406 0.465 0.653 0.257 1.000 0.295 0.723VRSTE 1.000 1.000 1.000 0.667 0.573 0.726 0.567 1.000 0.189 0.817SCALE 1.000 1.000 1.000 0.608 0.812 0.899 0.453 1.000 0.197 0.847St. deviation 0.000 0.000 0.000 0.025 0.027 0.021 0.013 0.035Mean 1.000 1.000 1.000 0.655 0.626 0.680 0.573 0.953 0.811
    40. 40. Grid-ComputingDEA Results41Bordeaux100%Lille100%Lyon100%Nancy65% Orsay63%Rennes68%Sophia57%Toulouse95%40%50%60%70%80%90%100%110%ScaleefficiencyVR Scale Efficiency Comparison byLocationsAlways Off81.20%Random79.40%Load81.50%Ts81.00%Exponential81.70%Gamma81.70%78.00%78.50%79.00%79.50%80.00%80.50%81.00%81.50%82.00%ScaleEfficiencyVR Scale Efficiency Comparison byEnergy policy
    41. 41. Grid-ComputingCorrections needed42VariableOriginalValueRadialMovementSlackmovementProjectedvalueOutput Saved energy 152,141 0 0 152,141Output #jobs 57,987 0 38,556 96,543Input #resources 714 -235 0 478Input #bootings 1,770,858 -584,429 -832,549 353,879Rennes under the energy policy Exponential.VariableOriginalValueRadialMovementSlackmovementProjectedvalueOutput Saved energy 85,250 0 0 152,141Output #jobs 165,995 0 0 165,995Input #resources 434 -27 0 406Input #bootings 876,026 -55,393 0 820,632Toulouse under the energy policy TS.
    42. 42. Grid-ComputingCorrections needed IILocations Policy PeersCorrectionsJobs Resources BootingsBordeaux Summary Bordeaux ↔ ↔ ↔Lille Summary Lille ↔ ↔ ↔Lyon Summary Lyon ↔ ↔ ↔Toulouse Summary B, Li, Ly and T ↑ ↓ ↓Locations Policy PeersCorrectionsJobs Resources BootingsBordeauxAlwz. 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 ↔ ↔ ↔LilleAlwz. 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 ↔ ↔ ↔LyonAlwz. 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 ↔ ↔ ↔ToulouseAlwz. 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
    43. 43. Grid-ComputingCorrections needed III44Locations Policy PeersCorrectionsJobs Resources BootingsNancyAlwz. 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 ↑ ↓ ↓OrsayAlwz. 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 ↑ ↓ ↓RennesAlwz. 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 ↑ ↓ ↓SophiaAlwz. 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 PeersCorrectionsJobs Resources BootingsNancy Summary Lille and Lyon ↑ ↓ ↓Orsay Summary Lille and Lyon ↑ ↓ ↓Rennes Summary Lille and Lyon ↑ ↓ ↓Sophia Summary Lille and Lyon ↑ ↓ ↓Locations Policy PeersCorrectionsJobs Resources BootingsBordeaux 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 ↑ ↓ ↓
    44. 44. Grid-ComputingConclusions45• DEA enables Grid managers to compare:o grid locations.o energy policies.• Thanks to DEA methodology, system managerscan detect which locations are underused andhence to carry out decisions.
    45. 45. Grid-Computing46JCR 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)
    46. 46. Lighting, Wireless Sensor Networks, User preferencesSmart environmentsLighting adjustment,User preferences
    47. 47. SmartEnvironmentsIntroduction• Residentialconsumption 20% ofSpanish total energyusage.20%25%55%Spanish Energy UsageResidentialTransportOther48• Saving energy in smart environments is animportant researching area.
    48. 48. SmartEnvironmentsIntroduction• Previous approaches adjust lighting to aconstant value and do not maintainknowledge of inhabitants’ preferences.• Use of WirelessSensor Networks toretrieve informationabout lightingconditions.49
    49. 49. SmartEnvironmentsMotivation50• Spanish technical building code establishes400 lumens as the optimal quantity of light fora standard office.• Measures may vary depending on:o sensors location.o type of lighting appliances.o windows orientation, size, etc.
