Final_Presentation_Docker_KP
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This document analyzes the performance of Docker containers running varying I/O intensive MySQL workloads. Key metrics like I/O entries, size, CPU utilization, and read/write ratios were collected from blktrace, blkparse, and iostat. The analysis found these metrics began saturating after 3 containers, indicating the default system cache was effectively handling redundant data between containers. While Docker performed well, a cache designed specifically for containers may further boost performance, especially with large numbers of identical containers.
Final_Presentation_Docker_KP
- 1. Analysis of Performance of Docker for Varying I/O Intensive Workloads Kaushik Padmanabhan Chander Mohan David Stewart Analysis of Performance of Docker for Varying I/O Intensive Workloads Kaushik Padmanabhan Chander Mohan David Stewart Analysis of Performance of Docker for Varying I/O Intensive Workloads Kaushik Padmanabhan Supervision of Professor, Chander Mohan Dr. Ningfang Mi David Stewart
- 2. Docker Alternative to Virtual Machine. Provides a way to run applications securely, isolated in a container, packaged with all its dependencies and libraries. A Docker possesses a Docker hub (Docker Registry) that hosts the images of applications which we want to run. Each image can have numerous containers holding homogenous instances of that particular application.
- 4. Difference Between VM-Ware and Docker
- 6. Abstract In this project, we are considering homogenous instances of a particular application in Docker containers under that particular application’s image, which can hold numerous records of data. Using this, we are going to evaluate the optimum number of containers under the same application’s image which is adversely affected by the number of records in numerous instances of an application within those containers.
- 7. Performance analysis tools ‘blktrace’ is a block layer IO tracing mechanism which provides detailed information about request queue operations up to user space. blktrace A utility which transfers event traces from the kernel into either long-term on- disk storage, or provides direct formatted output (via blkparse). blkparse A utility which formats events stored in files, or when run in live mode directly outputs data collected by blktrace. iostat Reports Central Processing Unit (CPU) statistics and input/output statistics for devices and partitions. tpmc Gives details on the number of transactions taken place in a database per minute.
- 8. Fig. 2: blktrace and blkparse
- 9. Our Method We are deploying MySQL images into the Docker containers, with TPCC benchmark. The motive of our project, To collect the blktrace and work on performance correlation of some features from blktrace. observe the optimal number of containers in terms of performance metrics. We have captured different features from blktrace for performance measurement for logical block addresses like, Frequency. Sequentiality. Block Size. Read/Write.
- 10. Benchmark Used TPC-C Benchmark tpcc is a write intensive test. We downloaded the tpcc benchmark (tpcc_mysql_master) to load and measure the performance of MySQL. tpcc_load – This will load the multiple records into the table without considering the manual loading. tpcc_start – This will start and run the database and measures the transactions happening between the connections mentioned.
- 11. Considerations 6 Containers and the image used is MySQL: 5.7 tpcc benchmark ran for, Container1: ramp up time – 900 secs and measure time 3600 secs Container2: ramp up time – 840 secs and measure time 3600 secs Container3: ramp up time – 780 secs and measure time 3600 secs Container4: ramp up time – 720 secs and measure time 3600 secs Container5: ramp up time – 660 secs and measure time 3600 secs Container6: ramp up time – 600 secs and measure time 3600 secs blktrace calculated for 1000 secs and iostat for 100 secs.
- 12. Process Flow We created containers and pulled MySQL images into the containers. We created database and imported ‘create_table.sql’ and ‘add_foreign_key.sql’ into the database. We ran the tpcc_load command to load the data into the tables in each container. We need ran the tpcc_start to start the execution of the tables. Simultaneously, we measured the performance of the containers using blktrace, blkparse and iostat commands.
- 13. Performance Metrics Considered Total I/O entries and size variation with respect to increasing Containers (blktrace, blkparse and iostat). CPU Utilization with respect to increasing Containers (iostat). Frequency of Logical Block Addressing with respect to increasing Containers (blktrace and blkparse). Sequential and Random Reads/Writes percentage variation with respect to increasing Containers (blktrace and blkparse). Read to Write ratio variation with respect to increasing Containers (blktrace and blkparse).
