1. Smita Vijayakumar Qian Zhu Gagan Agrawal 1

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

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

4. 4  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.  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 5

6.  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 6

7. 7  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 1 2 7 4 2 5 8 2 6 8 0 4 3 4 8 2 2 6 7 3 4 3 1 3 6 8 3 2 5 9 9 3 4 6 8..

8. 8  Accuracy-specific processing: User- defined processing accuracy should be met  Accuracy changes according to input Data Characteristics  Requires corresponding Resource Allocations  Final cost determined by amount of resources allocated over time

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

10. 10  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.  Background  Research Goals  CPU Resource Allocation  Accuracy Management  Experimental Evaluation  Conclusion 11

12.  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 12

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

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

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

16.  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 periodically 16

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

18. 18  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.  Research Objectives  Introduction to Cost Framework  CPU Resource Allocation  Accuracy Management  Experimental Evaluation  Static Experiments  Dynamic Experiments  Conclusion 19

20.  Experiments 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 20

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

22. 22  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. Accuracy Adaptation for 1.2MBps and 6MBps data rates 23

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

25. Spread DistbSharp DistbSpread Distb 25

26. Slow Data RateFast Data RateSlow Data Rate 26

27.  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 27

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