Energy-efficient Virtual Machine Provision Algorithms for Cloud System Ching-Chi Lin Institute of Information Science, Academia Sinica Department of Computer Science and Information Engineering, Nation Taiwan University Pangfeng Liu Department of Computer Science and Information Engineering, Nation Taiwan University Graduate Institute of Networking and Multimedia, Nation Taiwan University Jan-Jan Wu Institute of Information Science, Academia Sinica

Introduction Datacenters consume tremendous amount of energy. To reduce the power consumption in a datacenter ◦ Virtual machine consolidation. ◦ Reduce the number of power-on idle servers.

VM consolidation Definition: ◦ Virtual machines with different hardware requirements must be deployed using the minimum number of physical servers. VM consolidation is an NP-hard problem. ◦ Can be reduced by bin-packing problem.

Heuristic Solutions for VM Consolidation First-Fit ◦ Deploy VMs to the fist server that can accommodates this VM. ◦ Load imbalance for power-on servers. Round-Robin ◦ Deploy VM to the servers in circular order.  Balancing the load between servers. ◦ Hard for physical servers to become idle.

Dynamic Round-Robin Minimize the number of physical server used to run all virtual machines. Extension from Round-Robin Add two rules to reduce the number of physical server in use.

Rule 1 Physical server become “retiring”, i.e., not accepting new VMs, after one of its hosting VM finishes. ABC DE G F HH

Rule 2 If a retiring server stay active for too long, migrates its hosting VMs and power off this server. ◦ Retirement threshold: the waiting time before migrating hosting VMs. AC DE G F

A Hybrid Method Hybrid method chooses from these two strategies depending on incoming traffic. ◦ During rush hours, Dynamic Round-Robin may not perform better than First-Fit.  Rush hour: a period of time with high VM incoming rate. ◦ Rush hour, use First-Fit. ◦ Otherwise, use Dynamic Round-Robin.

System Models Power consumption model ◦ The power consumption of a physical server is a linear function of its CPU load.[1][2] ◦ The idle power is about 50% of the peak power.[1][2] Migration model ◦ Live migration [1]:Xiaobo Fan, Wolf-Dietrich Weber, and Luiz Andre Barroso. Power provisioning for a warehouse- sized computer. [2]:Gong Chen, Wenbo He, Jie Liu, Suman Nath, Leonidas Rigas, Lin Xiao, and Feng Zhao. Energy-aware server provisioning and load dispatching for connection-intensive internet services.

Verification Number of threads(cores )

Power Model The assumption of linear power model with 50% idle power is reasonable. The power consumption P of a physical server:  α : the percentage of idle power versus the peak power.  c: the total number of cores required. c ≦ C  C: the total number of cores on a physical machine.  c/C: the load of a physical machine.  P p : peak power.

Live Migration Move a running VM to a different physical server without interrupting the VM. ◦ Does not lengthen the execution time of a VM. ◦ But slightly increase power consumption of source and destination server.  An extra load caused by migration increases about 20% load to the destination server.

Migration Model Energy consumption on migrating VM:  t M : time migration takes.  L: extra load caused by the migration process.  E s, E d : energy consumption on source and destination physical machine.  c/C, c’/C: the load of physical machines. c, c’ ≦ C  α : the percentage of idle power versus the peak power.  P p : peak power.

Experimental Setting Physical server: ◦ Intel(R) Xeon(R) E5620 CPUs 2.40GHz, each CPU has four cores. ◦ 24GB memory. Virtual machine: ◦ Small: 1v-core, 1.7 GB of memory. ◦ Large: 2 v-cores, 7.5 GB of memory. ◦ Extra large: 4 v-cores, 15 GB of memory.

Experimental Setting(Cont.) Test cases ◦ 2,000 virtual machines. ◦ Arrival time follows normal distribution, ranging from 0:00 to 23:59, to simulate the workload of a day.  Rush hour: 10:00~14:00 ◦ Execution time is generated from uniform distribution, ranging from 2 to 12 hours.

Experiment 1 – Different Retirement Threshold Retirement threshold ◦ Time as threshold:0, 5, 10, 30 minutes ◦ Physical server load as threshold: ≦ 50% Baseline: First-Fit

Result 1 Using physical server load as threshold consume the least power.

Experiment 2 – Performance Comparison Dynamic Round-Robin ◦ Use physical server load as retirement threshold. Baseline: First-Fit

Result 2 The Hybrid method consume about 3% less average power than First-Fit.

Experiment 3 – Comparison with Eucalyptus Eucalyptus strategies ◦ Greedy & Round-Robin  Does not power off idle physical servers. ◦ Power-Save  Power off idle physical servers. Baseline: Eucalyptus Round-Robin

Result 3 Dynamic Round-Robin and Hybrid consume about 40% lesser compare to Eucalyptus default strategy.

Conclusion We propose Dynamic Round-Robin and Hybrid, to deploy the virtual machines for power saving purpose. Both experiment and simulation results show that Dynamic Round-Robin and Hybrid we propose can reduce the average power consumption compared to other deployment algorithms with power- saving.

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