1. Power-Aware Scheduling of Virtual Machines in DVFS-enabled Clusters
Gregor von Laszewski1, Lizhe Wang2, Andrew J. Younge2, Xi He2
1: Pervasive Technology Institute
Indiana University
Bloomington, IN
2: Service Oriented Cyberinfrastructure Laboratory
Rochester Institute of Technology
Rochester, NY
Let’s start.
Good afternoon, everyone. My name is Andrew Younge. Today I am going to talk about power-aware scheduling of virtual machines in DVFS-enabled clusters 1

2. Outline Introduction Virtual Machines Power-Aware Computing
System Model
Scheduling Algorithm
Implementation
Simulation
Conclusion

3. Power Consumption in Data Centers
Currently it is estimated that servers consume 0.5% of the world’s total electricity usage.
Approx. 1.2% when data center systems are factored in
Server energy demand doubles every 4-6 years.
This results in large amounts of CO2 produced by burning fossil fuels.
What if we could reduce the energy used with a minimal performance impact?

4. Virtualization
A Virtual Machine (VM) is a software artifact that executes other software as if it was running on a physical resource directly.
Typically uses a Hypervisor or VMM which abstracts the hardware from an Operating System
Enables multiple OSs to run simultaneously on one physical machine.

5. Virtualization in a Cluster
A compute cluster provides various virtual machine templates.
When a job arrives at the cluster, the cluster scheduler allocates the job with a pre-configured virtual machine then starts it on compute nodes.
The job is executed in the virtual machine.
After the job is completed, the virtual machine is shutdown.

6. Advantages of Virtualization
On-demand creation and customization
Enhanced system usage
Legacy support for specific software
Increased administrative control for users
Cluster site autonomy
Guaranteed quality of service

7. Power-Aware Clusters
A Power-aware cluster is a compute cluster where nodes support multiple power and performance modes.
Adjustable frequency, voltage, sleep state, etc
Lower the power consumption of the CPU when its not needed
We can Dynamic Voltage and Frequency Scaling (DVFS) methods to control processor power consumption
Intel SpeedStep Technology
AMD PowerNow

8. Energy Model Power consumption can be estimated as: Was
Where v is the processor voltage and s is the operating frequency at time period t

9. Cluster Model
A Processing Element is defined as: Where op is the operating point, v is the voltage, and s is the processor speed. Then we can define a cluster C as a collection of k PEs

10. VM Model A VM is defined as:
Where s is the speed with requirement r and t is the time, both as the execution time and the startup time required.
Then we can define a set of i VMs as:

11. Research Issue
We need a function f That schedules VMs to PEs at a certain operating point in such a way that minimizes the overall power consumption of the cluster C

12. Main Idea

13. Algorithm 1
Set all PEs running to the lowest voltage and processor speed, smin
Define pek.sa as the available processor speed if the processor does not change its operating point. Since no virtual machine are initially scheduled, pek .sa is initialized with smin
pek.∇s is the available PE speed when pek is operated to a highest level voltage from the current voltage level. pek.∇s is initialized with smax
At a predefined interval, reduce power profiles with Algorithm 3 and schedule all incoming VMs with Algorithm 2
We use an interval because it simplifies the model and works well for small to medium size workloads.

14. Algorithm 2
Sort the incoming virtual machine requests in decreasing order of required processing frequency, vmi .sr so the VMs with higher requirements are scheduled first
Find a pen with the most available processor speed
If this PE meets the needs of the VM, schedule it on pen
Continue for all VMs to be scheduled.

