Threads and ConcurrencyIdentifying Performance Deviations in Thread Pools

PRESENTED BY

158217G - PUSHPALANKA JAYAWARDHANA

158248C - SALIYA SAMARAWICKRAMA

Mark D. Syer, Bram Adams and Ahmed E. Hassan

Reference:M. D. Syer, B. Adams, and A. E. Hassan,"Identifying performance deviations in thread pools," in Proceedings of the International Conference on Software Maintenance,

sep 2011, pp. 83-92

Line Up

Objective

Thread pools

Performance deviations in thread pools

Methodology

Related work, results& limitations

Hiding the thread complexity

Summary

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Objective

Introduce a methodology to identify threads with

performance deviations in thread pools.

Significance

ULS systems vastly use thread pool pattern in high concurrent implementations

Isolating the deviating threads need immense effort

source: http://farm3.static.flickr.com/2275/2153328067_2aeda0e4d2_m.jpg

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Thread Pools What is a thread pools?

Benefits of thread pools

Thread creation/destroying overhead reduced

Improved responsiveness

Issues related to thread pools

Idle threads

Dead locks

Thread leakages

Get the suitable tuning

No control over the state and priority of the thread

source: https://www10.informatik.uni-erlangen.de/~buildscript/pe/trunk/doc/html/threadpool.png

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Performance Deviations in Thread

Pools Why is it hard to identify deviations?

No detailed log entries

Require thorough knowledge on system architecture

Hard to detect concurrency issues

Hard to track threads in the pool in isolation

Lacking tool support

Require heavy instrumentation

Hard to interpret hardware sensor gathered data

source: http://connect.greenbeacon.com/wp-content/uploads/2014/12/tracker.jpg, http://blog.infostruction.com/wp-content/uploads/2010/11/networksecurity.jpg

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Method to Identify

Iterative, top down approach

Apply statistical analysis on resource usage logs

Measure dissimilarity between resource usage

Isolate the most deviating abstractions

source: http://static2.xlstat.com/images/content/home-slides-03.jpg.pagespeed.ce.vt3YFEFWQF.jpg

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Method in Detail

► More resource usage metrics► More accurate understanding on behavior

► Higher level abstractions► Pooled threads in one machine

► Threads created in one hour slot

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Method in Detail

Click to add text

►Why Co-variance matrix?► Robust to noise in the performance data

► Factor out time

► compare load tests of deviating lengths

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Co-variance measures

A measure of how much two random variables change together

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CPUMem

Co-variance Ctd.. 10

M C H

source: http://images.wisegeek.com/black-desktop-computer.jpg, http://stattrek.com/matrix-algebra/covariance-matrix.aspx

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Forstner and Moonen Distance

Matrix

Method in Detail

Click to add text

► Merge the least distant abstraction to cluster

► Ward's method - least distance between cluster centroids after merge► Stop when all in one cluster made

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Method in Detail

Click to add text

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Method in Detail

► Recursively improve the dendrogram via an algorithm

► Stops when clusters are in good fit► Isolate the deviations

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Ranking Algorithm 14

Case Study - Background

Qualitatively evaluate the deviated threads

Calculate the precision & recall value

Load test using synthetically injected deviations

System

ULA system in e-commerce domain

Time duration 16 hours

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Case Study

Resource Measured

CPU CPU time usage as %

Virtual Bytes Virtual address space usage

Private Bytes Private memory usage

Handles # of open file handles

Micro Thread # allocated micro threads

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Sampled every 7 seconds

Each has

Thread ID

Time

Value of each resource usage

Case Study 17

Thread pool contains 40 threads

Each process one work item

Each item completion time two hours

Threads with identical life time grouped

Group called a “Wave”

Wave length 2 hours and 5 minutes

Observe 8 Waves

Using pvclust , hierarchical clustering done

Ranking done as per slide

Results - Qualitative 18

Results of first wave Ranking of waves

Results - Qualitative 19

T1W1 in solid line

T1W8 in dotted line

T1W5 in dashed line

Virtual memory allocation

Results - Qualitative 20

T1W1 in detail

Results – Quantitative 21

Results Summary & Limitations

High precision

Identify IO & memory leaks

identify CPU spikes

This evaluated has been done using a single software system, so this results may differ for

other systems…

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Summary

Threads vs thread pools

Benefits & risks related to thread pools

Light weight statistical approach to detect risks

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Thank You..!

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Hiding Complexity

Android handler

AsyncTask

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OptionalAn Experience On Multithreading Using Android's Handler Class

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Qualitative Evaluation

Selecting the abstractions to visualize based on ranking

Normalizing the time scale

Under sampling the metrics by averaging every two minutes to remove single-point anomalies and noise.

Plotting the under sampled metrics.

Plotting additional details, such as the average and standard

deviation, as needed. The

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Results – Average of All Test 28