Carbon-Efficient Virtual Machine Placement Based on Dynamic Voltage Frequency Scaling in Geo-Distributed Cloud Data Centers
Abstract
:1. Introduction
2. Related Works
- Selection of data centers and clusters is performed based on the PUE and CO2 emission rate, aiming to reduce the overall carbon footprints of the data centers;
- Load balancing is done by identifying a feasible server with a minimal operating frequency for the current workload with the required quality of service, aiming to reduce hot spots in CPU heat dissipation, which have a direct impact on hardware lifetime and performance;
- The impacts of static and dynamic power usage effectiveness (PUE) on placement decisions are analyzed, along with cooling load power impacts.
3. System Overview
3.1. Power Model
3.2. System Model
- Resource Management System: The resource management system contains information about the cluster list, PUE, CFR, total utility power, current IT load, and other metadata information related to the data centers.
- Management Node (MN): The Resource Allocation Management (RAM) algorithm is a daemon that is executed in the management node. It is updated with the cluster list, host list, PUE, carbon footprint rate, and other information related to the clusters in the data center. It activates the scheduling algorithm for VM-to-PM mapping and the resource deallocation algorithm to perform resource recovery, and updates target virtual machine queue (TargetVMQ) with VM-to-PM mapping information.
- Cluster Manager (CM): A node in the cluster is nominated as the head node to function as the cluster manager. The overall utilization of the cluster, number of machines in on and off states, maximum and minimum utilization, number of VMs operating in the cluster, and power consumption are maintained by the cluster manager and updated by the management node.
- Physical Machine Manager (PMM): The PM details related to available memory and CPU capacity, current operating frequency, power consumption, percentage of CPU utilization, number of active VMs, and other PM-related information are maintained by the PMM and updated by the CM in the head node.
- Virtual Machine Manager (VMM): The VMM is a daemon that is executed in each PM. It is responsible for maintaining the VMs executing in PM. VM resource utilization, percentage of CPU time utilized, submission time, placement time, active and idle states, remaining execution time, power consumption, and other VM details are maintained by the VMM.
4. Problem Formulation
4.1. Objective Function
4.1.1. Server Power
4.1.2. Cooling Power
5. Evaluated Algorithms
5.1. RAMAlgorithm
Algorithm 1: High-level overview of the algorithm approach |
Input: Hostlist, VM instancelist |
Output: TargetVMQ |
1 for interval do |
2 ReqQ←Get VMs from VM instance list; |
3 HostQ←Get Hosts from HostList; |
4 TargetVMQ←Call placement algorithm (presented in Section 5.2, Section 5.3, Section 5.4, Section 5.5 and Section 5.6); |
5 if interval >min-exe-time then |
6 Completedlist←Get VMs with active time completion from TargetVMQ; |
7 for completedlistdo |
8 Deallocate resources associated with the VM; |
9 Endfor |
10 Endif |
11 Endfor |
12 Return Target VMQ. |
5.2. Carbon- and Power-Efficient Optimal Frequency VM Placement (C-PEF)
Algorithm 2: CPEF Carbon and Power-Efficient Optimal Frequency VM Placement |
|
