Threads concurrency identifying performance deviations in thread pools(1)

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Presentation slides made for research paper 'Identifying Performance Deviations in Thread Pools', by Mark D. Syer, Bram Adams and Ahmed E. Hassan Software Analysis and Intelligence Lab (SAIL) School of Computing, Queen's University, Canada { mdsyer,bram, ahmed } @cs.queensu.ca

Software

Outline
● Introduction
● Thread Pools
● Methodology
● Case study
● Results
● Evaluation
● Conclusion
2

Introduction
● Ultra-Large-Scale (ULS) systems need high
concurrency and speed.
● Analysing system performance is difficult
○ Time Consuming
○ Significant manual review of the data and logs
○ Lack of tool support, need heavy instrumentation
○ Hardware sensor gathered data hard to interpret.
3

Main contributions
To analyze performance deviations in systems
designed using thread pools..
1. A top-down methodology for identifying and ranking
the most deviating thread behaviour
1. A qualitative and quantitative evaluation of proposing
methodology on a large-scale industrial ULS system
4

Thread Pools
● Advantages
o Avoid thread create/destroy overhead
o System become more responsive
● Difficulties
o Too many threads → resource thrashing →
performance degradation
o Hard to configure and test
o synchronization errors → idle threads/deadlocks
o Thread leakage
5

Motivational Example
● Analyse the system for x5 large workload
● Machine level macro threads for each thread
pool
● Identify majority and deviating behaviours
by the use of clusters in dendrogram
6

Methodology (cont.)
A. Performance Data
a. Resource usage metrics of the pooled resources
eg: CPU, memory, #opened files
a. Resource metrics - accuracy - overhead of
performance monitoring - data redundancy
9

Methodology (cont.)
B. Metric Abstraction
a. Group threads into higher level (macro) abstractions
by space or time.
eg: in a cluster of machines, all pooled threads
executing on one node aggregated into one.
b. Identify the deviations at higher level
c. Repeat methodology for deviations at lower level
10

Methodology (cont.)
C. Distance Calculation Between Covariance
Matrices
To get level of dissimilarity, or distance, between two
abstractions
11
Remember your Maths?

Methodology (cont.)
Covariance metric
12
Covariance between X(n) and X(1)
how much X(n) and X(1) variables change together
Ref - Wikipedia(2014, Dec), Covarience Metrics. link: http://en.wikipedia.org/wiki/Covariance_matrix
Variance of metric ( i )

Methodology (cont.)
Eg:
● Metrics Xc,XM,XH(cpu, memory, #open files) for
Threads A,B
● A is instrumented 100 times, B 1000 times
● Calculate distance metric (Forstner & Moonen method)
one-dimensional distance for each pair of covariance
matrices
● Distance value - Similarity
13

Methodology (cont.)
D. Hierarchical Clustering
● Starts with each abstraction in its own cluster and
proceeds to find and merge the closest pair of clusters
● Have used Ward's method of clustering
14ref -Large Scale Gene Expression Data Analysis I . link:http://compbio.uthsc.edu/microarray/lecture1.htm

Methodology (cont.)
E. Cluster Visualization
15

Methodology (cont.)
F. Ranking Clusters
● Recursive
● Top to bottom
16

Case Study
● Performance data
○ CPU, Virtual Bytes, Private Bytes, Handles,
MicroThreads
● Metric Abstraction
● Hierarchical Clustering and Ranking
18

Quantitative Evaluation
● Validating the ability of the methodology to
identify and rank deviations
● Identified important deviations
● Injecting synthetical deviations in to the
performance data
● Verify the methodology (precision/recall)
21

Conclusion
● A methodology for automatically identifying
deviating behaviour in ULS systems
● Ranking most deviating thread behavior
with different abstractions (wave, thread)
● It is possible to use this methodology for
other applications
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Threads concurrency identifying performance deviations in thread pools(1)

1. Identifying Performance Deviations in Thread Pools Mark D. Syer, Bram Adams and Ahmed E. Hassan (Software Analysis and Intelligence Lab (SAIL) School of Computing, Queen's University, Canada) 1

2. Outline ● Introduction ● Thread Pools ● Methodology ● Case study ● Results ● Evaluation ● Conclusion 2

3. Introduction ● Ultra-Large-Scale (ULS) systems need high concurrency and speed. ● Analysing system performance is difficult ○ Time Consuming ○ Significant manual review of the data and logs ○ Lack of tool support, need heavy instrumentation ○ Hardware sensor gathered data hard to interpret. 3

4. Main contributions To analyze performance deviations in systems designed using thread pools.. 1. A top-down methodology for identifying and ranking the most deviating thread behaviour 1. A qualitative and quantitative evaluation of proposing methodology on a large-scale industrial ULS system 4

5. Thread Pools ● Advantages o Avoid thread create/destroy overhead o System become more responsive ● Difficulties o Too many threads → resource thrashing → performance degradation o Hard to configure and test o synchronization errors → idle threads/deadlocks o Thread leakage 5

6. Motivational Example ● Analyse the system for x5 large workload ● Machine level macro threads for each thread pool ● Identify majority and deviating behaviours by the use of clusters in dendrogram 6

7. Motivational Example(Cont) 7

8. Methodology 8

9. Methodology (cont.) A. Performance Data a. Resource usage metrics of the pooled resources eg: CPU, memory, #opened files a. Resource metrics - accuracy - overhead of performance monitoring - data redundancy 9

10. Methodology (cont.) B. Metric Abstraction a. Group threads into higher level (macro) abstractions by space or time. eg: in a cluster of machines, all pooled threads executing on one node aggregated into one. b. Identify the deviations at higher level c. Repeat methodology for deviations at lower level 10

11. Methodology (cont.) C. Distance Calculation Between Covariance Matrices To get level of dissimilarity, or distance, between two abstractions 11 Remember your Maths?

