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An Energy Aware Framework for Virtual 
Machine Placement in Cloud Federated 
Data Centres 
Corentin Dupont 
Authors: Corentin Dupont (Create-Net); Giovanni Giuliani (HP Italy); 
Fabien Hermenier (INRIA); Thomas Schulze (Uni Mannheim); Andrey 
Somov (Create-Net)
Presentation 
ü FIT4Green seeks energy saving policies for DCs, enhancing the 
effects inside a federation by an aggressive strategy for 
reducing the energy consumption in ICT 
ü We aim at reducing cost for companies è Strengthening 
competitive position 
ü FIT4Green needs to be DC framework agnostic: 
§ Demonstrated in Cloud computing, Traditional computing, Super 
computing and Networking
Table of Contents 
ü Introduction 
ü Requirements 
ü Framework design 
ü SLA Constraints 
ü Power Objective Model 
ü Heuristics 
ü Experiments on Cloud Test-bed 
ü Scalability Evaluation 
ü Conclusion & Future work
Introduction 
The policies seek to: 
Consolidate application/services 
and turn unused servers off. 
Relocate application/services 
to efficient servers 
The strategies are ranked through their Energy KPIs
Single allocation 
Introduction 
Find the most energy efficient and suitable 
resource for a new Workload. 
Global optimization 
Rearrange the resources in a way that saves 
maximum amount of energy or carbon emission. 
Page 5
Requirements 
• Flexibility, extensibility 
• Deep exploration of the search space 
Abstracting out the constraints
Framework Design
SLA CONSTRAINTS 
SLA constraints flow
SLA CONSTRAINTS 
SLA constraints examples 
Category Constraint Approach LoC 
Hardware HDD Choco + ext. Entropy 121+(25) 
CPUCores Entropy (‘fence’) 0+(25) 
CPUFreq Entropy (‘fence’) 0+(25) 
RAM Choco + ext. Entropy 123+(25) 
GPUCores Entropy (‘fence’) 0+(25) 
GPUFreq Entropy (‘fence’) 0+(47) 
RAIDLevel Entropy (‘fence’) 0+(47) 
QoS MaxCPULoad Choco + ext. Entropy 90+(25) 
MaxVLoadPerCore Choco + ext. Entropy 109+(25) 
MaxVCPUPerCore Choco + ext. Entropy 124+(25) 
Bandwidth Entropy (‘fence’) 0+(49) 
MaxVMperServer Entropy (‘capacity’) 0+(25) 
Availability PlannedOutages Choco + ext. Entropy Future Work 
Availability Choco + ext. Entropy Future Work 
Additional Metrics Dedicated Server Entropy (‘capacity’) 0 + (25) 
Access Entropy (‘fence’) 0 + (25)
POWER OBJECTIVE MODEL 
Total Reconf. Energy 
Total Instant. Power * Reconf Time 
Energy Migrations Energy On/Off 
Power Servers Idle Power VMs Power Network 
Power Calculator
HEURISTICS 
Root node: 
no VM is allocated 
First level node: 
VM1 allocated on S1 
First level node: 
VM2 allocated on S1 
At each level: call F4G 
branching heuristic. If a 
constraint is broken, 
backtrack to go up. 
First level node: 
VMx allocated on Sy 
Leaf node: 
all VMs are allocated 
Leaf node: 
all VMsL eaaref naollodcea: t ed 
all VMsL eaaref naollodcea: t ed 
all VMs are allocated 
At leaf level: note down 
the solution and the 
energy saved, then 
backtrack to find a 
better solution. 
