Energy Efficiency in Cloud Data Centers: Energy Efficient VM Placement for Cloud Data Centers

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. Summary • Objectives • Proposed Algorithms • Evaluation • Conclusion Energy Efficient VM Placement for Cloud Data Centers

3. Objectives Energy aware VM placement in cloud data centers. Propose optimal algorithms for VM allocation and migration to reduce power consumption in cloud data centers Energy Efficient VM Placement for Cloud Data Centers direction ou services <pied de page>

4. Framework • Energy-aware VM placement • Responsible for the optimal energy aware VM placement in the data center. • Energy consomption estimator • Relies on energy estimation tools that use power models to infer power consumption of VMs or servers from resource usage • Cloud Iaas manager • OpenStack, OpenNebula, CloudStack • Control and manage cloud resources, handle clients requests, schedule and provisioning of VMs Energy Efficient VM Placement for Cloud Data Centers direction ou services <pied de page>

5. Proposed algoritms • Exact VM placement algorithm • selects where to deploy VMs • Exact VM Migration algorithm • migrates VMs to achieve consolidation • Adapted energy aware best fit algorithm • used for comparison purposes Energy Efficient VM Placement for Cloud Data Centers direction ou services <pied de page>

6. VM placement algorithmObjective, conditions & constraints • Objective • initial VM placement leading to minimum number of used servers (or containers) • Mathematical Programming Formulation • modelled as a bin packing problem with a minimum power consumption objective Energy Efficient VM Placement for Cloud Data Centers direction ou services <pied de page>

7. VM placement algorithmModel variables

8. VM Migration algorithm • Objective • Optimize data center power consumption using dynamic VM consolidation • Mathematical Programming Formulation • Based on linear integer programming formulation • Zijk= 1 if VM k migrated from server i to j • yi = 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 • qi is the total number of VMs hosted on server i and candidate for migration into destination servers, especially server j in equation Energy Efficient VM Placement for Cloud Data Centers direction ou services <pied de page>

9. VM Migration algorithm Maximize number of empty servers to shut them down by migrating VM to achieve consolidation if a VMk is migrated from a server i (source) to a server j (destination), it can not be migrated to any other server l (l j). Ensuing migrations forbidden Destination VM power budget limit has to be respected 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…. Energy Efficient VM Placement for Cloud Data Centers direction ou services <pied de page>

10. VM Migration algorithm  A VM can not be migrated to many servers at the same time A server candidate to a migration should not migrate its own VMs Energy Efficient VM Placement for Cloud Data Centers direction ou services <pied de page>

11. Adapted energy aware best fit algorithm 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 Energy Efficient VM Placement for Cloud Data Centers direction ou services <pied de page>

12. Evaluation • Proposed algorithms evaluated using the linear solver CPLEX • Estimateexpected percentage of energy or power consumption savings when combining the exact allocation and migration algorithms Energy Efficient VM Placement for Cloud Data Centers direction ou services <pied de page>

13. Evaluation • Comparison between Exact Placement and Best Fit algorithms Energy Efficient VM Placement for Cloud Data Centers direction ou services <pied de page>

14. Evaluation Performance comparison of the exact placement algorithm with and withoutmigration Energy Efficient VM Placement for Cloud Data Centers direction ou services <pied de page>

15. Evaluation • Convergence time of the Exact Placement Algorithm Energy Efficient VM Placement for Cloud Data Centers direction ou services <pied de page>

16. 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) Energy Efficient VM Placement for Cloud Data Centers direction ou services <pied de page>

17. Evaluation • Percentage of gained energy when migration is used Energy Efficient VM Placement for Cloud Data Centers direction ou services <pied de page>

18. 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 Energy Efficient VM Placement for Cloud Data Centers direction ou services <pied de page>

19. PublishedPaper 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