Pricing for Utility-driven Resource Management and Allocation in Clusters Chee Shin Yeo and Rajkumar Buyya Grid Computing and Distributed Systems (GRIDS) Lab. Dept. of Computer Science and Software Engineering The University of Melbourne, Australia www.gridbus.org/ WW Grid
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Pricing for Utility-driven Resource Management and Allocation in Clusters
WW Grid. Pricing for Utility-driven Resource Management and Allocation in Clusters. Gri d Computing and D istributed S ystems (GRIDS) Lab. Dept. of Computer Science and Software Engineering The University of Melbourne, Australia www.gridbus.org/. Chee Shin Yeo and Rajkumar Buyya. - PowerPoint PPT Presentation
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Pricing for Utility-driven Resource Management
and Allocation in Clusters
Chee Shin Yeo and Rajkumar Buyya
Grid Computing and Distributed Systems (GRIDS) Lab. Dept. of Computer Science and Software EngineeringThe University of Melbourne, Australia
Job QoS Satisfaction Cluster Profitability Average Waiting Time Average Response Time
17
Normalised Comparison of FCFS, Libra & Libra+$
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Job QoSSatisfaction
Cluster Profitability Average WaitingTime
Average ResponseTime
FCFS
Libra
Libra+$, β = 0.01
18
Varying Cluster Workload
Scheduling policies First-Come-First-Served (FCFS) Economy based Proportional Resource
Sharing (Libra) Libra with dynamic pricing (Libra+$)
An increasing mean job execution time 6, 7, 8, 10, 15 and 30 hours
19
Impact of Increasing Job Execution Time on Job QoS Satisfaction
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
6 7 8 10 15 30
Mean Job Execution Time (hours)
Job
Qo
S S
ati
sfa
ctio
n (
%)
FCFS
Libra
Libra+$, β = 0.01
20
Impact of Increasing Job Execution Time on Cluster Profitability
0%
5%
10%
15%
20%
25%
30%
6 7 8 10 15 30
Mean Job Execution Time (hours)
Clu
ster
Pro
fita
bili
ty (
%)
FCFS
Libra
Libra+$, β = 0.01
21
Varying Pricing Factor for Different Level of Sharing
Scheduling policies Libra with dynamic pricing (Libra+$)
An increasing dynamic pricing factor β 0.01, 0.1, 0.3, and 1
22
Impact of Increasing Dynamic Pricing Factor on Job QoS Satisfaction
0%
10%
20%
30%
40%
50%
60%
70%
80%
0.01 0.1 0.3 1
Dynamic Pricing Factor β
Job
Qo
S S
ati
sfa
ctio
n (
%)
FCFS
Libra
Libra+$
23
Impact of Increasing Dynamic Pricing Factor on Cluster Profitability
0%
10%
20%
30%
40%
50%
60%
70%
80%
0.01 0.1 0.3 1
Dynamic Pricing Factor β
Clu
ster
Pro
fita
bili
ty (
%)
FCFS
Libra
Libra+$
24
Tolerance against Estimation Error
Under-estimated execution time EEi e.g. job whose execution time Ei = 60 hours
has EEi = 30 hours for estimation error = 50% Scheduling policies
Libra – Economy based Proportional Resource Sharing (Libra)
Libra with dynamic pricing (Libra+$) An increasing estimation error for
estimated execution time EEi 0%, 10%, 30% and 50%
25
Impact of Increasing Estimation Error on Job QoS Satisfaction
0%
10%
20%
30%
40%
50%
60%
70%
80%
0% 10% 30% 50%
Estimation Error for Estimated Execution Time EE i (%)
Job
Qo
S S
ati
sfa
ctio
n (
%)
Libra
Libra+$, β = 0.01
Libra+$, β = 0.1
Libra+$, β = 0.3
Libra+$, β = 1.0
26
Impact of Increasing Estimation Error on Cluster Profitability
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
0% 10% 30% 50%
Estimation Error for Estimated Execution Time EE i (%)
Clu
ste
r P
rofi
tab
ility
(%
)
Libra
Libra+$, β = 0.01
Libra+$, β = 0.1
Libra+$, β = 0.3
Libra+$, β = 1.0
27
Conclusion & Future Work
Importance of effective pricing function (demand exceeds supply of resources)
Satisfy four essential requirements for pricing
Serves as means of admission control Tolerance against estimation errors Higher benefits for cluster owner Future work
Explore different pricing strategies Examine different application models
Backup
29
Related Work
Traditional cluster RMS Load Sharing Facility (LSF) – Platform Load Leveler – IBM Condor – University of Wisconsin Portable Batch System (PBS) – Altair Grid
Technologies Sun Grid Engine (SGE) – Sun Microsystems
Market-based cluster RMS REXEC Libra
30
Job details eg. Estimated execution time
Resource requirements eg. Memory size, Disk storage size