Quantifying the Energy Impact of Green Cloud Computing Users
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1 Quantifying the Energy Impact of Green Cloud Computing Users David Guyon University of Rennes 1 david.guyon@irisa.fr Anne-Cécile Orgerie CNRS Christine Morin Inria July 5 th, 2016 David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
2 Summary General Context and Motivation Thesis Presentation Contributions Conclusion David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
3 Summary General Context and Motivation General Context and Motivation Thesis Presentation Contributions Conclusion David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
4 General Context The cloud computing technology David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
5 General Context The cloud computing technology David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
6 General Context The cloud computing technology David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
7 General Context The cloud computing technology David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
8 General Context The cloud computing technology David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
9 Motivation The cloud consumes an enormous amount of energy The Cloud consumes around 2% of the worldwide total energy Quadruple by 2020 if the demand continues to go on David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
10 Summary Thesis Presentation General Context and Motivation Thesis Presentation Contributions Conclusion David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
11 Thesis Subject Supporting energy-awareness for Cloud users Rendre les nuages plus verts grâce aux utilisateurs David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
12 Thesis Objective David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
13 Thesis Objective David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
14 Thesis Objective David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
15 Thesis Objective David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
16 Thesis Objective David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
17 Problematic David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
18 Summary Contributions General Context and Motivation Thesis Presentation Contributions Include Cloud users in IaaS optimization loop Give to users the energy consumed by their VMs Conclusion David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
19 Include Cloud users into the IaaS energy optimization loop David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
20 Contribution Motivation David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
21 Contribution Motivation David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
22 Contribution Motivation Existing solutions use consolidation Turning off as many hosts as possible Do not take the user into consideration Complex to configure David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
23 Contribution Motivation Existing solutions use consolidation Turning off as many hosts as possible Do not take the user into consideration Complex to configure David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
24 Contribution Motivation Existing solutions use consolidation Turning off as many hosts as possible Do not take the user into consideration Complex to configure David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
25 Contribution Motivation Existing solutions use consolidation Turning off as many hosts as possible Do not take the user into consideration Complex to configure David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
26 Contribution Objective To reduce the electrical consumption of the Cloud by including the user in the optimization system David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
27 Our Research Contribution Give the user an easy-to-use parameter 1. Easy-to-use interface to involve the user 2. Algo to select VM size depending on chosen execution mode 3. Algo for the VMs placement on the servers 4. Prototype for the evaluation of the benefits of our approach David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
28 Our Research Contribution Give the user an easy-to-use parameter 1. Easy-to-use interface to involve the user 2. Algo to select VM size depending on chosen execution mode 3. Algo for the VMs placement on the servers 4. Prototype for the evaluation of the benefits of our approach David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
29 Our Research Contribution Give the user an easy-to-use parameter 1. Easy-to-use interface to involve the user 2. Algo to select VM size depending on chosen execution mode 3. Algo for the VMs placement on the servers 4. Prototype for the evaluation of the benefits of our approach David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
30 Our Research Contribution Give the user an easy-to-use parameter 1. Easy-to-use interface to involve the user 2. Algo to select VM size depending on chosen execution mode 3. Algo for the VMs placement on the servers 4. Prototype for the evaluation of the benefits of our approach David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
31 User s Application Data-intensive scientific workflow David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
32 User s Application Data-intensive scientific workflow David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
33 Virtual Machine Selection depending on the trade-off Each VM has a flavor type David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
34 Virtual Machine Selection depending on the trade-off Each VM has a flavor type David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
35 Selection of the Best Suitable Host for a VM 1 unit = tiny flavor David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
36 Selection of the Best Suitable Host for a VM blue triangle = Virtual Machine to create David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
37 Selection of the Best Suitable Host for a VM David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
38 Selection of the Best Suitable Host for a VM calculation David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
39 Selection of the Best Suitable Host for a VM calculation David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
40 Selection of the Best Suitable Host for a VM calculation David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
41 Selection of the Best Suitable Host for a VM calculation David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
42 Selection of the Best Suitable Host for a VM all are equal to 3 David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
43 Selection of the Best Suitable Host for a VM number of to 0 calculation David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
44 Selection of the Best Suitable Host for a VM number of to 0 calculation David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
45 Selection of the Best Suitable Host for a VM host 4 is the best suitable host David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
46 Experimentation Results Average values after 5 experiments on each execution mode D. Guyon et al., Energy-efficient User-oriented Cloud Elasticity for Data-driven Applications, GreenCom15, Sydney David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
47 Experimentation Results Average values after 5 experiments on each execution mode D. Guyon et al., Energy-efficient User-oriented Cloud Elasticity for Data-driven Applications, GreenCom15, Sydney David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
48 Experimentation Results Average values after 5 experiments on each execution mode D. Guyon et al., Energy-efficient User-oriented Cloud Elasticity for Data-driven Applications, GreenCom15, Sydney David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
49 Contribution Extension To know at which percent of green users energy-aware Cloud systems start to save in energy David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
50 Experimental Setup Cloud simulator architecture David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
51 Experimentation Results Average of 10 simulations of an infrastructure with 330 hosts used during 24h Big Medium Little Energy (KWh) Hosts used Percent David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
52 Experimentation Results Average of 10 simulations of an infrastructure with 330 hosts used during 24h Big Medium Little Energy (KWh) Hosts used Percent David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
53 Experimentation Results Average of 10 simulations of an infrastructure with 330 hosts used during 24h Big Medium Little Energy (KWh) Hosts used Percent David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
54 Inform Cloud users about the energy consumed by their Virtual Machines David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
55 Contribution Motivation David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
56 Contribution Motivation David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
57 Contribution Motivation David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
58 Contribution Objective To model the energy consumption of the VMs and give an understandable metric to the users David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
59 Summary Conclusion General Context and Motivation Thesis Presentation Contributions Conclusion David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
60 Conclusion First contribution published at the GreenCom conference Second contribution sent to the IGSC conference Ongoing work on VM energy consumption model David Compas 16 Quantifying the Energy Impact of Green Cloud Computing Users July 5 th, / 26
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