1. Energy Efficiency in Cloud Data Centers: Energy Efficient VM Placement for Cloud Data Centers Doctoral Student : Chaima Ghribi Advisor : Djamal Zeghlache Co-Author : Makhlouf Hadji Wireless Networks and Multimedia Services Department CNRS UMR 5157-Samovar, Telecom SudParis

2. ICWS 2011, Washington DC, USA. Implementation of Communities of Web Service Registries Summary  Objectives  Proposed Algorithms  Evaluation  Conclusion page 1 Energy Efficient VM Placement for Cloud Data Centers

3. ICWS 2011, Washington DC, USA. Implementation of Communities of Web Service Registries Objectives page 2direction ou services Energy Efficient VM Placement for Cloud Data Centers Energy aware VM placement in cloud data centers. Propose optimal algorithms for VM allocation and migration to reduce power consumption in cloud data centers

4. ICWS 2011, Washington DC, USA. Implementation of Communities of Web Service Registries Framework page 3direction ou services Energy Efficient VM Placement for Cloud Data Centers Energy-aware VM placement o Responsible for the optimal energy aware VM placement in the data center. Energy consomption estimator o Relies on energy estimation tools that use power models to infer power consumption of VMs or servers from resource usage Cloud Iaas manager o OpenStack, OpenNebula, CloudStack o Control and manage cloud resources, handle clients requests, schedule and provisioning of VMs

5. ICWS 2011, Washington DC, USA. Implementation of Communities of Web Service Registries Proposed algoritms page 4direction ou services Energy Efficient VM Placement for Cloud Data Centers Exact VM placement algorithm o selects where to deploy VMs Exact VM Migration algorithm o migrates VMs to achieve consolidation Adapted energy aware best fit algorithm o used for comparison purposes

6. ICWS 2011, Washington DC, USA. Implementation of Communities of Web Service Registries VM placement algorithm Objective, conditions & constraints page 5direction ou services Energy Efficient VM Placement for Cloud Data Centers Objective o initial VM placement leading to minimum number of used servers (or containers) Mathematical Programming Formulation o modelled as a bin packing problem with a minimum power consumption objective Variablecomment mNumber of servers P j,Max Server power consumption limit P j, current Current power consumption ejej Boolean = 1 if j hosts VM x ij Boolean = 1 if VM I assigned to server j nNumber of requested VMs

7. ICWS 2011, Washington DC, USA. Implementation of Communities of Web Service Registries 6/20 VM placement algorithm Model variables

8. ICWS 2011, Washington DC, USA. Implementation of Communities of Web Service Registries page 7direction ou services Energy Efficient VM Placement for Cloud Data Centers Objective o Optimize data center power consumption using dynamic VM consolidation Mathematical Programming Formulation o Based on linear integer programming formulation Z ijk = 1 if VM k migrated from server i to j y i = 1 if server i idle and = 0 if at least one VM is active m’ = number of non idle servers m’< m P’ k = power cost when migrating VM k q i is the total number of VMs hosted on server i and candidate for migration into destination servers, especially server j in equation VM Migration algorithm

9. ICWS 2011, Washington DC, USA. Implementation of Communities of Web Service Registries page 8direction ou services Energy Efficient VM Placement for Cloud Data Centers VM Migration algorithm Maximize number of empty servers to shut them down by migrating VM to achieve consolidation Destination VM power budget limit has to be respected Ensuing migrations forbidden if a VM k is migrated from a server i (source) to a server j (destination), it can not be migrated to any other server l (l  j). Non idle servers candidate for migration have to be entirely emptied Equivalent total number of empty servers Do not migrate a VM whose job is about to end….

10. ICWS 2011, Washington DC, USA. Implementation of Communities of Web Service Registries VM Migration algorithm page 9direction ou services Energy Efficient VM Placement for Cloud Data Centers A server candidate to a migration should not migrate its own VMs A VM can not be migrated to many servers at the same time

11. ICWS 2011, Washington DC, USA. Implementation of Communities of Web Service Registries Adapted energy aware best fit algorithm page 10direction ou services Energy Efficient VM Placement for Cloud Data Centers Adaptation of the Best-Fit heuristic which consists of : Sorting items (VMs) in a decreasing sequence of their power consumption. Place all the sorted VMs by considering the first item (VM) in a server with a minimum remaining power budget

12. ICWS 2011, Washington DC, USA. Implementation of Communities of Web Service Registries Evaluation page 11direction ou services Energy Efficient VM Placement for Cloud Data Centers Proposed algorithms evaluated using the linear solver CPLEX Estimate expected percentage of energy or power consumption savings when combining the exact allocation and migration algorithms

13. ICWS 2011, Washington DC, USA. Implementation of Communities of Web Service Registries page 12direction ou services Energy Efficient VM Placement for Cloud Data Centers Evaluation Comparison between Exact Placement and Best Fit algorithms

14. ICWS 2011, Washington DC, USA. Implementation of Communities of Web Service Registries page 13direction ou services Energy Efficient VM Placement for Cloud Data Centers Evaluation Performance comparison of the exact placement algorithm with and without migration

15. ICWS 2011, Washington DC, USA. Implementation of Communities of Web Service Registries page 14direction ou services Energy Efficient VM Placement for Cloud Data Centers Evaluation Convergence time of the Exact Placement Algorithm

16. ICWS 2011, Washington DC, USA. Implementation of Communities of Web Service Registries page 15direction ou services Energy Efficient VM Placement for Cloud Data Centers Evaluation Convergence time of the Exact Migration Algorithm (m’=5) Convergence time of the Exact Migration Algorithm (m’=10) Convergence time of the Exact Migration Algorithm (m’=20)

17. ICWS 2011, Washington DC, USA. Implementation of Communities of Web Service Registries page 16direction ou services Energy Efficient VM Placement for Cloud Data Centers Evaluation Percentage of gained energy when migration is used

18. ICWS 2011, Washington DC, USA. Implementation of Communities of Web Service Registries page 17direction ou services Energy Efficient VM Placement for Cloud Data Centers Conclusion Room for additional energy savings in data centers through even more efficient algorithms – joint / one shot scheduling and placement with reduced need for consolidation Pursue the quest for more efficient algorithms Looking currently at scheduling and placement leading to minimum power or energy consumption using graph coloring techniques

19. Chaima Ghribi, Makhlouf Hadji, Djamal Zeghlache, "Energy Efficient VM Scheduling for Cloud Data Centers: Exact Allocation and Migration Algorithms," ccgrid, pp.671-678, 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, 2013 Published Paper