The document proposes using workload patterns and collaborative filtering to predict quality of service for dynamic cloud service configuration and auto-scaling. It describes collecting monitoring data on invocations of different services, normalizing the data, identifying patterns in the data, and using those patterns and collaborative filtering to predict performance of future invocations.

1. Workload Patterns for Quality-driven Dynamic Cloud Service Configuration and Auto-scaling
Li Zhang*, YichuanZhang*, Pooyan Jamshidi, Lei Xu, Claus Pahl
IC4, School of Computing,Dublin City University, Ireland
*Northeastern University, China
Pooyan.jamshidi@computing.dcu.ie
UCC, London, 2014

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Service
name
1st
invocation
2nd
invocation
3rd
invocation
4th
invocation
5th
Invocation
s1
[0.2,10,0.2]
-> 0.5
[0.15,20,0.5]
-> 2.0
[0.25,10,0.1]
-> 0.2
[0.2,30,0.3]
-> 0.8
[0.2,8,0.2]
-> 1.2
s2
[0.12,11,0.1]
-> 0.3
[0.2,20,0.4]
-> 1.8
s3
[0.2,20,0.3]
-> 3.0
[0.1,20,0.2]
-> 6.0
[0.15,20,0.3]
-> 4.0
[0.2,15,0.2]
-> 2.4
[Memory, Network, CPU]-> performance range of metrics memorynetwork throughput CPU utilization stable

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











s
s s s
s s
s s
s s s s
3 ~ 4
0.4 ~ 0.5 2 ~ 2.4 0.3 ~ 0.5
0.8 ~1.1 1.1~1.5
0.2 ~ 0.5 1~1.3
1 2 3 4
4
3
2
1
M
M
M
M
M1 = [0.2-0.4, 30-40, 0.5-0.6]

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Invocation Pattern Match
New observation
QoSInformation in Matched Pattern?
SWP
Database
Return Prediction
Prediction based on Collaborative Filtering

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Observations 1 …… I …… n
Response Time T1 …… Ti …… Tn
Input Datasize Data1 …… Datai …… Datan
Throughput TP1 …… TPi …… TPn
CPU utilization CPU1 …… CPUi …… CPUn
Observations 1 …… I …… n
Response Time 1 …… y1(i) …… y1(n)
Input Datasize 1 …… y2(i) …… y2(n)
Throughput 1 …… y3(i) …… y3(n)
CPU utilization 1 …… y4(i) …… y4(n)
Normalization
• Usage Information of Service s:
• Take response time as the reference sequence x0(k), k =
1,…, n, and other characteristics as comparative sequences.
• Calculate association degree of other characteristics with
response time.
• Take the characteristics of an invocation as standard and
carry out normalization of the other characteristics

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Service ConsumerRegister CentreWeb ServicesMonitors on ServersMonitors on ClientsWSIP ExtractionWeb QoS management systemMonitoring Log RecorderExecution Log RecorderService ProvidersMonitoring LogExcution LogWSIP DatabaseWeb QoS Prediction Based on WSIP

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an increase of the dataset size improves the accuracy significantly

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010002000300040005000600070008000900012345678910Datasize(thousand) Time(ms) CF methodMCF method

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5
Service
name
1st
invocation
2nd
invocation
3rd
invocation
4th
invocation
5th
Invocation
s1 [2,10,0.2]
-> 0.5
[1.5,20,0.5]
-> 2.0
[2.5,10,0.1]
-> 0.2
[2,30,0.3]
-> 0.8
[2,8,0.2]
-> 1.2
s2 [1.2,11,0.1]
-> 0.3
[2,20,0.4]
-> 1.8
s3 [2,20,0.3]
-> 3.0
[1,20,0.2]
-> 6.0
[1.5,20,0.3]
-> 4.0
[2,15,0.2]
-> 2.4
[Memory, Network, CPU]-> performance
memory network
throughput CPU utilization
7












s
s s s
s s
s s
s s s s
3 ~ 4
0.4 ~ 0.5 2 ~ 2.4 0.3 ~ 0.5
0.8 ~1.1 1.1~1.5
0.2 ~ 0.5 1~1.3
1 2 3 4
4
3
2
1
M
M
M
M
M1 = [0.2-0.4, 30-40, 0.5-0.6]
16
an increase of the dataset size improves the
accuracy significantly
18
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1 2 3 4 5 6 7 8 9 10
Datasize(thousand)
Time(ms)
CF method
MCF method