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
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FIT4Green seeks energy saving policies for DCs, enhancing the effects inside a federation by an aggressive strategy for reducing the energy consumption in ICT We aim at reducing cost for companies è Strengthening competitive position FIT4Green needs to be DC framework agnostic: § Demonstrated in Cloud computing, Traditional computing, Super computing and Networking
Table of Contents
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Introduction
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Requirements
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Framework design
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SLA Constraints
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Power Objective Model
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Heuristics
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Experiments on Cloud Test-bed
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Scalability Evaluation
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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
Introduction
Single allocation 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.
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Requirements
• Flexibility, extensibility • Deep exploration of the search space Abstracting out the constraints
• 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 6350 Wh FIT4Green
4701 Wh
11051 Wh
W i t h 5190 Wh FIT4Green S t a t i c Allocation W i t h 5068 Wh FIT4Green D y n a m i c Allocation
4009 Wh
9199 Wh Saving 16.7%
3933 Wh
9001 Wh Saving 18.5%
W i t h 4860 Wh FIT4Green Optimized Policies
3785 Wh
8645 Wh Saving 21.7%
Scalability Evaluation
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Configuration 1 datacenter
Placement constraints activated none
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1 datacenter with overbooking factor=2 2 federated datacenters
“MaxVCPUPerCore” constraint set on each server “Fence” constraint set on each VM
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CONCLUSION & FUTURE WORK
Energy aware resource allocation in datacenters ü Flexibility & extensibility ü Saves up to 18% in HP experiment ü Scalability with parallel processing ü
An Energy Aware Framework for Virtual Machine Placement in Cloud ...
Authors: Corentin Dupont (Create-Net); Giovanni Giuliani (HP Italy);. Fabien Hermenier (INRIA); Thomas Schulze (Uni Mannheim); Andrey. Somov (Create-Net). An Energy Aware Framework for Virtual. Machine Placement in Cloud Federated. Data Centres. Corentin Dupont ...
Energy Efficient Virtual Machine Allocation in the Cloud. An Analysis of Cloud Allocation Policies. Ryan Jansen. University of Notre Dame. Center for Research ...
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