![Page 1: Leveraging Serverless Computing to Improve Performance for …faculty.washington.edu/wlloyd/slides/Parbio2019_slides.pdf · 2019-10-11 · Leveraging Serverless Computing to Improve](https://reader033.vdocuments.mx/reader033/viewer/2022042308/5ed4724b3fb4881c434b0597/html5/thumbnails/1.jpg)
Leveraging ServerlessComputing to Improve Performance for Sequence Comparison
Xingzhi Niu, Dimitar Kumanov, Ling-Hong Hung, Wes Lloyd, Ka Yee YeungSchool of Engineering and Technology, University of Washington TacomaSept 7, 2019
![Page 2: Leveraging Serverless Computing to Improve Performance for …faculty.washington.edu/wlloyd/slides/Parbio2019_slides.pdf · 2019-10-11 · Leveraging Serverless Computing to Improve](https://reader033.vdocuments.mx/reader033/viewer/2022042308/5ed4724b3fb4881c434b0597/html5/thumbnails/2.jpg)
> Introduction on serverless computing
> Protein sequence alignment as an example
> Serverless pipeline architecture
> Experiment and results
> Cloud provider comparison: AWS vs. Google
> Summary
Outline
2
![Page 3: Leveraging Serverless Computing to Improve Performance for …faculty.washington.edu/wlloyd/slides/Parbio2019_slides.pdf · 2019-10-11 · Leveraging Serverless Computing to Improve](https://reader033.vdocuments.mx/reader033/viewer/2022042308/5ed4724b3fb4881c434b0597/html5/thumbnails/3.jpg)
Why Serverless Computing
Pay only for
actual run time
High Availability
No Setup
Scalability
Memory
Running Time
Disk
Languages
Code Size
Advantages Limitations
3
![Page 4: Leveraging Serverless Computing to Improve Performance for …faculty.washington.edu/wlloyd/slides/Parbio2019_slides.pdf · 2019-10-11 · Leveraging Serverless Computing to Improve](https://reader033.vdocuments.mx/reader033/viewer/2022042308/5ed4724b3fb4881c434b0597/html5/thumbnails/4.jpg)
FaaS Platforms
AWS Lambda
Azure Functions
IBM Cloud Functions
Google Cloud Functions
Fn (Oracle)
Apache OpenWhiskOpen Source
Commercial
4
![Page 5: Leveraging Serverless Computing to Improve Performance for …faculty.washington.edu/wlloyd/slides/Parbio2019_slides.pdf · 2019-10-11 · Leveraging Serverless Computing to Improve](https://reader033.vdocuments.mx/reader033/viewer/2022042308/5ed4724b3fb4881c434b0597/html5/thumbnails/5.jpg)
> gap penalty Wk = uk + v with u=10, v=1
> u gap extension penalty
> v gap opening penalty
> scoring matrix (BLOSUM50) by default
Smith-Waterman (Dynamic Programming)
O(n3)From:https://en.wikipedia.org/wiki/Smith%E2%80%93Waterman_algorithmIdentification of Common Molecular Subsequences, Smith and Waterman, 1981
5
![Page 6: Leveraging Serverless Computing to Improve Performance for …faculty.washington.edu/wlloyd/slides/Parbio2019_slides.pdf · 2019-10-11 · Leveraging Serverless Computing to Improve](https://reader033.vdocuments.mx/reader033/viewer/2022042308/5ed4724b3fb4881c434b0597/html5/thumbnails/6.jpg)
> Smith-Waterman Algorithm– 2007 Farrar Striped Algorithm (SSW library):
https://github.com/mengyao/complete-striped-smith-waterman-library
Striped Smith-Waterman speeds database searches six times over other SIMD implementations, Farrar, 2007
> Partition 20,336 unique human protein sequences into 41 subsets
> 861 pairwise comparison tasks (41 * 40 / 2 + 41)
Protein Sequence Alignment
6
![Page 7: Leveraging Serverless Computing to Improve Performance for …faculty.washington.edu/wlloyd/slides/Parbio2019_slides.pdf · 2019-10-11 · Leveraging Serverless Computing to Improve](https://reader033.vdocuments.mx/reader033/viewer/2022042308/5ed4724b3fb4881c434b0597/html5/thumbnails/7.jpg)
Our Serverless Architecture
7
![Page 8: Leveraging Serverless Computing to Improve Performance for …faculty.washington.edu/wlloyd/slides/Parbio2019_slides.pdf · 2019-10-11 · Leveraging Serverless Computing to Improve](https://reader033.vdocuments.mx/reader033/viewer/2022042308/5ed4724b3fb4881c434b0597/html5/thumbnails/8.jpg)