12. Methodology (cont.) Covariance metric 12 Covariance between X(n) and X(1) how much X(n) and X(1) variables change together Ref - Wikipedia(2014, Dec), Covarience Metrics. link: http://en.wikipedia.org/wiki/Covariance_matrix Variance of metric ( i )

13. Methodology (cont.) Eg: ● Metrics Xc,XM,XH(cpu, memory, #open files) for Threads A,B ● A is instrumented 100 times, B 1000 times ● Calculate distance metric (Forstner & Moonen method) one-dimensional distance for each pair of covariance matrices ● Distance value - Similarity 13

14. Methodology (cont.) D. Hierarchical Clustering ● Starts with each abstraction in its own cluster and proceeds to find and merge the closest pair of clusters ● Have used Ward's method of clustering 14ref -Large Scale Gene Expression Data Analysis I . link:http://compbio.uthsc.edu/microarray/lecture1.htm

15. Methodology (cont.) E. Cluster Visualization 15

16. Methodology (cont.) F. Ranking Clusters ● Recursive ● Top to bottom 16

17. Algorithm 17

18. Case Study ● Performance data ○ CPU, Virtual Bytes, Private Bytes, Handles, MicroThreads ● Metric Abstraction ● Hierarchical Clustering and Ranking 18

19. Results 19

20. Results (cont.) 20

21. Quantitative Evaluation ● Validating the ability of the methodology to identify and rank deviations ● Identified important deviations ● Injecting synthetical deviations in to the performance data ● Verify the methodology (precision/recall) 21

22. Quantitative Evaluation (cont.) 22

23. Conclusion ● A methodology for automatically identifying deviating behaviour in ULS systems ● Ranking most deviating thread behavior with different abstractions (wave, thread) ● It is possible to use this methodology for other applications 23

24. Thank You! 24

Editor's Notes

10 sec
15 sec
30 sec- ULS - data, connections, hardware , 300-400 items are sold per second, 13 minl trasactions per second need - near-perfect up-time, hundreds or even thousands of concurrent connections, speed Performance analysis- determine maximum operating capacity, validate non-functional requirements, cover bottlenecks hrs to days . jprofiler, j-test CPU/memory resource metrics very hard to interpret
30 sec Performance deviation in ULS systems that are designed using the thread pools architectural pattern. can be applied with very little understanding of the architecture even
4 min -a design pattern, technique for designing scalable multithreaded and distributed systems. - explain diagram -useful in server applications where work items are typically short-lived and the number of incoming work items is large Ad; -. - abling incoming work items to be assigned and executed quickly Dis resource thrashing- resources expended to manage the threads increases at the expense of the resources available to process the work items thread syncs issues - deadlock where all threads in the pool are waiting for a work item on the queue to be processed, but there are no "available" threads to process that work item Thread leakage - when a thread finishes processing its work item, but fails to return to the pool
1 min test system with newly integrated package tracking system - fast method top down aggregate each machine's thread data into one macro thread at the machine level,
1 min iterative, top-down methodology notice two groups of behaviour: on the threads of machine4 one thread for cluster competition for access to the database between machine4 and the other machines is affecting the access time
30
30 sec resource usage metrics of
1 min Top down apply method at high level abstractions
sample - http://stattrek.com/matrix-algebra/covariance-matrix.aspx every covariance matrix is symmetric covariance is a measure of how much two random variables change together. If the greater values of one variable mainly correspond with the greater values of the other variable, and the same holds for the smaller values, i.e., the variables tend to show similar behavior, the covariance is positive expected value of a random variable is intuitively the long-run average value of repetitions of the experiment it represents. For example, the expected value of a die roll is 3.5 because
1.30
1min
cluster visualization - Once we calculated hierarchical clustering, details cluster visualization would be helpful to manually identify any deviating behavior in a particular abstraction. clusters of abstractions are visualized using hierarchical cluster dendrograms.These are binary tree like diagrams that show each stage of the clustering procedure as nested clusters.The height of the dendrogram is proportional to the distance between clusters. Higher the height higher the deviation. 1 min
Ranking clusters - Once the hierarchical cluster dendrograms have been generated, they are analyzed to identify and rank the most deviating behaviour. The authors have formalized an algorithm from their experiences analyzing dendrograms. We can recognize some characteristics of the algorithm. recursive top to bottom min: 1
2 min
2 min Subject system: the case study is based on data of an industrial ultra-large-scale system. These system follow thread pool architecture. The threads of the thread pool manage their own micro-threds. But they are been ignored due to unavailability of metrics. Performance Data: The Data in this case study consists of five resource usage metrics.The metrics are sampled approximately every 7 seconds. CPU - Percentage of CPU time in use VirtualBytes - Amount of virtual address space in use PrivateBytes - Amount of private( non-shared) memory in use Handles - Number of open file handles MicroThreads - Number of allocated micro-threads Hierarchical Clustering The authors have used pvclust and R package for hierarchical clustering, to cluster abstractions. (R is a free software environment for statistical computing and graphics) For ranking clusters: need to use algorithm
0.5 min
1 min
To verify at the wave level need to use this, and to verify at the thread level can be used same set of equations 1min
1 min

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Editor's Notes