1. Autonomic Workload Execution Control Using Throttling
Wendy Powley, Patrick Martin, Mingyi Zhang
School of Computing, Queen’s University, Canada
Paul Bird, Keith McDonald
IBM Toronto Lab, Canada
March 1, 2010

2. Acknowledgements
This work is supported by IBM’s Center for Advanced Studies (CAS), IBM Canada Ltd., Toronto, ON, Canada; Natural Science and Engineering Research Council (NSERC) of Canada; Ontario Centers of Excellence for Communication and Information Technology (OCE-CCIT); and Queen's University, Kingston, ON, Canada.
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3. Outline Introduction Query throttling approaches
Throttling control mechanisms
Step function controller
Black-box model controller
Experimental validation
Conclusions & future research
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4. Introduction
In a data server environment, there may be multiple types of workloads simultaneously present
On-line transactional processing (OLTP) workloads
On-line analytical processing (OLAP) workloads
Workloads can come from different sources and have different priorities and performance goals
There is an interdependency among the concurrently running workloads
Workloads compete for shared system resource
Some workloads may miss their performance goals
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5. Introduction (cont)
Workload management for database management systems (DBMSs) aims to meet performance objectives for all workloads
We previously proposed a workload execution control technique - query throttling [1]
It slows down long-running and resource intensive queries to free up shared system resources for more important workloads
W. Powley, P. Martin and P. Bird, “DBMS Workload Control Using Throttling: Experimental Insights”. In CASCON ’08: Proc. of the 2008 Conf. of the Center for Advanced Studies on Collaborative Research. Toronto, Canada. October pp
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6. Query Throttling Approaches
Two approaches of the query throttling
Constant throttling
Interrupt throttling
An example query
execution time: 100(s)
throttle percentage: 50%
constant throttle pause length: 1.0(s)
then:
tw = 100(s)
throttle time = 100(s) * 50% = 50(s)
tc = 1.0(s)
k = 50(s) / 1.0(s) = 50
ti = 50(s)
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7. Autonomic Control Mechanism
In this study, we specifically consider
Query throttling approaches as a component of a self- managing DBMS
We apply autonomic computing principles to dynamically control the amount of throttling
We examine two different types of
controllers
A step function controller
A black-box model controller
Feedback Loop
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8. Autonomic Control Mechanism (cont)
The Analyzer considers a goal achieved when
where, Pc is the current performance, Pg is the goal, and ε is the acceptable boundary
The Planner determines the amount of throttling
Important workloads meet performance goals
Less important workloads are not penalized more than necessary
An effector implements the constant throttling approach
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9. Step Function Controller
The controller determines the amount of throttling in n-th step
where, , th0 = 0, and Δ is the adjustment amount for throttling in a step
Distance between the goal and the actual performance
The adjustment amount of throttling
if then
where, σ is a defined threshold
where, n is the number of steps, and thmax
is the maximum amount of throttling
A Diminishing Step Function
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10. DBMS Black-box Model A DBMS black-box model: The control input u( k ):
the amount of throttling imposed on
less important workloads (OLAP)
The measured output y( k ):
the throughput of important
workloads (OLTP)
The disturbance input ( d ):
system’s mix of workloads
DBMS Black-box Model
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11. Black-box Model Controller
The relationship between inputs and outputs:
where:
y( k+1 ): predicted throughput in the (k+1)st time unit
y( k ): measured throughput in the k-th time unit
u( k ): the amount of throttling in the k-th time unit
a and b: the model parameters, which are determined experimentally
The model is built for the worst-case scenario
The highest ratio of less important to important workloads
The mix of workloads is unpredictable and changes over time
For other scenarios controller favors more important workloads by over-estimating the level of throttling required
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12. Black-Box Model Validation
Comparison Between the Projected and Actual Throughputs
In the experiments, the control inputs are in the range [0, 250] percent throttling
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13. Experiments
Experiments examine the effectiveness of the two controllers in maintaining the goals set for the important workload
Experiments were conducted on a PostgreSQL DBMS
Two separate databases with their own running workload
Competition for shared physical resources while no lock contention
OLTP workload is the important workload, and OLAP workload is the less important workload
Throttling OLAP workload in order to make OLTP workload meet its performance goal
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14. Impact of Mixing Workloads
The throughput of the important OLTP workload dropped when the workloads compete for share system resources
The response time of the OLAP workload doubled when the workloads compete for
share system resources
The effects of running the two workloads alone and simultaneously
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15. Throughput of the OLTP workload
Simple Controller
Throughput of the OLTP workload
The simple controller is used to adjust the amount of throttling for the OLAP workload
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16. Black-Box Model Controller
Throughput of the OLTP workload
The black-box model controller is used to adjust the amount of throttling for the OLAP workload
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17. Throughput of the OLTP workload
Hybrid Controller
Throughput of the OLTP workload
A hybrid controller combines the two approaches, where the black-box model controller initially sets the throttle value, and the simple controller fine-tunes performance
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18. Conclusions
We illustrate the use of two different types of controllers to control the level of throttling required
We verify that the important workload can dynamically meet the performance goals by using the controllers
Experimental results show that the hybrid controller is a good choice for query throttle control
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19. Future Work
To investigate how the black-box model may be updated dynamically to adapt to the highly varied and frequently changing workloads
To investigate possible key identifiers, such as high CPU or high memory usage, that may indicate workloads that are possible candidates for throttling
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20. Thank You
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21. The amount of throttling
Simple Controller
The amount of throttling
The simple controller is used to adjust the amount of throttling for the OLAP workload
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