First level node: 
VMxF iarsllot cleavteeld n oond eS:y 
VMx allocated on Sy
Heuristics 
Call the F4G VM 
selector 
Select VM on the least 
energy efficient server and 
least loaded server 
VM selected 
Call the F4G 
Server selector 
Select Server which is the 
most energy efficient 
server and most loaded 
server 
Server selected 
Call the F4G Server 
selector 
Select Server which is 
empty and the least energy 
efficient server 
Server selected 
Composable heuristics 
• Candidate VM 
for migration 
• Target server 
for migration 
• Candidate Server 
for extinction
Heuritics 
To sum up…
Experiments on Cloud Testbed 
Lab trial ressources 
Blade Enclosure 1 Blade Enclosure 2 
Node 
Controller 
Node 
Controller 
Node 
Controller 
Node 
Controller 
Node 
Controller 
Node 
Controller 
Node 
Controller 
Cluster 
Controller 
Power and 
Monitoring 
Collector 
Cluster 
Controller 
Cloud 
Controller 
Task scheduler 
FIT4Green VMs 
Enclosure 1 Enclosure 2 
Processor model Intel Xeon 
E5520 
Intel Xeon 
E5540 
CPU frequency 2.27GHz 2.53GHz 
Cpu& Cores Dual cpu – Quad 
core 
Dual cpu – Quad 
core 
RAM 24 GB 24GB 
• DC1: 4 BL 460c blades using VMWare ESX v4.0 native hypervisor, 3 
blades for Cluster and Cloud Control 
• DC2: 3 BL460c blades using VMWare ESX v4.0 native hypervisor, 2 
blades for Cluster Control and Power and Monitoring System.
Experiments on Cloud Testbed 
Lab trial Workload 
Number of 
active VMs 
Time 
Total number of active virtual machines during full week of work 
Active SLAs constraints: 
• Max vCPU per core = 2 
• Min VM Slot = 3 
• Max VM Slot = 6
Experiments on Cloud Testbed 
Final test results for the various configurations 
Configuration Data 
Centre 1 
Data 
Centre 2 
Energy for 
Federation 
W i t h o u t 
FIT4Green 
6350 Wh 4701 Wh 11051 Wh 
W i t h 
F I T 4 G r e e n 
S t a t i c 
Allocation 
5190 Wh 4009 Wh 9199 Wh 
Saving 
16.7% 
W i t h 
F I T 4 G r e e n 
D y n a m i c 
Allocation 
5068 Wh 3933 Wh 9001 Wh 
Saving 
18.5% 
W i t h 
F I T 4 G r e e n 
O p t i m i z e d 
Policies 
4860 Wh 3785 Wh 8645 Wh 
Saving 
21.7%
Scalability Evaluation 
# Configuration Placement constraints activated 
1 1 datacenter none 
2 1 datacenter 
with overbooking factor=2 
“MaxVCPUPerCore” constraint 
set on each server 
3 2 federated datacenters “Fence” constraint set on each 
VM
CONCLUSION & FUTURE WORK 
ü Energy aware resource allocation in datacenters 
ü Flexibility & extensibility 
ü Saves up to 18% in HP experiment 
ü Scalability with parallel processing 
ü Future work: 
ü SLA re-negotiation 
ü Green SLAs
An energy aware framework for virtual machine placement in cloud federated data centres

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An energy aware framework for virtual machine placement in cloud federated data centres

  • 1. An Energy Aware Framework for Virtual Machine Placement in Cloud Federated Data Centres Corentin Dupont Authors: Corentin Dupont (Create-Net); Giovanni Giuliani (HP Italy); Fabien Hermenier (INRIA); Thomas Schulze (Uni Mannheim); Andrey Somov (Create-Net)
  • 2. Presentation ü FIT4Green seeks energy saving policies for DCs, enhancing the effects inside a federation by an aggressive strategy for reducing the energy consumption in ICT ü We aim at reducing cost for companies è Strengthening competitive position ü FIT4Green needs to be DC framework agnostic: § Demonstrated in Cloud computing, Traditional computing, Super computing and Networking
  • 3. Table of Contents ü Introduction ü Requirements ü Framework design ü SLA Constraints ü Power Objective Model ü Heuristics ü Experiments on Cloud Test-bed ü Scalability Evaluation ü Conclusion & Future work
  • 4. Introduction The policies seek to: Consolidate application/services and turn unused servers off. Relocate application/services to efficient servers The strategies are ranked through their Energy KPIs
  • 5. Single allocation Introduction Find the most energy efficient and suitable resource for a new Workload. Global optimization Rearrange the resources in a way that saves maximum amount of energy or carbon emission. Page 5
