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SLA-aware load balancing for cloud datacenters. 指導教授：王國禎 學生：黎中誠 國立交通大學資訊工程系 行動計算與寬頻網路實驗室. Problem Definition. Tree of Load Balancing. Related work. Proposed Architecture. Related work. Proposed Architecture. Delta Learning Rule. Load balancing method. Capacity index Weight. - PowerPoint PPT Presentation
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Copyright © 2010, [email protected]
SLA-aware load balancing for cloud datacenters
指導教授：王國禎 學生：黎中誠國立交通大學資訊工程系行動計算與寬頻網路實驗室
Copyright © 2010, [email protected]
Problem Definition
User
InternetLoad
Balancer
WAN/LAN
Real Server 1
Real Server 2 Real Server n
. . . . . . .
Cloud Computing Environment
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Tree of Load Balancing
Cloud load balancing
DistributedCentralized[1]
P2P[3][4]Client-Server[2]
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Proposed Architecture
Global balancer
Local balancer Monitor
VM1 VM2 VMn...
Virtual Zone 1
Global balancer
Local balancer Monitor
VM1 VM2 VMn...
Global balancer
Local balancer Monitor
VM1 VM2 VMn...
Virtual Zone 3
Virtual Zone 2
User
Request
P2P
P2P P2P
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Proposed Architecture
Communication
Prediction
Scheduler
Handler
History
Monitor
Adjustment
Global balancer
Local balancer
Real server1
Real server2
Real server3
Real servern
.
.
.
.
Request
P2P P2P
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Delta Learning Rule
Load balancer
x1c1 x2c2 x3c3
Server 1 Server 2 Server 3
weight 𝑖=𝑥𝑖∗ c𝑖
∑𝑗=1
𝑛
𝑥 𝑗∗ c 𝑗
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Load balancing method
• Capacity index
• Weight
C apacity index (𝐶𝑖)=1−MAX (𝐶𝑃𝑈 ,𝑀𝑒𝑚 , h𝐵𝑎𝑛𝑑𝑤𝑢𝑑𝑡 , 𝐼 /𝑂)
weight 𝑖=𝑥𝑖∗ c𝑖
∑𝑗=1
𝑛
𝑥 𝑗∗ c 𝑗
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Artificial neural network
• Supervised learning– Supervised learning is the machine learning
task of inferring a function from supervised (labeled) training data
• Unsupervised learning– Unsupervised learning also encompasses many
other techniques that seek to summarize and explain key features of the data
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Delta Learning Rule
• r = (0.8 ． di-oi)f’(neti)• Δωi = η ． r ． x
(．) f(．)neti f(neti) oi
Learning signal generator╳ di
Δωi
η
xr
.
.
.
.
Δωi1
Δωijxj
x1
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Comparison of Load Balancing
[1] [2] [3] [4] Our design
Architecture Centralized Distributed Distributed Distributed Distributed
Information exchange
Connection Connection Connection Shared information
Connection
Connection Client-Server Client-Server P2P Read information
P2P
Dynamic scheduling
N Y Y Y Y
Monitor N N N Y Y
Consider the SLA
N N N N Y
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Conclusions
• We propose architecture based on distributed load balancer which is different from general centralized balancer
• Combination of system performance monitoring and neural network
• This system can avoid SLA violations
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References
• [1] V. Nae, A. Iosup, and R. Prodan, "A Scheduling Strategy on Load Balancing of Virtual Machine Resources in Cloud Computing Environment“, in Parallel and Distributed Systems, IEEE Transactions on , 2010, pp. 380 - 395.
• [2] R. Suselbeck, G. Schiele, and C. Becker, "Towards a Load Balancing in a Three-level Cloud Computing Network," in Network and Systems Support for Games (NetGames), 2009, pp. 1 - 2.
• [3] Shu-Ching Wang, Kuo-Qin Yan, Wen-Pin Liao, and Shun-Sheng Wang, "A Load Balancing Mechanism Based on Ant Colony and Complex Network Theory in Open Cloud Computing Federation," in IEEE ICCSIT, 2010, pp. 108 - 113.
• [4] Rajkumar Rajavel, "A Comparative Study into Distributed Load Balancing Algorithms for Cloud Computing," in IEEE INCOCCI, Erode, 2010, pp. 419 - 424.
