Measuring and Improving Energy
Efficiency of a Data Center in a
Self-Adaptive Context
Monica Vitali
Ph.D. Candidate in Inf...
Goal of the Research
GOAL: proposing a new methodology and novel
techniques to assess and improve Energy Efficiency and
Su...
Goal-Oriented Adaptation
The GOAL-ORIENTED model
Goal-Oriented Adaptation
The GOAL-ORIENTED model
GOAL
ACTION
Goal-Oriented Adaptation
The GOAL-ORIENTED model
GOAL TO GOAL RELATIONS
express the influence
existing between goals
(posi...
Goal-Oriented Adaptation
ACTION TO GOAL RELATIONS
express the effect of an
action over a goal (positive or
negative)
GOAL ...
Goal-Oriented Adaptation
ACTION TO GOAL RELATIONS
express the effect of an
action over a goal (positive or
negative)
GOAL ...
Goal-Oriented Adaptation
ACTION TO GOAL RELATIONS
express the effect of an
action over a goal (positive or
negative)
GOAL ...
Goal-Oriented Adaptation
Which are the system goals?
GPIs
GREEN PERFORMANCE
INDICATORS
related to energy
efficiency (i.e.
Performance per Energy,
R...
Which are the system goals?
Each indicator is associated with thresholds
indicating its maximum and minimum desired value....
Learning Goal to Goal Relations
GOAL to GOAL relations are represented through a
Bayesian Network. It describes influences...
Learning Goal to Goal Relations
GOAL to GOAL relations are represented through a
Bayesian Network. It describes influences...
Learning Goal to Goal Relations
U(x) = CPU usage of x, R(x) = response time of x, PE(x) = performance per energy of x, and...
Learning Goal to Goal Relations
DRAWBACK: computation time increases esponentially with
the number of variables but...
●
E...
Goal-Oriented Adaptation
Which are the treatments?
Repair actions can be used to improve the
system state by fixing indicators violations:
●
Virtua...
Learning Action to Goal Relations
ACTION to GOAL relations are represented through
positive and negative links (positive =...
Learning Action to Goal Relations
ACTION to GOAL relations are represented through
positive and negative links (positive =...
Learning Action to Goal Relations: Exploration
A1(x,y) = migration, A2(x) = add CPU core, A3(x) = remove CPU core, A6(x) =...
Learning Action to Goal Relations: Exploration
A1(x,y) = migration, A2(x) = add CPU core, A3(x) = remove CPU core, A6(x) =...
Learning Action to Goal Relations: Exploitation
The exploitation phase uses the quality matrix to select
the best adaptati...
Learning Action to Goal Relations: Exploitation
The exploitation phase uses the quality matrix to select
the best adaptati...
Learning Action to Goal Relations: Exploitation
The exploitation phase uses the quality matrix to select
the best adaptati...
Learning Action to Goal Relations: Exploitation
DISTANCE
The distance of a context is the sum
of the distance of the indic...
Learning Action to Goal Relations: Exploitation
DISTANCE
The distance of a context is the sum
of the distance of the indic...
Learning Action to Goal Relations: Exploitation
DISTANCE
The distance of a context is the sum
of the distance of the indic...
Learning Action to Goal Relations: Exploitation
DISTANCE
The distance of a context is the sum
of the distance of the indic...
Simulating the DC Behavior
MOTIVATIONS:
●
Monitoring the system behavior under different load
rates
●
Collecting data repo...
Simulating the DC Behavior
SERVER
 ID
 Number of cores
 Peak load power
consumption
VIRTUAL MACHINE
 ID
 Number of co...
Simulating the DC Behavior
HOSTS
S1
RUNSV1 A1
RUNSV3 A3
HOSTSS2 RUNSV2 A2
CONFIGURATION 1
HOSTS
S1
RUNSV1 A1
RUNSV3 A3
HOS...
Simulating the DC Behavior
Final Remarks
ACHIEVEMENTS
●
Metrics selection and proposal for greenness assessment
●
Definition of a goal-oriented model...
Final Remarks
FUTURE WORK
●
Application dependent selection of relevant metrics
●
Monitoring data management and compressi...
Publications
●
B. Pernici and M. Vitali, A Survey on Energy Efficiency in Information Systems, International Journal of
Co...
