refining the estimation of the available bandwidth in inter-cloud links for task...
TRANSCRIPT
Refining the Estimation ofthe Available Bandwidth in Inter-Cloud
Links for Task Scheduling
Thiago A. L. Genez, Luiz F. Bittencourt,Nelson L. S. da Fonseca, Edmundo R. M. Madeira
Institute of Computing (IC)University of Campinas (UNICAMP)
Campinas, SP, Brazil
December 10, 2014IEEE GLOBECOM 2014
1 / 22
Outline
Introduction
Related Works
Procedure for Deflating Estimates of the Available Bandwidth
Evaluation
Final Considerations
2 / 22
Introduction
Workflow Scheduling Problem in Hybrid CloudsI Peak demand time:
• Private resources → overloaded or insufficient• Hybrid Cloud: Public resources + private resources
I What are the advantages of using public clouds?• Elasticity• Pay-as-you-go basis
I Workflow scheduling problem
3 / 22
Introduction
Current schedulersI Not designed to cope with imprecise information
I Produce schedules without taking into account the variability of theavailable bandwidth in inter-cloud links
I Available bandwidth can increase or decrease at the running time
I Application execution can lead• Violation of deadlines
4 / 22
Introduction
Purpose of this work
How to reduce the negative impact of imprecise information about theinter-cloud available bandwidth on the production of schedules by ascheduler that was not designed to address with such impreciseinformation?
Challenge
Use the original scheduling algorithm
Proposed Mechanism
Deflating the estimate of the inter-cloud available bandwidth based onthe expected imprecision of such estimate and provide a deflatedbandwidth estimate as an input to the scheduler
5 / 22
Introduction
Purpose of this work
How to reduce the negative impact of imprecise information about theinter-cloud available bandwidth on the production of schedules by ascheduler that was not designed to address with such impreciseinformation?
Challenge
Use the original scheduling algorithm
Proposed Mechanism
Deflating the estimate of the inter-cloud available bandwidth based onthe expected imprecision of such estimate and provide a deflatedbandwidth estimate as an input to the scheduler
5 / 22
Introduction
Purpose of this work
How to reduce the negative impact of imprecise information about theinter-cloud available bandwidth on the production of schedules by ascheduler that was not designed to address with such impreciseinformation?
Challenge
Use the original scheduling algorithm
Proposed Mechanism
Deflating the estimate of the inter-cloud available bandwidth based onthe expected imprecision of such estimate and provide a deflatedbandwidth estimate as an input to the scheduler
5 / 22
Outline
Introduction
Related Works
Procedure for Deflating Estimates of the Available Bandwidth
Evaluation
Final Considerations
6 / 22
Related Works
Rahman et al.– Performance of the network of the Amazon EC2
(2010)
– Analysis of the packets delay of VMs to/from Amazon EC2– Large delay variations– Negatively impact the performance of scientific applications
Batista et al. – Describe tools for estimating available bandwidth2010 – Produce estimations with large variability
8 / 22
Outline
Introduction
Related Works
Procedure for Deflating Estimates of the Available Bandwidth
Evaluation
Final Considerations
9 / 22
Procedure for Deflating Estimates of the AvailableBandwidth in Inter-cloud Links
Available bandwidth
estimation toolScheduler
Estimate of the
Available Bandwidth
Hybrid Cloud
Application workflow
and
Deadline Value
Schedule
10 / 22
Procedure for Deflating Estimates of the AvailableBandwidth in Inter-cloud Links
Available bandwidth
estimation toolScheduler
Estimate of
the Available
Bandwidth
Expected
uncertainty
value
Hybrid Cloud
Procedure
Deflated
Available
Bandwidth
Estimate
Application workflow
and
Deadline Value
Schedule
10 / 22
Procedure for Deflating Estimates of the AvailableBandwidth in Inter-cloud Links
ProcedureI History of past executions of the target workflow
I When a workflow is about to be scheduled
1. Estimate of the available bandwidth2. Expected uncertainty value3. Query the history of past executions of the target workflow4. Calculates the deflating factor U
U = 10 ⇒ 90% of the estimate of the available bandwidth
I Schedule produced is based on the expected uncertainty of theestimate of available bandwidth in inter-cloud links
11 / 22
Procedure for Deflating Estimates of the AvailableBandwidth in Inter-cloud Links
ProcedureI History of past executions of the target workflow
I When a workflow is about to be scheduled
1. Estimate of the available bandwidth2. Expected uncertainty value3. Query the history of past executions of the target workflow4. Calculates the deflating factor U
U = 10 ⇒ 90% of the estimate of the available bandwidth
I Schedule produced is based on the expected uncertainty of theestimate of available bandwidth in inter-cloud links
11 / 22
Procedure for Deflating Estimates of the AvailableBandwidth in Inter-cloud Links
ProcedureI History of past executions of the target workflow
I When a workflow is about to be scheduled
1. Estimate of the available bandwidth2. Expected uncertainty value3. Query the history of past executions of the target workflow4. Calculates the deflating factor U