    50. 50. SmartEnvironmentsTheoretical Analysis• y=f(x)relates illumination between lightson and off.• µ+α -> inhabitant threshold.• ymin ->min luxes lights on.• xmax -> max luxes.• I -> zone of regulation.• II -> zone of swichting off.51LuxesLuxes
    51. 51. SmartEnvironmentsExperimental Environment• Lighting:o Four groups offluorescent lights.o Window faces South.• Sensors:o Indoor and outdoormote.o Occupancy.52
    52. 52. SmartEnvironmentsExperimental EnvironmentCapabilities:• Sensors:o Photodiodes:- PAR.- TSR.o Humidity.o Temperature.• Zigbee.• Programmable:Java IDE.53
    53. 53. SmartEnvironmentsExperimental Environment• Mote Dashboard software developed.• Data retrieved and stored for several monthsincluding:o Indoor lighting (PAR).o Indoor light state.o Motion.o ...54
    54. 54. SmartEnvironmentsUser Preference Threshold• Questionnaire every two hours for tenworking 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 JAAYes 90 95 89 101No 83 88 75 92020406080100120sexuLLuminance threshold analysis55
    55. 55. SmartEnvironmentsEmpirical Analysis• Comparing lighting conditions when ligths areoff and on.• 50+ tests pairinglightingconditions, variousmoments of dayand night.56
    56. 56. SmartEnvironmentsEmpirical Analysis• Linear modelo y = f(x) = 1.1039 x + 97.15o ymin= 97.15 lux• Linear regression:o x = f*(y) =0.873 y – 77.15o Given a PARONvalue we canestimate PAROFF57
    57. 57. SmartEnvironmentsSaving Energy• μ+α = μPAR guarantees the user preferencesare satisfied.TotalPARPAR>µPAR PAR<µPAR#instances % #instances % #instances %lights On 13810 18.84 13740 52.58 70 0.15lights Off 59490 81.16 12390 47.42 47100 99.85TOTAL 73300 100 26130 100 47170 10058
    58. 58. SmartEnvironmentsSaving Energy59)]max([ ,ONlightsedOffEstimatPARON PARPAR• Computation of luxes wasted:I -> zone of regulation.II -> zone of switching off.
    59. 59. SmartEnvironmentsSaving Energy60• Computation of energy consumed by lights:totalKWh = totalTimeOn * # tubes * (wattsPerTube / 1000)totalKgOfCO2 = totalKWh * 0.274Kg / KWhtotal€ = totalKWh * 0.14€ / KWh• Computation of kgs of CO2:• Computation of euros:
    60. 60. SmartEnvironmentsSaving EnergyInterval Per yearDays computed 101.8 365Days with lights on 19.2 68.77Total luxes generated 167,010,000 598,775,280Total luxes wasted 119,715,110 429,210,527KWh CO2 (kg) € KWh CO2 (kg) €Total Consumption 132.6 36.3 17 475.3 130.2 64.2Total Waste 95.0 26.0 12 340.7 93.36 46.0• Total savings of about 74%, 46€ and 93.36kgsof CO2 per year for a single standard office.61
    61. 61. SmartEnvironments62JCR 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)
    62. 62. Final RemarksConclusions & Future Work,CurriculumData center, Stress measurement, Curriculum
    63. 63. FinalRemarksConclusions64• Various energy saving policies have beendesigned and tested– These energy policies can save up to 40% ofenergy on Grid-Computing infrastructures.• DEA is a useful tool for comparing efficiency oflocations and suggesting improvements.– measures relative efficiency betweenlocations, useful to take corrective decisions.
    64. 64. FinalRemarksConclusions65• Energy and economic costs savings can becarried out by means of cheap devices such assensors and control appliances• Energy policies applied to lighting conditionsand based on user preferences can save up to74% of energy.
    65. 65. FinalRemarksFuture work66• Grid-Computing:o Adapting energy policies to take into account thevariety of energy consumptions of resources.o Adapting models to data centers issues.o Development of new policies and combination ofpolicies.• Smart Environments:o Collaboration with U. Reutlingen in order toextend Smart Environments model wherebiometric information is considered.
    66. 66. FinalRemarksPublications67
    67. 67. FinalRemarksStages68o 2007oUC3MoENTI Group –Natividad Martínezo 2008-2009oENS - Lyon.oRESO Group –Laurent Lefevreo 2012oUPC - CETpDoCecilo Angulo
    68. 68. Alejandro Fernández-Montes GonzálezAdvisors: Juan Antonio OrtegaLuis González AbrilEnergy-Saving Policies inGrid-Computing andSmart Environments

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