Measuring and Improving Energy
Efficiency of a Data Center in a
Self-Adaptive Context
Monica Vitali
Ph.D. Candidate in Inf...
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Thesis Presentation on Energy Efficiency Improvement in Data Centers

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Thesis Presentation on Energy Efficiency Improvement in Data Centers

  1. 1. Measuring and Improving Energy Efficiency of a Data Center in a Self-Adaptive Context Monica Vitali Ph.D. Candidate in Information Technology, Cycle: XXVI Dip. Elettronica Informazione e Bioingegneria Politecnico di Milano, Italy
  2. 2. Goal of the Research GOAL: proposing a new methodology and novel techniques to assess and improve Energy Efficiency and Sustainability in a data center from an application perspective, while respecting the constraints established in the Service Level Agreement with the clients. A TYPICAL ENVIRONMENT FOR THE METHODOLOGY APPLICATION
  3. 3. Goal-Oriented Adaptation The GOAL-ORIENTED model
  4. 4. Goal-Oriented Adaptation The GOAL-ORIENTED model GOAL ACTION
  5. 5. Goal-Oriented Adaptation The GOAL-ORIENTED model GOAL TO GOAL RELATIONS express the influence existing between goals (positive or negative) GOAL ACTION
  6. 6. Goal-Oriented Adaptation ACTION TO GOAL RELATIONS express the effect of an action over a goal (positive or negative) GOAL TO GOAL RELATIONS express the influence existing between goals (positive or negative) The GOAL-ORIENTED model GOAL ACTION
  7. 7. Goal-Oriented Adaptation ACTION TO GOAL RELATIONS express the effect of an action over a goal (positive or negative) GOAL TO GOAL RELATIONS express the influence existing between goals (positive or negative) CONTEXT The best repair action depends from the state of the system The GOAL-ORIENTED model GOAL ACTION
  8. 8. Goal-Oriented Adaptation ACTION TO GOAL RELATIONS express the effect of an action over a goal (positive or negative) GOAL TO GOAL RELATIONS express the influence existing between goals (positive or negative) CONTEXT The best repair action depends from the state of the system DYNAMIC ENVIRONMENT Relations between goals and actions effects may change over time The GOAL-ORIENTED model GOAL ACTION
  9. 9. Goal-Oriented Adaptation
  10. 10. Which are the system goals? GPIs GREEN PERFORMANCE INDICATORS related to energy efficiency (i.e. Performance per Energy, Resources Usage Metrics) KPIs KEY PERFORMANCE INDICATORS related to quality of service (i.e. Response Time, Availability, Throughput) Indicators at several granularity level are used to measure the behavior of an application Which relations exist among indicators? Which indicators are significant in a system?
  11. 11. Which are the system goals? Each indicator is associated with thresholds indicating its maximum and minimum desired value. Thresholds are decided according to the Service level agreement and considering indications contained in the Green Grid Data Center Maturity Model (DCMM) Indicator Value
  12. 12. Learning Goal to Goal Relations GOAL to GOAL relations are represented through a Bayesian Network. It describes influences existing between the indicators states. Why it is important? ● Allows what if analysis and prediction ● Enables indirect repair action application ● Does not rely on experts
  13. 13. Learning Goal to Goal Relations GOAL to GOAL relations are represented through a Bayesian Network. It describes influences existing between the indicators states. Three steps learning process: ● Structure learning (using correlations) ● Directionality learning (using MMHC algorithm) ● Parameters learning (using Maximum a Priori Estimation)
  14. 14. Learning Goal to Goal Relations U(x) = CPU usage of x, R(x) = response time of x, PE(x) = performance per energy of x, and E(x) = energy consumption of x EXPERIMENT: results obtained with a 2 servers – 3 virtual machines configuration. Prediction success rate with different parameters
  15. 15. Learning Goal to Goal Relations DRAWBACK: computation time increases esponentially with the number of variables but... ● Execution can be performed off line ● It is possible to define an ontology and to generalize some relations, reducing the number of variables that need to be considered DYNAMIC ENVIRONMENT: when components are added or removed from the system configuration only a limited part of the network is involved (BN conditional independence)
