Reporter: 楊凱程 Advisor: 曾學文

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An Energy Efficient VM Management Scheme with Power Law Characters in Video Streaming Datacenters (VMPL)

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Introduction

Related Works about VM Placement

VM Management Scheme with Power Law Characters (VMPL)

Experiment results

Outline

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Power consumption in DCNs (1.5% in the world) Energy costs (Watts) double per 5 years[1] Computing systems and cooling systems Server: 70% and switch: 30% [2] (Don’t consider cooling systems) The idle power consumption of a server can be more than 50%[3]

Introduction

[1]C. Ghribi, M.Hadji, D. Zeghlache, “Energy Efficient VM Scheduling for Cloud Data Centers: Exact Allocation and Migration Algorithms” Proceedings of the 13th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) ,pp671-678,2013[2]D. Kliazovich, P. Bouvry, and S. U. Khan,” A Packet-level Simulator of Energy- aware Cloud Computing Data Centers,” Journal of Supercomputing, vol. 62, no. 3, pp. 1263-1283, 2012.[3]MASTELIC, Toni, et al. "Cloud Computing: Survey on Energy Efficiency." Journal ACM Computing Surveys (CSUR),47.2: 33,2015

Video traffic is increasing Internet traffic will grow double every 2.5 years (Cisco)[4] Video traffic will constitute approximately 90% of global Internet traffic(2015) [5] YouTube alone accounts for more than 35% of the Internet traffic.[6] Every day people watch hundreds of millions of hours on YouTube and generate billions of views[7] Need decoding/transmission CPU/BW bound works How to management videos in VDCNs are important

Virtualization is popular in VDCNs Youtube[4] and UUSee[13] use VMs to deal with video requests Virtualization enables data center more flexible VMs cause mainly and More power consumption in VDCNs

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Introduction

[4]Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update 2014–2019 White Paper (http://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/white_paper_c11-520862.html)[5]Jianhua Tang,Wee Peng Tay, Yonggang Wen, "Dynamic Request Redirection and Elastic Service Scaling in Cloud-Centric Media Networks" Proceedings of the Multimedia, IEEE Transactions on,pp.1434-1445,2014[6]Alessandro Finamore , Marco Mellia , Maurizio M. Munafò , Ruben Torres , Sanjay G. Rao, YouTube everywhere: impact of device and infrastructure synergies on user experience, Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference, November 02-04, 2011[7]http://www.youtube.com/yt/press/statistics.html

Servers create a new VM to handle requests of a new video[13] Popular videos (30%)

Large power law (>10K views) and long tail (>7days) Popular videos90% total traffic in VDCN[9][10] Easily cause traffic burst and waste of resources[8]

Unpopular videos (70%)

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Introduction

[8]Monia Ghobadi, Yuchung Cheng, Ankur Jain and Matt Mathis, “Trickle: rate limiting YouTube video streaming,” Proceedings of the 2012 USENIX conference on Annual Technical Conference, p.17-17, June 13-15, 2012, Boston, MA.[9]Meeyoung Cha, Haewoon Kwak, Pablo Rodriguez, Yong-Yeol Ahn and Sue Moon, “I tube, you tube, everybody tubes: analyzing the world's largest user generated content video system,” Proceedings of the 7th ACM SIGCOMM conference on Internet measurement, October 24-26, 2007, San Diego, California, USA.[10]L.Braun, A.Klein, G.Carle, H.Reiser, J.Eisl, ""Analyzing caching benefits for YouTube traffic in edge networks — A measurement-based evaluation",Proceedings of the IEEE Network Operations and Management Symposium (NOMS), pp.311-318,2012

80% 20%

about 7 days

Power-law Long tail

t

view

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Conventional VM placement schemes All new VMs are placement in the server with the most available resources

Hotpots with power-law in VDCN The resources requirement for a VM is variable Resource is not enough Migration Cost and more Power consumption

Introduction

VM

VM

VM

VM

Power-law

Server1 Server2

New VMVM VM

VM

VM

CrashServer1 Server2

Add 4new VM

𝑡 0 𝑡1

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Traditional VM placement schemes with power saving

