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
Summary
page 1

Objectives

Proposed Algorithms

Evaluation

Conclusion
ICWS 2011, Washington DC, USA.
Implementation
of Communities
of Webfor
Service
Registries
Energy Efficient
VM Placement
Cloud
Data Centers
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
page 2
ICWS 2011,ou
Washington
direction
servicesDC, USA.
Implementation
of Communities
of Webfor
Service
Energy
Efficient
VM Placement
CloudRegistries
Data Centers
<pied
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Framework
• 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
page 3
ICWS 2011,ou
Washington
direction
servicesDC, USA.
Implementation
of Communities
of Webfor
Service
Energy
Efficient
VM Placement
CloudRegistries
Data Centers
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Proposed algoritms
 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
page 4
ICWS 2011,ou
Washington
direction
servicesDC, USA.
Implementation
of Communities
of Webfor
Service
Energy
Efficient
VM Placement
CloudRegistries
Data Centers
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VM placement algorithm
Objective, conditions & constraints
 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
page 5
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Variable
comment
m
Number of servers
Pj,Max
Server 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
Implementation
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Service
Energy
Efficient
VM Placement
CloudRegistries
Data Centers
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VM placement algorithm
Model variables
6/20
ICWS 2011, Washington DC, USA.
Implementation of Communities of Web Service Registries
VM Migration algorithm
 Objective
o Optimize data center power
consumption using dynamic VM
consolidation
 Mathematical Programming
Formulation
o 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
page 7
ICWS 2011,ou
Washington
direction
servicesDC, USA.
Implementation
of Communities
of Webfor
Service
Energy
Efficient
VM Placement
CloudRegistries
Data Centers
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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….
page 8
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Implementation
of Communities
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Service
Energy
Efficient
VM Placement
CloudRegistries
Data Centers
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VM Migration algorithm
 A server candidate to a migration
should not migrate its own VMs
page 9
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 A VM can not be migrated to many
servers at the same time
Implementation
of Communities
of Webfor
Service
Energy
Efficient
VM Placement
CloudRegistries
Data Centers
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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
page 10
ICWS 2011,ou
Washington
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Implementation
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Service
Energy
Efficient
VM Placement
CloudRegistries
Data Centers
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Evaluation
 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
page 11
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Washington
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servicesDC, USA.
Implementation
of Communities
of Webfor
Service
Registries
Energy
Efficient
VM Placement
Cloud
Data Centers
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Evaluation

page 12
Comparison between Exact Placement and Best Fit algorithms
ICWS 2011,ou
Washington
direction
servicesDC, USA.
Implementation
of Communities
of Webfor
Service
Registries
Energy
Efficient
VM Placement
Cloud
Data Centers
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Evaluation
 Performance comparison of the exact placement algorithm with and without migration
page 13
ICWS 2011,ou
Washington
direction
servicesDC, USA.
Implementation
of Communities
of Webfor
Service
Energy
Efficient
VM Placement
CloudRegistries
Data Centers
<pied
de page>
Evaluation

page 14
Convergence time of the Exact Placement Algorithm
ICWS 2011,ou
Washington
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Implementation
of Communities
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Service
Energy
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VM Placement
CloudRegistries
Data Centers
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Evaluation


Convergence time of the Exact Migration Algorithm (m’=5)
Convergence time of the Exact Migration
Algorithm (m’=10)
page 15
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
Convergence time of the Exact Migration
Algorithm (m’=20)
Implementation
of Communities
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Service
Energy
Efficient
VM Placement
CloudRegistries
Data Centers
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Evaluation

page 16
Percentage of gained energy when migration is used
ICWS 2011,ou
Washington
direction
servicesDC, USA.
Implementation
of Communities
of Webfor
Service
Energy
Efficient
VM Placement
CloudRegistries
Data Centers
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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
page 17
ICWS 2011,ou
Washington
direction
servicesDC, USA.
Implementation
of Communities
of Webfor
Service
Energy
Efficient
VM Placement
CloudRegistries
Data Centers
<pied
de page>
Published Paper
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