U = 10 ⇒ 90% of the estimate of the available bandwidth
I Schedule produced is based on the expected uncertainty of theestimate of available bandwidth in inter-cloud links
11 / 22
Procedure for Deflating Estimates of the AvailableBandwidth in Inter-cloud Links
Database
Available bandwidth
estimation toolScheduler
Observed
Available
Bandwidth
value
Expected
uncertainty
value
Hybrid Cloud
Procedure
Deflated
Available
Bandwidth
Application workflow
and
Deadline Value
Schedule
12 / 22
Procedure for Deflating Estimates of the AvailableBandwidth in Inter-cloud Links
Database
Available bandwidth
estimation toolScheduler
Estimate of
the Available
Bandwidth
Expected
uncertainty
value
Hybrid Cloud
Procedure
Deflated
Available
Bandwidth
Application workflow
and
Deadline Value
Schedule
12 / 22
Procedure for Deflating Estimates of the AvailableBandwidth in Inter-cloud Links
Database
Available bandwidth
estimation toolScheduler
Estimate of
the Available
Bandwidth
Expected
uncertainty
value
Hybrid Cloud
Procedure
Deflated
Available
Bandwidth
Application workflow
and
Deadline Value
Schedule
12 / 22
Procedure for Deflating Estimates of the AvailableBandwidth in Inter-cloud Links
Database
Available bandwidth
estimation toolScheduler
Estimate of
the Available
Bandwidth
Expected
uncertainty
value
Hybrid Cloud
Procedure
Deflated
Available
Bandwidth
Application workflow
and
Deadline Value
Schedule
12 / 22
Procedure for Deflating Estimates of the AvailableBandwidth in Inter-cloud Links
Database
Available bandwidth
estimation toolScheduler
Estimate of
the Available
Bandwidth
Expected
uncertainty
value
Hybrid Cloud
Procedure
Deflated
Available
Bandwidth
Application workflow
and
Deadline Value
Schedule
12 / 22
Procedure for Deflating Estimates of the AvailableBandwidth in Inter-cloud Links
Database
Available bandwidth
estimation toolScheduler
Estimate of
the Available
Bandwidth
Expected
uncertainty
value
Hybrid Cloud
Procedure
Deflated
Available
Bandwidth
Application workflow
and
Deadline Value
Schedule
Untouched QualifedSolution
12 / 22
Procedure for Deflating Estimates of the AvailableBandwidth in Inter-cloud Links
Computation of the Deflating factor U for the TargetWorkflow
I Multiple Linear Regression: f(x, y) = ax+ by + c
• x: Current estimate of the available bandwidth• y: Current expected uncertainty• Deflating factor U = f(x, y)
Computation of the coefficients a, b and cI Target workflow G: dataset HG
• 5-tuple hi =(bw, p,U , errormG , error$G
)I Subset Hk ⊆ HG
• For each pair (bw, p) in HG
•(bw, p,Um)
and(bw, p,U$) are added into Hk
I Subset Hk is used by the multiple linear regression
13 / 22
Outline
Introduction
Related Works
Procedure for Deflating Estimates of the Available Bandwidth
Evaluation
Final Considerations
14 / 22
Evaluation
Experimental ParametersI Scheduler
• HCOC scheduling algorithm
I Hybrid Cloud Scenario• 1 private cloud and 2 public clouds• Inter-cloud bandwidths of 10 to 60 Mbps• Intra-cloud bandwidths of 1 Gbps
I Simulator• Estimates the makespan and cost of the execution of the workflow
15 / 22
Evaluation
Scheduler
DAX
File
VMs
File
Schedule Simulator
Reduction factor Uncertain
Makespanand
Cost ($)
Available Bandwidth
Makespanand
Cost ($)U p
b
Database
16 / 22
Evaluation
Experimental Steps
1. History of execution was created• Fixed bandwidth deflating factors U ∈ {0, 25, 50}• p varying from 45% to 99%• 100 simulations
2. Multiple linear regression (MLR) procedure• f(x, y) = ax+ by + c• Employs using 50% and 100% of the dataset
3. Use the equation f(x, y) to calculate the deflating factor U• 100 simulations
17 / 22
Evaluation
40
50
60
70
80
90
100
0 45 50 60 70 80 90 100
% o
f q
ua
lifie
d s
olu
tion
s
Uncertainty p
Montage DAG
U=0U=25U=50
MLR 50%MLR 100%
Inter-cloud available bandwidth of 60Mbps
D = Tmax × 3/718 / 22
Evaluation
25
30
35
40
45
50
55
60
0 45 50 60 70 80 90 100
Ave
rag
e m
ake
spa
n e
stim
atio
n
Uncertainty p
Montage DAG
U=0U=25U=50
MLR 50%MLR 100%
Inter-cloud available bandwidth of 60Mbps
D = Tmax × 3/719 / 22
Outline
Introduction
Related Works
Procedure for Deflating Estimates of the Available Bandwidth
Evaluation
Final Considerations
20 / 22
Final Considerations
ConclusionI Current scheduler
• Estimated available bandwidth is precise at the scheduling time• Produce inefficient scheduling decisions• Missing deadlines, increasing costs and makespan more than expected
I The proposed procedure• Deflates the estimate of the available bandwidth in inter-cloud links• Multiple linear regression approach• Increases the number of qualified solutions
21 / 22
Final Considerations
ConclusionI Current scheduler
• Estimated available bandwidth is precise at the scheduling time• Produce inefficient scheduling decisions• Missing deadlines, increasing costs and makespan more than expected
I The proposed procedure• Deflates the estimate of the available bandwidth in inter-cloud links• Multiple linear regression approach• Increases the number of qualified solutions
21 / 22
Thank You!
Questions?
Acknowledgment:
grant #2014/08607-4
Sao Paulo Research Foundation
22 / 22