- 14. Total I/O Entries and Size comparison - blktrace 21291760 22986336 23130872 22681392 22737824 22608464 1 5000001 10000001 15000001 20000001 25000001 1 2 3 4 5 6 SizeinBytes No. of Containers Total I/O size 412859 440947 447577 431115 432504 431306 1 100001 200001 300001 400001 500001 1 2 3 4 5 6 NumberofI/OEntries No. of Containers Total number of I/O Entries
- 15. Total I/O Size and Service Time – iostat for 100sec 2.36 2.15 2.06 2.4 2.39 2.41 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 1 2 3 4 5 6 TimeinSeconds No. of Containers Service time 89964.4 97728.44 102075 99165.93 97461.64 97059.68 1 20001 40001 60001 80001 100001 120001 1 2 3 4 5 6 SizeinBytes No. of Containers Total I/O Size
- 16. Tpmc Metric – Transaction Rate 1320.733 1448.283 1459.48 1343.85 1342.8 1344.77 1 201 401 601 801 1001 1201 1401 1601 1 2 3 4 5 6 tpmcmetric No. of Containers Tpmc metric
- 17. Inferences Made Using blktrace The number of entries and the size saturate. Shows the I/O entries made (reads and writes) are saturating after running 3 containers simultaneously. Using iostat The same happened with respect to the size. When the Service Time (svctm) changes, taken by the I/O for performing the Read/Write, are considered (obtained using iostat), the saturation of the I/O size and entries produced can be easily explained. Using tpmc Analyzed tpmc metrics with increasing containers. The analysis reveals the saturation of tpmc after running 3 containers, which gives information on the saturation of I/O entries and size.
- 18. CPU Utilization - iostat 89.52 90.22 90.25 90.28 90.34 90.38 85 85.5 86 86.5 87 87.5 88 88.5 89 89.5 90 90.5 91 1 2 3 4 5 6 CPUUtilization% No. of Containers CPU Utilization
- 19. Inferences Made ▶ The CPU utilization rises with number of containers. ▶ More resources needed to manage container processes.
- 20. Frequency Comparison 0.783657375 0.728030806 0.683480161 0.635841945 0.606789764 0.58321 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 2 3 4 5 6 RatioofFrequencyofLBAused No. of Containers Frequency Ratio Logical Block Addressing (LBA) – refers to the location of blocks of data stored on computer storage devices.
- 21. Inferences Made The frequency of LBA reuse on secondary storage device decreases. Means less LBA hits in Secondary Storage Device. This means cache is being utilized effectively. Decrease in frequency means equivalent rise in cache hits when more containers use redundant data.
- 22. Sequential I/O Comparison 0.260297099 0.2580635 0.255705722 0.248108 0.24633 0.23497 0.22 0.225 0.23 0.235 0.24 0.245 0.25 0.255 0.26 0.265 1 2 3 4 5 6 SequentialRead/WriteRatio No. of Containers Sequential I/O Ratio 9670976 9980648 9937348 9897348 9874776 9834956 9500000 9600000 9700000 9800000 9900000 10000000 10100000 1 2 3 4 5 6 SequentialI/OSize(Bytes) No. of Containers Sequential I/O Size
- 23. Randomness I/O Comparison 0.739702901 0.748448226 0.752294278 0.754991128 0.758018441 0.7650271 0.725 0.73 0.735 0.74 0.745 0.75 0.755 0.76 0.765 0.77 1 2 3 4 5 6 RandomRead/WriteRatio No. of Containers Random I/O Ratio 11620752 13005656 13233456 13299685 13363032 13417000 10500000 11000000 11500000 12000000 12500000 13000000 13500000 14000000 1 2 3 4 5 6 RandomI/OSize(Bytes) No. of Containers Random I/O Size
- 24. Inferences Made Due to pre-fetch policy in the cache management, the number of sequential accesses from the secondary storage device reduce. That leads to increase in the random accesses from the secondary storage device at the same point.
- 25. Read to Write Ratio 0.000475 0.0007309 0.00147 0.00204322 0.00212 0.003018 0 0.0005 0.001 0.0015 0.002 0.0025 0.003 0.0035 1 2 3 4 5 6 Read/Writeratio No. of Containers Read/Write
- 26. Read Comparison 0.0004798 0.0007302 0.001467904 0.002047 0.00219 0.00302 0 0.0005 0.001 0.0015 0.002 0.0025 0.003 0.0035 1 2 3 4 5 6 ReadRatio No. of Containers Read Ratio
- 27. Write Comparison 0.999520417 0.999269754 0.998532096 0.9979524 0.99781475 0.996533 0.995 0.9955 0.996 0.9965 0.997 0.9975 0.998 0.9985 0.999 0.9995 1 1 2 3 4 5 6 WriteRatio No. of Containers Write Ratio
- 28. Inferences Made ▶ Since the data has already been written for previous containers, if we increase the containers, the read will be more than writes. ▶ As the containers increase, there is a utilization of cache memory present between mysql containers and hard drive resulting in decrease of actual writes to secondary memory. ▶ The reads increase though due to cache misses which leads to increased random accesses in the secondary storage device.
- 29. Conclusion Default system cache does good job. The performance of Docker containers are measured with increasing containers for different metrics. It is found that different metrics have affected differently A cache designed just for containers as seen in slacker paper may boost performance more. It may be helpful especially in large number of containers of same image. Future Work
- 30. Docker Project Files GitHub: https://github.com/KaushikKPDARE/DockerPro https://docs.docker.com/engine/getstarted/ https://docs.docker.com/engine/installation/linux/ubuntulinux/ https://docs.docker.com/engine/userguide/ References