  16. 16. Goal-Oriented Adaptation
  17. 17. Which are the treatments? Repair actions can be used to improve the system state by fixing indicators violations: ● Virtual level - migration, reconfiguration, duplication, removal ● Server level – turn on/off server, storage migration, frequency scaling, storage mode modification ● Process level – skip/execute optional activity ● Do Nothing
  18. 18. Learning Action to Goal Relations ACTION to GOAL relations are represented through positive and negative links (positive = action increasing the indicator value). Why it is important? ● Context dependent adaptation ● Responsive to environment modification
  19. 19. Learning Action to Goal Relations ACTION to GOAL relations are represented through positive and negative links (positive = action increasing the indicator value). Two steps process: ● Exploration: learning the action – goal connections ● Exploitation: selecting the best action given a context
  20. 20. Learning Action to Goal Relations: Exploration A1(x,y) = migration, A2(x) = add CPU core, A3(x) = remove CPU core, A6(x) = Turn on server x, A7(x) = Turn off server x, AC1(x) = migrate + turn off, AC2(x,y) = turn on + migrate The exploration phase enables the construction of a matrix describing the effect of actions over goals
  21. 21. Learning Action to Goal Relations: Exploration A1(x,y) = migration, A2(x) = add CPU core, A3(x) = remove CPU core, A6(x) = Turn on server x, A7(x) = Turn off server x, AC1(x) = migrate + turn off, AC2(x,y) = turn on + migrate The exploration phase enables the construction of a matrix describing the effect of actions over goals DYNAMIC ENVIRONMENT: At each application of an action, the information in the matrix are updated. The importance of the new value depends on the time elapsed since the last application.
  22. 22. Learning Action to Goal Relations: Exploitation The exploitation phase uses the quality matrix to select the best adaptation action given the context EXAMPLE: 2 servers – 2 virtual machines. Server 2 is off. CPU usage is low for V1 and high for V2. Everything else is satisfied.
  23. 23. Learning Action to Goal Relations: Exploitation The exploitation phase uses the quality matrix to select the best adaptation action given the context EXAMPLE: 2 servers – 2 virtual machines. Server 2 is off. CPU usage is low for V1 and high for V2. Everything else is satisfied. Actions available Add a core Remove a core Turn on a server Turn on a server and migrate a VM on it
  24. 24. Learning Action to Goal Relations: Exploitation The exploitation phase uses the quality matrix to select the best adaptation action given the context EXAMPLE: 2 servers – 2 virtual machines. Server 2 is off. CPU usage is low for V1 and high for V2. Everything else is satisfied. Actions available Add a core Remove a core Turn on a server Turn on a server and migrate a VM on it w= 1 w= -0.5 penalty
  25. 25. Learning Action to Goal Relations: Exploitation DISTANCE The distance of a context is the sum of the distance of the indicators from the optimal state: distance (C)=∑ distance( I n ,C) EXPERIMENT: results obtained with a 2 servers – 2 virtual machines configuration (server 2 initially off).
  26. 26. Learning Action to Goal Relations: Exploitation DISTANCE The distance of a context is the sum of the distance of the indicators from the optimal state: distance (C)=∑ distance( I n ,C) The load is low. The algorithm select the resource reconfiguration action and improves the initial condition by reducing the amount of resources allocated to the VMs
  27. 27. Learning Action to Goal Relations: Exploitation DISTANCE The distance of a context is the sum of the distance of the indicators from the optimal state: distance (C)=∑ distance( I n ,C) The system reacts to the load growth by turning on a new server and migrate a VM on it. Also, according to the demand, additional resources are assigned to the VMs
