Investigating Business-Driven
Cloudburst Schedulers for e-Science
Bag-of-Tasks Applications
David Candeia, Ricardo Araújo, Raquel
Lopes, Francisco Brasileiro
UFCG/LSD - Brazil
E-Science
• Computers are changing scientific
research
– More collaborative
– As investigation tools
(simulations, data analysis, etc...)
• Many researchers are now
hungry for computing resources
– Sometimes they have deadlines
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Solution 1: P2P grids
www.ourgrid.org
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Solution 1: P2P grids
www.ourgrid.org
© Raquel Lopes - UFCG/LSD
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Solution 1: P2P grids
www.ourgrid.org
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The problem with the solution 1
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Solution 2: Cloud providers
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The problem with solution 2
• They have in-house resources and they’d like
to use them
• They have access to a P2P grid almost for
“free” and they’d like to use it
• They have some budget to buy cloud
resources, but they’d like to do that efficiently
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The problem
• We’d like to run BoT applications
in this hybrid infrastructure
• How to schedule the
application?
– To meet the deadline
– Use the budget efficiently
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Business driven approach
• Resources from cloud providers have a cost
• Applications have value to their owners
– Utility functions
– What is the gain if we run the application in ∆t
units of time?
• Maximise profit: utility(∆t) – cost(myCloud)
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Approaches we studied
• Use all your budget acquiring cloud resources
until the application has finished
– Greedy scheduler
• Try to find the execution time that
maximises the profit achieved by running
the application
– Online cloudburst scheduler
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Online Cloudburst Scheduler
BoT
submitted
1.
2.
3.
4.
“Estimate” P2P grid throughput
Simulation process…
Find out the best time to finish
Acquire cloud instances for the
next hour
BoT
deadline
…
1
2
3
…
© Raquel Lopes - UFCG/LSD
Time (hours)
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Online Cloudburst Scheduler
BoT
submitted
1.
2.
3.
4.
“Estimate” P2P grid throughput
Simulation process…
Find out the best time to finish
Acquire cloud instances for the
next hour
BoT
deadline
…
1
2
3
…
© Raquel Lopes - UFCG/LSD
Time (hours)
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P2P grid throughput estimation
• Collects past information about the grid
• Uses the information to estimate future
throughput
• Prediction approaches:
– Conservative
– Derivative
– Predictive
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Evaluation
• Question: These online solutions seem to be
more sophisticated than the greedy approach,
but are they more efficient?
• Simulation experiments
– We developed in Java
– Simulates a scheduler coordinating the execution of a
BoT application
– Each simulation experiment gives the profit achieved
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Evaluation
• Optimal scheduler
– Knows the real grid capacity
– Able to make optimal decisions
• Compare profits
– Efficiency metric in (0,-∞)
– e = -0.3 means the profit is 30% worse than the
optimal profit achieved
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Experimental setup - Application
• Collection of tasks whose demand are
normally distributed
– Four application flavours
• Two utility function:
• Maximum utility: 1xCost, 2xCost,
10xCost, 50xCost,
100xCost
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Experimental setup - others
• One cloud provider
– $0.085 per one hour of cloud instance
– Number of instances acquired simultaneously:
limited to 20, or unlimited
• Scheduling policies:
– Greedy
– Online schedulers (Conservative, Derivative,
Predictive-±10%, Predictive-±50%)
• Turn size: 1 hour
• Researcher budget: +∞ ()
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Linearly decaying utility – cloud unlimited
1 2
10
50
1 2
10
50
1 2
10
50
1 2
10
50
Utility/Cost (log scale)
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Utility/Cost (log scale)
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Exponentially decay utility – unlimited
1 2
10
50
1 2
10
50
1 2
10
50
1 2
10
50
Utility/Cost (log scale)
© Raquel Lopes - UFCG/LSD
Utility/Cost (log scale)
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Linear decay utility – cloud limit is 20
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Exponentially decay utility – cloud limit is 20
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Conclusions
• Modeled the problem and carried out
simulation experiments whose results were
treated with appropriate statistical methods
• Utility/Cost relationship drives the scheduling
– Small (units): beware of the costs
– High: the cost of acquiring more resources is almost
negligible in the face of the utility they return
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Future work
• Investigate other online schedulers
– Use a more intelligent grid QoS predicting model
• Consider data transfer costs in the model
• Carry out measurement experiments
– Cloudburst scheduler implemented as an OurGrid
Broker
• Consider different experimental environments
– User has also a cluster in-house
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Thanks
david@lsd.ufcg.edu.br
ricardo@lsd.ufcg.edu.br
raquel@dsc.ufcg.edu.br
fubica@dsc.ufcg.edu.br
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