VM placement schemes for video streaming DCNs

Related Works about VM Placement

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Traditional VM placement schemes with power saving Static workload

Best-fit decreasing[11] Not real-time for vary resource requirements of VMs

Dynamic workload More consider multi-dimension resources: CPU/BW/Memory Correlation coefficient for placement[12] Consolidation VM to achieve power saving Do not consider for video streaming DCNs

[11]D.S. Johnson, A. Demers, J.D. Ullman, M.R. Carey and R.L. Graham. “Worst case performance bounds for simple one-dimensional packing algorithms,” Proc. SIAM Journal on Coniputing 3, pp. 299-325,1974.[12] Zhibo Cao and Shoubin Dong “Dynamic VM consolidation for energy-aware and SLA violation reduction in cloud computing,” Proc. IEEE 2012 13th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT),pp.363-369,2012.

Related Works about VM Placement

VM

VM

VM

VM

Power-law

Server1 Server2

New VMVM VM

VM

VM

CrashServer1 Server2

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VM placement schemes for video streaming DCNs Use Nash bargaining to satisfy users’ need[13]

Maximum the resource utilization Placement resources based on multi-users’ interests More computation cost O()

Split video streaming[14] Increase QoS of a video Speed up transcoding by different VMs More merging cost

It’s not good for VDCNs with large amount of videos Don’t consider power law character of popular videos

[13] Yuan Feng, Baochun Li and Bo Li, "Bargaining Towards Maximized Resource Utilization in Video Streaming Datacenters", in IEEE Infocom, 2012.[14]F.Jokhio, A.Ashraf, S.Lafond, I.Porres and J.Lilius, "Prediction-based dynamic resource allocation for video transcoding in cloud computing," 21st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), pp. 254-261, 2013.

Related Works about VM Placement

VM Management Scheme with Power Law(VMPL) TAR forecast[15] PL placement Triple threshold migration

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View traffic

Classification

Popular videos Unpopular videos

Do not Forecast TAR forecast

Allocation and migration alg.

𝑆𝑒𝑟𝑣𝑒𝑟 1

=74%

VM

VM

VM

𝑆𝑒𝑟𝑣𝑒𝑟𝑚…

80%

= 20%

VMPL

[15]S.Casolari, M.Andreolini and M.Colajanni, “Runtime prediction models for Web-based system resources,” Proc. 2008 Modeling, Analysis and Simulation of Computers and Telecommunication Systems(MASCOTS), pp.1-8, 2008.

VMPL

Most of the traffic is concentrated in a pod[16] We have a VMM in a pod VMM manage all servers in the pod

80% 20%

about 7 days

Power law Long tail

1. Initialize: 2. Placement algorithm3. Record views duration a week4. Forecast algorithm5. If new videos :6. Placement algorithm7. Else:8. Migration algorithm

[16]Tarik. Taleb, and Alden. Ksentini, “Impact of emerging social media applications on mobile networks,” Proc. IEEE CC, pp.5934-5938, 2013.

概念 : 在符合 SLA 的要求下最小化能源消耗

=

Initialize:

Step1: 計算需要開多少實體機 N

Step2: 分散擺放 If :

Place in i++

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VMPL

Server1 Server2

𝑉𝑀𝑛𝑒𝑤

=2

𝑉𝑀𝑛𝑒𝑤

𝑉𝑀𝑛𝑒𝑤

𝑉𝑀𝑛𝑒𝑤

𝑉𝑀𝑛𝑒𝑤

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Step0: 預測熱門影片的流量 Step1: 將新影片加入列表 Step2: 將超過門檻的 VM 搬走 Step3:lock 熱門影片 Step4: 將低於門檻的 pm 加入 shutdownlist

Step5: 將和 server 可用空間由大到小排序，並從placementlist 中選出擁有最大的 Server 當作來源端，擁有最大的 Server 當作目的地端且依序擺放，利用 VM 搬移的策略達到該 Server 的資源最大利用率

