Power-Aware Scheduling of Virtual Machines in DVFS-enabled Clusters

Transcription

1 Power-Aware Scheduling of Virtual Machines in DVFS-enabled Clusters Gregor von Laszewski, Lizhe Wang, Andrew J. Younge, Xi He Service Oriented Cyberinfrastructure Lab Rochester Institute of Technology, Rochester, NY IEEE Cluster 2009

2 Outline Introduction System Model The Scheduling Algorithm Implementation Simulation Conclusion and Future Work 2

3 Introduction (1/2) Today s high performance computers consume tremendous amounts of energy. And every 10 increase of temperature leads to a doubling of the system failure rate. Dynamic voltage and frequency scaling (DVFS) is an efficient technology to control the process power consumption. Processors can be operated in several frequencies with different supply voltage. Intel SpeedStep AMD PowerNow! 3

4 Introduction (2/2) Virtual machine technology is adopted for high end computing to achieve efficient computing resource usage. In this paper, we focus on implementing a power-aware scheduling algorithm where VMs are dynamically provided for executing jobs. This algorithm is to minimize the processor power dissipating by scaling down processor frequencies without drastically increasing the overall VM execution time. 4

5 System Model (1/5) Performance Model (1/2) An Operating Point is defined as: op j = v op, s op We can define a set of j operating points as: OP = 1 j J op j Supply Voltage (V) Frequency (GHz)

6 System Model (2/5) Performance Model (2/2) E = E dynamic + E static P dynamic = ACv 2 s E dynamic = P dynamic Δt E E dynamic E v 2 s Δt t t

7 Cluster Model System Model (3/5) A Process Element is defined as: pe k = op pe, v pe, s pe We can define a cluster C as a collection of k PEs: C = pe k 1 k K 7

8 VM Model A VM is defined as: System Model (4/5) vm i = s r, t, t r We can define a set of i VMs as: VM = vm i 1 i I 8

9 System Model (5/5) Virtual Machine Mapping We need a function f, which maps VM to certain PE that operated in certain operating point: f: vm i pe k. s pe, pe k. v pe, vm i VM, pe k C 9

10 The Scheduling Algorithm (1/6) 10

11 The Scheduling Algorithm (2/6) The rules of algorithm: 1. Minimize the process supply voltage by scaling down the processor frequency. 2. Schedule VMs to PEs with low voltage and try not to scale PE to high voltages. 11

12 The Scheduling Algorithm (3/6) Set all PEs running to the lowest voltage and processor speed, s min. Define pe k. s a as the available processor speed if the processor does not change its operating point. Since no virtual machine are initially scheduled, pe k. s a is initialized with s min. pe k. s is the available PE speed when pe k is operated to a highest level voltage from the current voltage level. pe k. s is initialized with s max. At a predefined interval, reduce power profiles with Algorithm 3 and schedule all incoming VMs with Algorithm 2. Supply Voltage (V) Frequency (GHz)

13 The Scheduling Algorithm (4/6) Sort the incoming virtual machine requests in decreasing order of required processing frequency, vm i. s r so the VMs with higher requirements are scheduled first. Find a pe n with the most available processor speed. If this PE meets the needs of the VM, schedule it on pe n. Continue for all VMs to be scheduled. 13

14 The Scheduling Algorithm (5/6) If vm i cannot be scheduled, take the pe n with the maximum potential speed, we raise the speed the lowest possible level to satisfy the requirements of vm i. Schedule vm i on pe n. 14

15 The Scheduling Algorithm (6/6) During the interval, a VM may finish execution. If it does, try to lower the operating point of pe n to the lowest possible point which meets the requirements of all currently running VMs on pe n. 15

16 Environment Head node Ubuntu 8.10 OpenNebula 1.2 Intel Pentium 4 CPU Compute node Implementation Ubuntu Server 8.10 Xen unstable nbench Benchmark Tool Intel Core i7-920 (Nehalem) Quad-core Processor Frequency: 1.6GHz, 1.86GHz, 2.13GHz, 2.53GHz, 2.66GHz With Hyper-Threading (4C8T) Measure power consumption using a Watts-Up power meter. 16

17 Power Consumption Variations

18 Performance Impact of VMs 18

19 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 19

20 DVFS-enabled Cluster Scheduling Simulation Results

21 Observation Observation 1: The scheduling algorithm can reduce power consumption in a DVFS-enabled cluster. Observation 2: In case that the number of PEs is fixed, the power consumption increases as the number of incoming virtual machines increases. Observation 3: In case that the number of incoming virtual machine is fixed, the power consumption decreases as the number of PEs increases. 21

22 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. 22

23 Conclusion and Future Work The need to minimize wasted server energy becomes important. The field of Green computing provides a way to prevent unnecessary CO 2 emissions and save large amounts of money on operating costs. The proposed algorithm dynamically scaled the operating frequencies and voltages of the compute nodes in a cluster without degrading the VM performance beyond unacceptable levels. Both experimental and analytical results show our algorithm is possible to reduce power consumption efficiently within a DVFS-enabled cluster environment. Future work includes the analysis and measuring of VM migration costs in a cluster, and the development of a temperature-aware scheduling algorithm for multi-core clusters. 23