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

Doctoral Student : Chaima Ghribi Advisor : Djamal ZeghlacheCo-Author : Makhlouf Hadji

Wireless Networks and Multimedia Services DepartmentCNRS UMR 5157-Samovar, Telecom SudParis

ICWS 2011, Washington DC, USA. Implementation of Communities of Web Service Registries

Summary

Objectives

Proposed Algorithms

Evaluation

Conclusion

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ICWS 2011, Washington DC, USA. Implementation of Communities of Web Service Registries

Objectives

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Energy aware VM placement in cloud data centers.

Propose optimal algorithms for VM allocation and migration to reduce power consumption in cloud data centers

ICWS 2011, Washington DC, USA. Implementation of Communities of Web Service Registries

Framework

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• Energy-aware VM placement o Responsible for the optimal energy

aware VM placement in the data center.

• Energy consomption estimatoro Relies on energy estimation tools that

use power models to infer power consumption of VMs or servers from resource usage

• Cloud Iaas managero OpenStack, OpenNebula, CloudStacko Control and manage cloud resources,

handle clients requests, schedule and provisioning of VMs

ICWS 2011, Washington DC, USA. Implementation of Communities of Web Service Registries

Proposed algoritms

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Exact VM placement algorithmo selects where to deploy VMs

Exact VM Migration algorithmo migrates VMs to achieve consolidation

Adapted energy aware best fit algorithmo used for comparison purposes

ICWS 2011, Washington DC, USA. Implementation of Communities of Web Service Registries

VM placement algorithmObjective, conditions & constraints

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Objectiveo initial VM placement leading to minimum number of used servers

(or containers)

Mathematical Programming Formulationo modelled as a bin packing problem with a minimum power

consumption objectiveVariable commentm Number of servers

Pj,MaxServer power consumption limit

Pj, current Current power consumption

ej Boolean = 1 if j hosts VM

xij Boolean = 1 if VM I assigned to server j

n Number of requested VMs

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VM placement algorithmModel variables

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Objective oOptimize data center power

consumption using dynamic VM consolidation

Mathematical Programming Formulationo 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

VM Migration algorithm

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VM Migration algorithmMaximize 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 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).

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….

ICWS 2011, Washington DC, USA. Implementation of Communities of Web Service Registries

VM Migration algorithm

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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

ICWS 2011, Washington DC, USA. Implementation of Communities of Web Service Registries

Adapted energy aware best fit algorithm

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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

ICWS 2011, Washington DC, USA. Implementation of Communities of Web Service Registries

Evaluation

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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

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Evaluation

Comparison between Exact Placement and Best Fit algorithms

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Evaluation

Performance comparison of the exact placement algorithm with and without migration

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Evaluation

Convergence time of the Exact Placement Algorithm

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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)

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Evaluation

Percentage of gained energy when migration is used

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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

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