5.3. Carbon-Aware First-Fit Optimal Frequency VM Placement (C-FFF)
5.4. Carbon- and Power-Efficient VM Placement (C-PE)
5.5. Carbon-Aware First-Fit Least-Empty VM Placement (C-FFLE)
Algorithm 3: C-FFF Carbon-Aware First-Fit Least-Empty VM Placement |
|
5.6. Carbon-Aware First-Fit VM Placement (C-FF)
6. Experimental Environment and Assumptions
Physical Machine and VM Reservation Modeling
7. Results and Discussions
- Reduction in Overall Carbon Footprint
- The ratio of VM acceptance (RVA)
7.1. Scenario-I: Energy-Efficient Mapping of VMs to PMs with StaticPUE
7.2. Scenario-II: Energy-Efficient Mapping of VMs to PMs with DynamicPUE
8. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Machines | Frequency(GHz) | No. of Cores | Power Model | Memory(GB) | Storage (GB) | Network Bandwidth (Mbps) |
---|---|---|---|---|---|---|
M1 | 2 | 2 | 1 | 16 | 2000 | 1000 |
M2 | 4 | 4 | 1 | 32 | 6000 | 1000 |
M3 | 4 | 8 | 2 | 32 | 7000 | 2000 |
M4 | 8 | 8 | 2 | 64 | 7000 | 4000 |
M5 | 16 | 8 | 2 | 128 | 9000 | 4000 |
Power Model | Idle | 10% | 20% | 30% | 40% | 50% | 60% | 70% | 80% | 90% | 100% |
---|---|---|---|---|---|---|---|---|---|---|---|
1 | 60 | 63 | 66.8 | 71.3 | 76.8 | 83.2 | 90.7 | 100 | 111.5 | 125.4 | 140.7 |
2 | 41.6 | 46.7 | 52.3 | 57.9 | 65.4 | 73 | 80.7 | 89.5 | 99.6 | 105 | 113 |
Name | ECU | Core Speed(GHz) | Memory(MB) | Storage(GB) | Network Band Width(Mbps) | Probability |
---|---|---|---|---|---|---|
M1.small | 1 | 1 | 1740 | 160 | 500 | 0.25-BT |
M1.large | 2 | 4 | 7680 | 850 | 500 | 0.25-BT/0.12-WR |
M1.xlarge | 4 | 8 | 15,360 | 1000 | 1000 | 0.08-WR |
M2xlarge | 2 | 6.5 | 17,510 | 1000 | 1000 | 0.12-WR |
M22.xlarge | 4 | 13 | 35,020 | 1000 | 1000 | 0.08-WR |
C1.median | 2 | 5 | 1740 | 500 | 500 | 0.1-BT |
Data Center | Carbon Footprint Rate (CFR) in Tons/MWh | PUE |
---|---|---|
DC1 | 0.124, 0.147 | 1.56 |
DC2 | 0.350, 0.658 | 1.7 |
DC3 | 0.466, 0.782 | 1.9 |
DC4 | 0.678, 0.730 | 2.1 |
Interval (300 s) | Active VMs | Power Consumption (kW) | Total Active PMs | Total CPU Utilization % | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
C-PEF | C-PE | C-FFF | C-FFLE | C-FF | C-PEF | C-PE | C-FFF | C-FFLE | C-FF | |||
1 | 64 | 1512.53 | 1436.84 | 1626.45 | 2070.33 | 1954.24 | 50 | 36 | 56 | 60 | 56 | 10.94 |
2 | 123 | 2596.98 | 2624.79 | 2879.36 | 3624.09 | 3185.06 | 89 | 74 | 98 | 106 | 91 | 20.90 |
3 | 181 | 3646.54 | 4106.17 | 4003.47 | 5360.29 | 4935.64 | 111 | 116 | 123 | 151 | 136 | 29.34 |
4 | 237 | 4997.41 | 5505.73 | 5272.04 | 6951.47 | 6278.71 | 141 | 152 | 153 | 196 | 172 | 38.46 |
5 | 288 | 6228.52 | 6717.20 | 6270.41 | 8246.13 | 7189.17 | 175 | 186 | 175 | 231 | 199 | 47.02 |
6 | 334 | 7056.55 | 7453.36 | 7207.61 | 9257.91 | 8179.25 | 198 | 208 | 203 | 258 | 227 | 54.36 |
7 | 381 | 8010.12 | 8428.66 | 8295.90 | 10295.59 | 9043.74 | 227 | 238 | 238 | 289 | 252 | 63.09 |
8 | 420 | 8770.99 | 9264.71 | 8951.71 | 11077.27 | 9856.81 | 253 | 262 | 258 | 312 | 276 | 69.76 |
Reservation Interval | Minimum CPU Utilization % | Number of Hosts with 100% CPU Utilization | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
C-PEF | C-PE | C-FFF | C-FFLE | C-FF | C-PEF | C-PE | C-FFF | C-FFLE | C-FF | |
1 | 40.625 | 50 | 25 | 50 | 50 | 17 | 23 | 17 | 35 | 33 |
2 | 31.25 | 62.5 | 25 | 50 | 62.5 | 19 | 42 | 27 | 62 | 56 |
3 | 40.625 | 50 | 40.625 | 62.5 | 62.5 | 56 | 75 | 64 | 96 | 91 |