Step6: 若可用空間不足則減少關閉的機器或開新的機器，並重複步驟 5

Step7: 關閉 shutdownlist 中的 pm

VMPL

20%10%

15%15%

60%70%

20%15%

15%10%20%10%

Server1

Max_thr

Min_thr

Placementlist: =16.91

30%20%

20%15%

Server2 Server3

15%15%

Shutdownlist: Server1

Lock shutdown

20%

15%

60%

20%

15%20%

30%

=16.9120%15% 15%15%

10%10%

Server0

shutdown

Server0

10%

thr pl=54.66𝐶𝑃𝑈𝑛𝑒𝑤=0.7×0.73+0.3×54.66=16.91

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1. Add to placelist #step1 將新影片加入列表2. If > or >: #step2 將超過門檻的 VM 搬3. Add VM to placelist4. If< or 5. Add pm to shutdownlist #step3 將低於門檻的 pm 加入 shutdownlist6. If > or>: #step4 lock 熱門影片7. 8. lock pm i9. Sort #step5 將由大到小排序10. Sort #step5 將 server 可用空間由大到小排序11. While len(placelist)>0{ #step5 依序擺放 :O(n) , traditional:O()12. If and ( :13. Place in 14. i++15. If 所有 pm 已滿 : #step6 若可用空間不足則減少關閉的機器或開新的機器16. If shutdownlist 有 pm:17. Open 18. Else: 19. Open }20. Shutdown shutdownlist’s pms #step7 關閉 shutdownlist 中的 pm

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Experiment structure The Common simulation

CloudSim – python based (pyCloudSim)[3] Interval: 60min by YouTube 3/11 15:40~3/18 21:59 2956 music videos Keyword: official music video/official music/official audio

Server scales: CPU: Intel(R)Core(TM)i3-2100CPU@3.10GHz RAM: 4.00GB OS: Windows(x64)

CPU utilization 1request =N(2,0.25)+bitrate/100N(1,0.25)

Bandwidth 1request = bitrate/1000

Experiment Results

[3]A.P.M.De La Fuente Vigliotti, D.Macedo Batista, “A green network-aware VMs placement mechanism” Proc. IEEE Global Communications Conference (GLOBECOM), pp.2530 – 2535,2014.

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Compared with Nash-Bargaining[13] /Best-Fit

Target Power-law (overload and underload) characters in VDCNs Power consumption and resource utilization Migration cost and placement cost

Experiment Results

[13] Yuan Feng, Baochun Li and Bo Li, "Bargaining Towards Maximized Resource Utilization in Video Streaming Datacenters", in IEEE Infocom, 2012.

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Our method can effectively reduce overload and underload in VDCNs

Number of overload and underload servers

VMPL Nash Best-Fit0

2

4

6

8

10

12

14

16

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Avg. overload servers(台 )

VMPL Nash Best-Fit0

10

20

30

40

50

60

Avg. underload servers(台 )

39.1%

23.3%

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Power consumption and resource utilization

VMPL Nash Best-Fit0

10000

20000

30000

40000

50000

60000

Avg. power consumption in DCN(W)

VMPL Nash Best-Fit160

170

180

190

200

210

220

230

Avg. active servers(台 )

VMPL Nash Best-Fit70

75

80

85

90

95

Avg. resource utilization(%)

3.4%

6.3台 36.8台10.6% 21.1%

Our method have minimal energy consumption and maximum resource utilization

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Migration times Migrate overload VMs:30.66 We also consider

Migrate idle server’s VMs: 38.88 Reserve space for popular VMs: 54.11

Migration Cost and Placement Cost

VMPL Nash Best-Fit0

20

40

60

80

100

120

140

Avg. migration times(次 )

VMPL Nash Best-Fit0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

Avg. placement time(s)

123.6

48.4

4.7

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VM Management Scheme with Power Law(VMPL) Reduce 10.6% power consumption in VDCNs Reducing 39.1% number of overload servers Improve 3.4% resource utilization Tradeoff

We generate additional  2.5 times of migration times

Conclusions

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End