1. Smita Vijayakumar Qian Zhu Gagan Agrawal
Automatic and Dynamic Accuracy Management and Resource Provisioning in A Cloud Environment
Smita Vijayakumar
Qian Zhu
Gagan Agrawal

2. Outline Research Goals Accuracy Management Experimental Evaluation
Background
Data Streams
Virtualization
Dynamic Resource Allocation
Accuracy Adaptation
Research Goals
CPU Resource Allocation
Accuracy Management
Experimental Evaluation
Conclusion

3. Data Streams Sequence of data packets in transmission Example
Live Camera Captures, Stock Markets, Video Streaming Applications, etc
Require Real-Time Analysis 3

4. Data Stream Processing on Clouds and VMs
Why Clouds for Streaming Apps
Pay-as-you model
Meet dynamically varying demand
Clouds are based on Virtual Machines
Software implementation of a machine that executes programs
Hides hardware and software heterogeneity
CPU can be shared between VMs
Modes of Operation
Capped Mode
Non-Capped Mode

5. Resource Allocation: Guiding Principles
Pay-as-you-go
Automatic Resource Allocation
Dynamic Resource Allocation
Both varying Data Rates and Characteristics affect resource requirement
Amount of Data and its Complexity determine CPU requirement

6. Research Goals
Framework for providing Accuracy and Resource Management in Cloud Environment
Accuracy Management
Convergence to application-specific accuracy goal
Maintain user-specified accuracy requirement for the entire duration of run
CPU Management
Converge to near -optimal resource allocation by constant monitoring of load characteristics

7. An Adaptive Application: Average of Input Stream
Random stream of integers
Application considers every third integer in the stream to compute average
Adaptive Parameter, sample = 1/3
If higher accuracy is desired, sample can be set to ½ or 1
But then, that requires more CPU resources

8. Accuracy in Data Stream Processing
Accuracy-specific processing: User- defined processing accuracy should be met
Changes according to input Data Characteristics
Require corresponding Resource Allocations
Final cost determined by amount of resources allocated over time

9. Calculating Current Application Accuracy
Application developer provides Accuracy Function
Many methods of calculating accuracy:
Method of direct comparison with input data
Not always viable
Method of correlation with more fine-grained processing
Process data with current adaptive parameters
Process same data set with adaptive parameter set to greater accuracy
Compare results

10. Example of Accuracy in Adaptive Application
Process batch with current value sample =1/3
For same data set
Set sample = 1and find new average
Accuracy = f(avg, higher_avg)
If Accuracy < Accuracy Goal, set sample = 1/2
Repeat adapting sample

11. Outline Background Research Goals CPU Resource Allocation
Accuracy Management
Experimental Evaluation
Conclusion

12. CPU Allocation Algorithm
Monitor current load statistics
Buffer Write Time
Processing Time
Time-Averaged rates
Average data rate over a time window
Update CPU allocation
Time- Averaged pattern indicates decrease or increase in data flow
Continuous Monitoring and Action
Arrive at most optimal CPU Allocation

13. CPU Allocation Algorithm
Resource Allocation Adjustments:
Coarse Multiplicative Increase
Fine Linear Increase
Fine Linear Decrease
Coarse Linear Decrease
Inspired by TCP Congestion Control

14. CPU Allocation Algorithm
Met Accuracy Goal?
Sleep and awaken periodically
Adjust CPU Allocation
Met Allocation Needs?
Yes No
Yes No

15. Outline Background Research Goals CPU Resource Allocation
Accuracy Management
Experimental Evaluation
Conclusion

16. Accuracy Management Checks periodically for accuracy level
Re-computes application accuracy
If less than specified value then
Adjust adaptive parameters
Repeat
Once target accuracy is achieved, wakes up after every 500 rounds of processing

17. Accuracy Adaptation: Design
Get Current Application Accuracy
Met Accuracy Goal?
Sleep and awaken periodically
Yes No
Adjust Adaptive Parameters

18. Interaction between Components
Process Data Block
If baseline accuracy not met
Accuracy Module adapts till accuracy is met
State: Accuracy Met
Else, periodically monitor accuracy
Periodically CPU Manager wakes up
Checks if accuracy goal is met
Checks CPU resource allocation

19. Outline Research Objectives Introduction to Cost Framework
CPU Resource Allocation
Accuracy Management
Experimental Evaluation
Static Experiments
Dynamic Experiments
Conclusion

20. Experimental Focus Experiments Process Static Experiments
Constant Data Rate And Characteristics
Dynamic Experiments
Varying Data Rates and/or Characteristics
Process
Accuracy Adaptation to User-Specified Accuracy
CPU Convergence to near-optimal Allocation

21. Streaming Applications
Multi-staged pipelined processing
Two streaming applications considered:
CluStream
Intermediate Microclustering of data
Approx-Freq-Counts
Mining most frequently seen itemset within permissible error

22. Experimental Setup Virtualization Technology: Xen
Ideal CPU Usage: Xentop
Applications initialized to values corresponding to least accuracy
Communication between management node and processing nodes using UDP

23. CluStream Static Accuracy Adaptation
Accuracy Adaptation for 1.2MBps and 6MBps data rates

24. CluStream Static Accuracy Adaptation
Ideal CPU Load: 74%
Average CPU Allocated: 76.0%
Ideal CPU Load: 54%
Average CPU Allocated: 55.4%
Accuracy and CPU Allocation Adaptation for 1.2MBps and 6MBps data rates

25. Approx-Freq-Counts Dynamic Accuracy Adaptation
Spread Distb
Sharp Distb
Spread Distb

26. Approx-Freq-Counts Dynamic Accuracy Adaptation
Sharp Distb
Slow Data Rate
Spread Distb
Fast Data Rate
Sharp Distb
Slow Data Rate

27. Conclusion
A framework for automatically and dynamically managing resource allocations on cloud environments
Eliminates manual intervention
Ensures user-specified accuracy is maintained
Converges to near-optimal resource allocation
Adapts to varying data stream characteristics
Low Overheads: Within 2% ideal resource allocation

28. Thank You!