1. Alejandro Fernndez-Montes GonzlezUniversity of Sevilla. Spainafdez@us.esEnergy-Saving Policies inGrid-Computing

2. Grid-ComputingEnergy-Saving policies,Efficiency ComparisonGrid5000, Simulation Software, On-off policies, Data Envelopment Analysis 3. Grid-ComputingSmart scheduling for saving energy in grid computingFernndez-Montes et. Al.Expert Systems with applications.http://dx.doi.org/10.1016/j.eswa.2012.02.11515216418520121824025172.61161261341441561691811942072180501001502002500% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%Power(Watts)PerformanceComparison of Power Consumptionw2w2Data Center3 IT energy consumption 3%-5% of CO2 emissions. Manufacturers double electrical efficiency every 1,5years. 4. Grid-ComputingSmart scheduling for saving energy in grid computingFernndez-Montes et. Al.Expert Systems with applications.http://dx.doi.org/10.1016/j.eswa.2012.02.115Data Center4 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 5. Grid-ComputingSmart scheduling for saving energy in grid computingFernndez-Montes et. Al.Expert Systems with applications.http://dx.doi.org/10.1016/j.eswa.2012.02.115Power Management Layers5ComponentPhysicalOperating SystemRackData center ACPI (low-level). ACPI (high-level). Core parking. Aggregation tools. Energy Policies. 6. Grid-ComputingSmart scheduling for saving energy in grid computingFernndez-Montes et. Al.Expert Systems with applications.http://dx.doi.org/10.1016/j.eswa.2012.02.115Grid50006 deployedover 9 Francelocations. Designed to supportcomputational greedytasks. 8560 CPU-cores(a.k.a. resources). 7. Grid-ComputingSmart scheduling for saving energy in grid computingFernndez-Montes et. Al.Expert Systems with applications.http://dx.doi.org/10.1016/j.eswa.2012.02.115Resources7 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 8. Grid-ComputingSmart scheduling for saving energy in grid computingFernndez-Montes et. Al.Expert Systems with applications.http://dx.doi.org/10.1016/j.eswa.2012.02.115Jobs8 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. 9. Grid-ComputingSmart scheduling for saving energy in grid computingFernndez-Montes et. Al.Expert Systems with applications.http://dx.doi.org/10.1016/j.eswa.2012.02.115Graphical representation9resourcestimer0t0r6r5r4r3r2r1t8t7t6t5t4t3t2t1 t10t9Job_idStarttimeStoptimeSubmissiontime 10. Grid-ComputingSmart scheduling for saving energy in grid computingFernndez-Montes et. Al.Expert Systems with applications.http://dx.doi.org/10.1016/j.eswa.2012.02.115Scheduling Energy Policies10 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 11. Grid-ComputingSmart scheduling for saving energy in grid computingFernndez-Montes et. Al.Expert Systems with applications.http://dx.doi.org/10.1016/j.eswa.2012.02.1151. Always On11 Current Grid5000 behaviour. Useful to compute current energyconsumption and to be compared with. 12. Grid-ComputingSmart scheduling for saving energy in grid computingFernndez-Montes et. Al.Expert Systems with applications.http://dx.doi.org/10.1016/j.eswa.2012.02.1152. Always Switch Off12 Always switches resources off. Simplest policy. 13. Grid-ComputingSmart scheduling for saving energy in grid computingFernndez-Montes et. Al.Expert Systems with applications.http://dx.doi.org/10.1016/j.eswa.2012.02.1153. Load13 Load is defined as thepercentage ofresources executing ajob. Depending on currentGrid5000 load, leavethem on, or switchthem off. The thresholdpercentage isparameterized. 14. Grid-ComputingSmart scheduling for saving energy in grid computingFernndez-Montes et. Al.Expert Systems with applications.http://dx.doi.org/10.1016/j.eswa.2012.02.1154. Switch Off TS14 TS is defined as the minimum time thatensures energy saving if a resource is switchedoff between two jobs.Ts =Es - Poff *dtot + EOnOff + EOff OnPIdle - Poff[A.C. Orgerie, et. al, 2009] 15. Grid-ComputingSmart scheduling for saving energy in grid computingFernndez-Montes et. Al.Expert Systems with applications.http://dx.doi.org/10.1016/j.eswa.2012.02.1154. Switch Off TS15 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. 16. Grid-ComputingSmart scheduling for saving energy in grid computingFernndez-Montes et. Al.Expert Systems with applications.http://dx.doi.org/10.1016/j.eswa.2012.02.1155. Random16 Leaves resources on or switch them offrandomly. If other policy is worse, suspect you are doingsomething wrong. 17. Grid-ComputingSmart scheduling for saving energy in grid computingFernndez-Montes et. Al.Expert Systems with applications.http://dx.doi.org/10.1016/j.eswa.2012.02.1156. Exponential17 The exponential model describes timebetween consecutive events. Every time a job finishes, the parameter () 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 18. Grid-ComputingSmart scheduling for saving energy in grid computingFernndez-Montes et. Al.Expert Systems with applications.http://dx.doi.org/10.1016/j.eswa.2012.02.1157. Gamma18 The gamma model describes time betweenevents The mean duration between jobs (), and theratio of available resources and meanresources () are computed. Hence, probability of arrival of new job in atime less than Ts is given byg(k -1,qTs )G(k -1) 19. Grid-ComputingSmart scheduling for saving energy in grid computingFernndez-Montes et. Al.Expert Systems with applications.http://dx.doi.org/10.1016/j.eswa.2012.02.115Arranging policies19 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. 20. Grid-ComputingSmart scheduling for saving energy in grid computingFernndez-Montes et. Al.Expert Systems with applications.http://dx.doi.org/10.1016/j.eswa.2012.02.115Experimentation20 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. 21. Grid-ComputingSmart scheduling for saving energy in grid computingFernndez-Montes et. Al.Expert Systems with applications.http://dx.doi.org/10.1016/j.eswa.2012.02.115Experimentation21 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. 22. Grid-ComputingSmart scheduling for saving energy in grid computingFernndez-Montes et. Al.Expert Systems with applications.http://dx.doi.org/10.1016/j.eswa.2012.02.115Grid5000 Toolbox. Simulation software22 23. Grid-ComputingSmart scheduling for saving energy in grid computingFernndez-Montes et. Al.Expert Systems with applications.http://dx.doi.org/10.1016/j.eswa.2012.02.115Grid5000 Toolbox. Simulation software23 24. # 25. # 26. Grid-ComputingSmart scheduling for saving energy in grid computingFernndez-Montes et. Al.Expert Systems with applications.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 Grid5000infrastructure.o 318 tons of CO2.o 1,163,286 kWh.Madrid Barcelona78 AveMadrid-Barcelona61,314 Eurozone citizens26 27. Alejandro Fernndez-Montes GonzlezUniversity of Sevilla. Spainafdez@us.esEnergy-Saving Policies inGrid-Computing