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

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

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?

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.

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.

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

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

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

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

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:

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

Main Idea

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.

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.

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

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

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

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

Xen Power Management Xen Hypervisor to change the power consumption Use the Xen 3.3.4-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 3.3.1 power management functions were broken, had to use the nightly build. Xen 3.4.1 has since been released which has full support.

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

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=480 22.5% more processing power

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

DVFS-enabled Cluster Scheduling

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

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

Questions?

Appendix

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”

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

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

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 1 @ 138W Node 2 @ 138W VM VM VM VM Node 3 @ 138W Node 4 @ 138W

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