  • 6. Requirements • Flexibility, extensibility • Deep exploration of the search space Abstracting out the constraints
  • 8. SLA CONSTRAINTS SLA constraints flow
  • 9. SLA CONSTRAINTS SLA constraints examples Category Constraint Approach LoC Hardware HDD Choco + ext. Entropy 121+(25) CPUCores Entropy (‘fence’) 0+(25) CPUFreq Entropy (‘fence’) 0+(25) RAM Choco + ext. Entropy 123+(25) GPUCores Entropy (‘fence’) 0+(25) GPUFreq Entropy (‘fence’) 0+(47) RAIDLevel Entropy (‘fence’) 0+(47) QoS MaxCPULoad Choco + ext. Entropy 90+(25) MaxVLoadPerCore Choco + ext. Entropy 109+(25) MaxVCPUPerCore Choco + ext. Entropy 124+(25) Bandwidth Entropy (‘fence’) 0+(49) MaxVMperServer Entropy (‘capacity’) 0+(25) Availability PlannedOutages Choco + ext. Entropy Future Work Availability Choco + ext. Entropy Future Work Additional Metrics Dedicated Server Entropy (‘capacity’) 0 + (25) Access Entropy (‘fence’) 0 + (25)
  • 10. POWER OBJECTIVE MODEL Total Reconf. Energy Total Instant. Power * Reconf Time Energy Migrations Energy On/Off Power Servers Idle Power VMs Power Network Power Calculator
  • 11. HEURISTICS Root node: no VM is allocated First level node: VM1 allocated on S1 First level node: VM2 allocated on S1 At each level: call F4G branching heuristic. If a constraint is broken, backtrack to go up. First level node: VMx allocated on Sy Leaf node: all VMs are allocated Leaf node: all VMsL eaaref naollodcea: t ed all VMsL eaaref naollodcea: t ed all VMs are allocated At leaf level: note down the solution and the energy saved, then backtrack to find a better solution. First level node: VMxF iarsllot cleavteeld n oond eS:y VMx allocated on Sy
  • 12. Heuristics Call the F4G VM selector Select VM on the least energy efficient server and least loaded server VM selected Call the F4G Server selector Select Server which is the most energy efficient server and most loaded server Server selected Call the F4G Server selector Select Server which is empty and the least energy efficient server Server selected Composable heuristics • Candidate VM for migration • Target server for migration • Candidate Server for extinction
  • 14. Experiments on Cloud Testbed Lab trial ressources Blade Enclosure 1 Blade Enclosure 2 Node Controller Node Controller Node Controller Node Controller Node Controller Node Controller Node Controller Cluster Controller Power and Monitoring Collector Cluster Controller Cloud Controller Task scheduler FIT4Green VMs Enclosure 1 Enclosure 2 Processor model Intel Xeon E5520 Intel Xeon E5540 CPU frequency 2.27GHz 2.53GHz Cpu& Cores Dual cpu – Quad core Dual cpu – Quad core RAM 24 GB 24GB • DC1: 4 BL 460c blades using VMWare ESX v4.0 native hypervisor, 3 blades for Cluster and Cloud Control • DC2: 3 BL460c blades using VMWare ESX v4.0 native hypervisor, 2 blades for Cluster Control and Power and Monitoring System.
  • 15. Experiments on Cloud Testbed Lab trial Workload Number of active VMs Time Total number of active virtual machines during full week of work Active SLAs constraints: • Max vCPU per core = 2 • Min VM Slot = 3 • Max VM Slot = 6
  • 16. Experiments on Cloud Testbed Final test results for the various configurations Configuration Data Centre 1 Data Centre 2 Energy for Federation W i t h o u t FIT4Green 6350 Wh 4701 Wh 11051 Wh W i t h F I T 4 G r e e n S t a t i c Allocation 5190 Wh 4009 Wh 9199 Wh Saving 16.7% W i t h F I T 4 G r e e n D y n a m i c Allocation 5068 Wh 3933 Wh 9001 Wh Saving 18.5% W i t h F I T 4 G r e e n O p t i m i z e d Policies 4860 Wh 3785 Wh 8645 Wh Saving 21.7%
  • 17. Scalability Evaluation # Configuration Placement constraints activated 1 1 datacenter none 2 1 datacenter with overbooking factor=2 “MaxVCPUPerCore” constraint set on each server 3 2 federated datacenters “Fence” constraint set on each VM
  • 18. CONCLUSION & FUTURE WORK ü Energy aware resource allocation in datacenters ü Flexibility & extensibility ü Saves up to 18% in HP experiment ü Scalability with parallel processing ü Future work: ü SLA re-negotiation ü Green SLAs