15. Algorithm 2 (cont)
If vmi cannot be scheduled, take the pen with the maximum potential speed, we raise the speed the lowest possible level to satisfy the requirements of vmi
Schedule vmi on pen

16. Algorithm 3 During the interval, a VM may finish execution
If it does, try to lower the operating point of pen to the lowest possible point which meets the requirements of all currently running VMs on pen

17. Validation Need to validate the potential of our algorithm
Implement the algorithm within a small VM cluster
Simulate VMs scheduled on a cluster with the algorithm

18. OpenNebula
OpenNebula is a virtual infrastructure manager for data centers
The standard scheduler is independent of the other components
We can replace this scheduler with our own Power-aware scheduling algorithm
Then adjust the frequency at the Hypervisor level
Default schedule = rank based policy, which allocates compute resources for virtual machines based on resource rank

19. Xen Power Management Xen Hypervisor to change the power consumption
Use the Xen unstable power management tools
Within Dom0, we can control each core’s operating frequency using xenpm
Intel Nehalem Quad Core processors
Can set frequency to 1.6Hz, 1.86Ghz, 2.13GHz, 2.53GHz, or 2.66Ghz
Uses Hyperthreading which provides 8 virtual cores, each of which can run a VM instance
Measure power consumption using a Watts-UP power meter
The stable power management functions were broken, had to use the nightly build. Xen has since been released which has full support.

20. Power Consumption Variations
We can immediately see the impact of frequency scaling on power consumption.
Also shows that the difference in power consumption between 2 VMs and 8 VMs is minimal, therefore the power consumption savings is considerable. It only cost 11.5% more to use all 8 vcores vs 4

21. Performance Impact of VMs
Image 2: Slight impact of scheduling 8 VMs instead of 4, but overall performance is still greater 4*93=372 vs 8*60= % more processing power

22. DVFS Simulation
Simulate a test cluster with 10, 20, 30, 40, and 50 compute nodes
Each node was simulated as a Pentium M at 1.4ghz
Simulate 100, 200, 300, 400, and 500 virtual machines deployed on the cluster
VMs randomly pick frequency requirements in .100Mhz intervals
Use the DVFS scheduling algorithm to schedule VMs on nodes

23. DVFS-enabled Cluster Scheduling

24. Overall operating point distribution
Simulate scheduling 200 VMs on 40 PEs
In round 1, most PEs run at the lowest voltage
Over subsequent rounds, the operating points scale up as the utilization rises
The overall distribution varies widely, with the majority of the time running below the maximum operating frequency

25. Conclusion
Recent trend to consolidate resources into a virtualized data center.
There is a need to address the power consumption in these data centers.
We can use DVFS technologies when scheduling VMs to conserve power whenever possible.
Both experimental and analytical results show our algorithm its possible to reduce power consumption within a cluster environment.
Further research is needed to reduce the energy consumed in data centers

26. Questions?

27. Appendix

28. Cloud Computing
Distributed Systems encompasses a wide variety of technologies
Grid computing spans most areas and is becoming more mature.
Clouds are an emerging technology, providing many of the same features as Grids without many of the potential pitfalls.
From “Cloud Computing and Grid Computing 360-Degree Compared”

29. Framework Green Cloud Framework Virtual Machine Controls Scheduling
Power Aware
Thermal Aware
Management
VM Image Design
Migration
Dynamic Shutdown
Data Center Design
Server & Rack Placement
Air Cond. & Recirculation

30. VM scheduling on Multi-core Systems
There is a nonlinear relationship between the number of processes used and power consumption
We can schedule VMs to take advantage of this relationship in order to conserve power
Mension core i7 system
Describe in more details, illustrating how I’ve created it
Power consumption curve on an Intel Core i7 920 Server
(4 cores, 8 virtual cores with Hyperthreading)
Scheduling

31. 485 Watts vs. 552 Watts VM VM VM VM VM VM VM VM Node 1 @ 170W
Assume we have 4 nodes with the same power consumption curve described in slide 10
My Algorithm 1 compared to a classic Round Robin scheduling algorithm (used in Eucalytus and OpenNebula) VM VM
Node 138W
Node 138W VM VM VM VM
Node 138W
Node 138W

32. Minimal VM Image
Ubuntu Linux
Easier to slim down a fully functional distro than to create one from scratch
Selected Ubuntu Linux
Jaunty 9.04
Minimal install site
Package management software (aptitude)
Continuing support
FIX THIS SLIDE!
Vs.
Cloud Ubuntu
VM Image Design