> Corresponding components across cloud providers
Serverless Pipeline Components
8
![Page 9: Leveraging Serverless Computing to Improve Performance for …faculty.washington.edu/wlloyd/slides/Parbio2019_slides.pdf · 2019-10-11 · Leveraging Serverless Computing to Improve](https://reader033.vdocuments.mx/reader033/viewer/2022042308/5ed4724b3fb4881c434b0597/html5/thumbnails/9.jpg)
Configurations Tested (one client thread)
> Local Laptop client w/&w/o both FaaS providers
> AWS VM client w/&w/o AWS Lambda
> Google VM client w/&w/o Google Cloud Functions
Experiment: Benchmarking Different Clients
Client OS Memory
Local Laptop Ubuntu 16.04 4GB
AWS m5.2xlarge Ubuntu 18.04 32GB
Google n1-standard-8 Ubuntu 18.04 30GB
FaaS Platform Memory Timeout
AWS Lambda 2GB 540s
Google Cloud Functions 2GB 540s
9
![Page 10: Leveraging Serverless Computing to Improve Performance for …faculty.washington.edu/wlloyd/slides/Parbio2019_slides.pdf · 2019-10-11 · Leveraging Serverless Computing to Improve](https://reader033.vdocuments.mx/reader033/viewer/2022042308/5ed4724b3fb4881c434b0597/html5/thumbnails/10.jpg)
Execution Time (Speedup)
Client Type AWS Google
Laptop w/o serverless 8h:42m:0s (1x)
Cloud VM w/o serverless 4h:17m:16s (2.0x) 5h:2m:25s (1.7x)
Laptop + serverless 0h:2m:32s (206.1x) 0h:11m:49s (44.2x)
Cloud VM + serverless 0h:1m:17s (406.8x) 0h:11m:6s (47.0x)
10
![Page 11: Leveraging Serverless Computing to Improve Performance for …faculty.washington.edu/wlloyd/slides/Parbio2019_slides.pdf · 2019-10-11 · Leveraging Serverless Computing to Improve](https://reader033.vdocuments.mx/reader033/viewer/2022042308/5ed4724b3fb4881c434b0597/html5/thumbnails/11.jpg)
Price Comparison
Client Type AWS Google
Laptop w/o serverless N/A
Cloud VM w/o serverless $1.65 $1.82
Laptop + serverless $0.85 $0.76
Cloud VM + serverless $0.89 $0.79
11
![Page 12: Leveraging Serverless Computing to Improve Performance for …faculty.washington.edu/wlloyd/slides/Parbio2019_slides.pdf · 2019-10-11 · Leveraging Serverless Computing to Improve](https://reader033.vdocuments.mx/reader033/viewer/2022042308/5ed4724b3fb4881c434b0597/html5/thumbnails/12.jpg)
AWS vs. Google Comparison
Benchmarking metrics AWS Google
Average Function Run Time 76s 67s
Total Invocation Time 1.4s 599s
Maximum Deployment size 50MB 500MB, <100MB each file
Deployment time <2s ~2min
12
![Page 13: Leveraging Serverless Computing to Improve Performance for …faculty.washington.edu/wlloyd/slides/Parbio2019_slides.pdf · 2019-10-11 · Leveraging Serverless Computing to Improve](https://reader033.vdocuments.mx/reader033/viewer/2022042308/5ed4724b3fb4881c434b0597/html5/thumbnails/13.jpg)
Details on Invocation Rate
13
-100
0
100
200
300
400
500
600
700
800
900
1000
-50 50 150 250 350 450 550 650
invo
ked
time(s)
Invoked functions after specific time
Google AWS
AWS
![Page 14: Leveraging Serverless Computing to Improve Performance for …faculty.washington.edu/wlloyd/slides/Parbio2019_slides.pdf · 2019-10-11 · Leveraging Serverless Computing to Improve](https://reader033.vdocuments.mx/reader033/viewer/2022042308/5ed4724b3fb4881c434b0597/html5/thumbnails/14.jpg)
> Leveraging serverless computing to improve sequence comparison workflows
> Experiments on Smith-Waterman algorithm with AWS and Google platform
> The advantage on both speed and price of serverless computing
> Comparison between two serverless providers: AWS and Google
Summary
14
![Page 15: Leveraging Serverless Computing to Improve Performance for …faculty.washington.edu/wlloyd/slides/Parbio2019_slides.pdf · 2019-10-11 · Leveraging Serverless Computing to Improve](https://reader033.vdocuments.mx/reader033/viewer/2022042308/5ed4724b3fb4881c434b0597/html5/thumbnails/15.jpg)
Questions
Supported by NIH R01GM126019.