  28. 28. Learning Action to Goal Relations: Exploitation DISTANCE The distance of a context is the sum of the distance of the indicators from the optimal state: distance (C)=∑ distance( I n ,C) The system gets saturated. The number of violated indicator is higher than in the initial configuration because of the second server. However, even if in violation, the performance of the system is improved as far as possible
  29. 29. Simulating the DC Behavior MOTIVATIONS: ● Monitoring the system behavior under different load rates ● Collecting data reports of energy efficiency and quality of service of a data center ● Perform experiments in a repeatable, dependable, and scalable environment ● Predicting the outcome of a system design change without affecting performance ● Detect system bottlenecks before deploying on the real system
  30. 30. Simulating the DC Behavior SERVER  ID  Number of cores  Peak load power consumption VIRTUAL MACHINE  ID  Number of cores  Activity ID ACTIVITY  ID  Number of operations  Average CPU demand  Average completion time  Probability of execution Load: number of requests received in a fixed time T
  31. 31. Simulating the DC Behavior HOSTS S1 RUNSV1 A1 RUNSV3 A3 HOSTSS2 RUNSV2 A2 CONFIGURATION 1 HOSTS S1 RUNSV1 A1 RUNSV3 A3 HOSTSS2 RUNSV2 A2 CONFIGURATION 3 HOSTSS1 RUNSV1 A1 RUNSV3 A3 HOSTS S2 off RUNSV2 A2 CONFIGURATION 2 HOSTSS3 WHAT-IF Migrate V3 and turn off S2 WHAT-IF Turn on S3 and migrate V3 on S3
  32. 32. Simulating the DC Behavior
  33. 33. Final Remarks ACHIEVEMENTS ● Metrics selection and proposal for greenness assessment ● Definition of a goal-oriented model for efficiency management of data centers ● Representation and learning of indicators relations ● Automatic and adaptive selection of a repair strategy for improving the data center state ● Definition of a simulation environment for data centers OTHER ACHIEVEMENTS ● Hierarchical representation of Green IT and Green IS approaches
  34. 34. Final Remarks FUTURE WORK ● Application dependent selection of relevant metrics ● Monitoring data management and compression ● Generalization of goal-to-goal and action-to-goal relations using an ontology ● Adaptation for cloud environments
  35. 35. Publications ● B. Pernici and M. Vitali, A Survey on Energy Efficiency in Information Systems, International Journal of Cooperative Information Systems (IJCIS) ● M. Fugini, T. Jiang, A. Kipp, J. Liu, B. Pernici, I. Salomie, M. Vitali, Applying Green Metrics to optimise the energy consumption footprint of IT service centres, International Journal of Space-Based and Situated Computing (IJSSC), September 2012 ● C. Cappiello, D. Chen, E. Henis, T. Jiang, R. I. Kat, A. Kipp , J. Liu, A. Mello Ferreira, B. Pernici, D.Sotnikov, M. Vitali, Usage centric green performance indicators. ACM SIGMETRICS Performance Evaluation Review, Volume 39 Issue 3, pp 92-96, December 2011 ● M. Vitali, U.-M. O’Reilly, and K. Veeramachaneni, Modeling Service Execution on Data Centers for Energy Efficiency and Quality of Service Monitoring, IEEE International Conference on Systems, Man, and Cybernetics (SMC2013), 2013 ● C. Cappiello, P. Plebani, and M. Vitali, Energy-Aware Process Design Optimization, Proceedings of the EuroEcoDC workshop, within the 3rd International Conference on Cloud and Green Computing, 2013 ● I. Anghel, D. Arnone, M. Bertoncini, C. Cappiello, D. Chen, W. Christmann, M. Fugini, G. Goldberg, E. Henis, T. Jiang, R. Kat, A. Kipp, B. Pernici, P. Plebani, A. Rossi, I. Salomie, M. Vitali, and M. Vor dem Berge, Setting energy efficiency goals in data centres: the GAMES approach, In proc. of the 1st International Workshop on Energy-Efficient Data Centres (E2DC 2012), Madrid, May 2012 ● C. Cappiello, A. Mello Ferreira, M. Fugini, P. Plebani, and M. Vitali, Business process co-design for energy-aware adaptation, In Proc. of ICCP, Cluj-Napoca, Romania, August 2011 ● C. Cappiello, D. Chen, E. Henis, T. Jiang, R. I. Kat, A. Kipp , J. Liu, A. Mello Ferreira, B. Pernici, D.Sotnikov, M. Vitali, Usage centric green performance indicators. In Proc. Of GreenMetrics 2011 (Workshop of SIGMETRICS 2011) , San Jose, California, USA, 2011
  36. 36. Measuring and Improving Energy Efficiency of a Data Center in a Self-Adaptive Context Monica Vitali Ph.D. Candidate in Information Technology, Cycle: XXVI Dip. Elettronica Informazione e Bioingegneria Politecnico di Milano, Italy

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