4 | 62.5 | 62.5 | 40.625 | 50 | 62.5 | 91 | 101 | 94 | 127 | 118 |
5 | 62.5 | 50 | 50 | 50 | 62.5 | 113 | 121 | 117 | 152 | 138 |
6 | 50 | 40.625 | 40.625 | 62.5 | 62.5 | 125 | 146 | 135 | 173 | 159 |
7 | 62.5 | 62.5 | 40.625 | 50 | 40.625 | 142 | 162 | 153 | 188 | 172 |
8 | 31.25 | 62.5 | 31.25 | 40.625 | 62.5 | 156 | 176 | 164 | 204 | 189 |
Placement Algorithm | Power (kW) | Carbon Footprint(Tons) | Number of VMs Placed |
---|---|---|---|
C-FFF | 676,296.2775 | 48.72382575 | 1634 |
C-PE | 691,256.2894 | 49.87335009 | 1611 |
C-PEF | 665,341.0031 | 48.28496006 | 1623 |
C-FFLE | 782,225.6419 | 59.79732901 | 1622 |
C-FF | 695,623.92 | 50.4902789 | 1598 |
RVA% Under Different VM Requests | ||||||
---|---|---|---|---|---|---|
Algorithm | 481 | 910 | 1276 | 1591 | 1861 | 2000 |
C-FFF | 100 | 88.68132 | 81.5047 | 81.58391 | 81.30038 | 81.7909 |
C-PE | 100 | 87.03297 | 80.17241 | 80.32684 | 80.06448 | 80.5903 |
C-PEF | 100 | 88.35165 | 81.03448 | 81.01823 | 80.65556 | 81.14057 |
C-FFLE | 100 | 87.25275 | 80.721 | 81.26964 | 80.60183 | 81.14057 |
C-FF | 100 | 87.03297 | 80.01567 | 80.01257 | 79.36593 | 79.93997 |
C-FFF | C-PE | C-PEF | C-FFLE | C-FF | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Metric | % CPU Utilization | Power (kW) | % CPU Utilization | Power (kW) | % CPU Utilization | Power (kW) | % CPU Utilization | Power (kW) | % CPU Utilization | Power (kW) | |
Min | 0.1471 | 1626 | 0.1471 | 1437 | 0.1471 | 1513 | 0.1471 | 2070 | 0.1471 | 1954 | |
Max | 89.12 | 6.76 × 105 | 90.04 | 6.91 × 105 5 | 88.38 | 6.65 × 105 | 92.46 | 7.82 × 105 | 89.87 | 6.96 × 105 | |
Mean | 48.6 | 4.49 × 105 | 48.91 | 4.58 × 105 | 47.82 | 4.42 × 105 | 50.46 | 5.17 × 105 | 48.97 | 4.64 × 105 |
Placement Algorithm | Power(kW) | Carbon Footprint (Tons) | Number of VMs Placed |
---|---|---|---|
C-PEF | 564,350.9 | 23.631227 | 1617 |
C-PE | 628,328.4 | 24.191322 | 1636 |
C-FFF | 582,335.1 | 24.35445 | 1641 |
RVA% with Different VM Requests | ||||||
---|---|---|---|---|---|---|
Algorithm | 481 | 910 | 1276 | 1591 | 1861 | 2000 |
C-PEF | 98.96 | 87.14 | 81.42 | 80.95 | 80.65 | 80.89 |
C-PE | 100 | 88.4 | 81.97 | 82.08 | 81.67 | 82.09 |
C-FFF | 99.37 | 88.69 | 82.66 | 83.58 | 82.3 | 82.84 |
C-FFF | C-PE | C-PEF | ||||
---|---|---|---|---|---|---|
Metric | %CPU Utilization | Power (kW) | %CPU Utilization | Power (kW) | %CPU Utilization | Power (kW) |
Min | 0.1471 | 1029 | 0.1471 | 644.5 | 0.1471 | 969 |
Max | 88.94 | 5.823 × 105 | 89.67 | 6.283 × 105 | 86.69 | 5.644 × 105 |
Mean | 48.33 | 3.854 × 105 | 48.48 | 4.156 × 105 | 46.25 | 3.74 × 105 |
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Renugadevi, T.; Geetha, K.; Prabaharan, N.; Siano, P. Carbon-Efficient Virtual Machine Placement Based on Dynamic Voltage Frequency Scaling in Geo-Distributed Cloud Data Centers. Appl. Sci. 2020, 10, 2701. https://doi.org/10.3390/app10082701
Renugadevi T, Geetha K, Prabaharan N, Siano P. Carbon-Efficient Virtual Machine Placement Based on Dynamic Voltage Frequency Scaling in Geo-Distributed Cloud Data Centers. Applied Sciences. 2020; 10(8):2701. https://doi.org/10.3390/app10082701
Chicago/Turabian StyleRenugadevi, T., K. Geetha, Natarajan Prabaharan, and Pierluigi Siano. 2020. "Carbon-Efficient Virtual Machine Placement Based on Dynamic Voltage Frequency Scaling in Geo-Distributed Cloud Data Centers" Applied Sciences 10, no. 8: 2701. https://doi.org/10.3390/app10082701