Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
Performance Stability of Public Clouds
Search
xLeitix
April 03, 2019
Research
1
67
Performance Stability of Public Clouds
Talk given at VECS (automotive industry conference in Gothenburg)
xLeitix
April 03, 2019
Tweet
Share
More Decks by xLeitix
See All by xLeitix
2024_uzh_collo.pdf
xleitix
0
24
CrossFit: Fine-Grained Benchmarking of Serverless Application Performance Across Cloud Providers
xleitix
0
210
Unit testing performance using code microbenchmarks - how far are we?
xleitix
0
370
Developer-Targeted Performance Engineering (ZHAW Colloquium)
xleitix
0
71
Developer-Targeted Performance Engineering
xleitix
0
210
Cachematic – Automatic Invalidation in Application-Level Caching Systems
xleitix
0
110
AWS Lambda and #serverless. What’s all the fuzz about?
xleitix
1
570
Performance Testing in a Public Cloud - How bad is it really?
xleitix
0
430
Performance Testing of and in the Cloud
xleitix
1
380
Other Decks in Research
See All in Research
クラウドのテレメトリーシステム研究動向2025年
yuukit
3
810
データサイエンティストの採用に関するアンケート
datascientistsociety
PRO
0
160
コーパスを丸呑みしたモデルから言語の何がわかるか
eumesy
PRO
11
3.4k
言語モデルによるAI創薬の進展 / Advancements in AI-Driven Drug Discovery Using Language Models
tsurubee
2
280
ノンパラメトリック分布表現を用いた位置尤度場周辺化によるRTK-GNSSの整数アンビギュイティ推定
aoki_nosse
0
250
ラムダ計算の拡張に基づく 音楽プログラミング言語mimium とそのVMの実装
tomoyanonymous
0
440
サーブレシーブ成功率は勝敗に影響するか?
vball_panda
0
600
3D Gaussian Splattingによる高効率な新規視点合成技術とその応用
muskie82
0
310
(NULLCON Goa 2025)Windows Keylogger Detection: Targeting Past and Present Keylogging Techniques
asuna_jp
1
330
eAI (Engineerable AI) プロジェクトの全体像 / Overview of eAI Project
ishikawafyu
0
430
移動ビッグデータに基づく地理情報の埋め込みベクトル化
tam1110
0
290
プロシェアリング白書2025_PROSHARING_REPORT_2025
circulation
1
300
Featured
See All Featured
Improving Core Web Vitals using Speculation Rules API
sergeychernyshev
12
630
Building a Modern Day E-commerce SEO Strategy
aleyda
39
7.2k
The Art of Programming - Codeland 2020
erikaheidi
53
13k
Save Time (by Creating Custom Rails Generators)
garrettdimon
PRO
30
1.1k
How to Think Like a Performance Engineer
csswizardry
22
1.5k
Responsive Adventures: Dirty Tricks From The Dark Corners of Front-End
smashingmag
251
21k
個人開発の失敗を避けるイケてる考え方 / tips for indie hackers
panda_program
102
19k
Bash Introduction
62gerente
611
210k
Design and Strategy: How to Deal with People Who Don’t "Get" Design
morganepeng
129
19k
Code Review Best Practice
trishagee
67
18k
Thoughts on Productivity
jonyablonski
69
4.5k
Music & Morning Musume
bryan
46
6.4k
Transcript
Performance Stability of (Public) Clouds Philipp Leitner philipp.leitner@chalmers.se @xLeitix
Chalmers !2 Cloud Computing Image Credit: https://www.networkworld.com/article/3195527/did-cloud-kill-backup.html
Chalmers !3 Some disclaimers before we get started …. Image
Credit: https://thenounproject.com/term/exclamation-mark/
Chalmers !4 Image Credit: https://nordicapis.com/living-in-the-cloud-stack-understanding-saas-paas-and-iaas-apis/
Chalmers !5 Cloud Usage in Automotive Source (Accenture): https://www.accenture.com/t20150914T170053__w__/us-en/_acnmedia/Accenture/Conversion-Assets/DotCom/Documents/Global/PDF/ Industries_18/Accenture-Cloud-Automative-PoV.pdf
Chalmers !6 Cloud Usage in Automotive Source (Accenture): https://www.accenture.com/t20150914T170053__w__/us-en/_acnmedia/Accenture/Conversion-Assets/DotCom/Documents/Global/PDF/ Industries_18/Accenture-Cloud-Automative-PoV.pdf
Chalmers !7 (One) Challenge for Cloud Adoption in Automotive: Predictability
Image Credit: http://chittagongit.com
Chalmers !8 Predictability Do I know what I will get?
Do I get the same thing every time? Image Credit: http://chittagongit.com
Chalmers !9 Aside: Cloud Instance Types (“flavors”) Image Credit (Rightscale):
https://www.rightscale.com/about-cloud-management/cloud-cost-optimization/cloud-pricing-comparison
Chalmers !10 Predictability Inter-Instance Intra-Instance
Chalmers !11 Predictability Inter-Instance Intra-Instance
Chalmers !12 Predictability Inter-Instance Intra-Instance
Chalmers !13 Source (Leitner and Cito): https://arxiv.org/pdf/1411.2429.pdf
Chalmers !14 Relative Standard Deviations Benchmarks of identical instances Source
(Leitner and Cito): https://arxiv.org/pdf/1411.2429.pdf (anno ~ 2015)
Chalmers !15 Recent Results (unpublished data) (Feb 2019) 2015
Chalmers !16 Instance Runtime (unpublished data) (Feb 2019) 2015 3.5
4.0 4.5 5.0 5.5 0 20 40 60 Benchmark Runtime [h] Benchmark Value Continuous io azure D2s
Chalmers !17 Changes Over the Years (mean of all measurements)
Chalmers !18 2015 2019 CPU 8.1 3.6 - 55% Changes
Over the Years (mean of all measurements)
Chalmers !19 2015 2019 CPU 8.1 3.6 - 55% MEM
12.6 6.5 - 48% Changes Over the Years (mean of all measurements)
Chalmers !20 2015 2019 CPU 8.1 3.6 - 55% MEM
12.6 6.5 - 48% IO 38.6 15.9 - 59% Changes Over the Years (mean of all measurements)
Chalmers !21 What has changed?
Chalmers !22 For IO: multi-tenancy For CPU: hardware heterogeneity Traditional
Reasons for Lack of Predictability
Chalmers !23 Reason 0: More commitment to predictable performance levels
and transparency
Chalmers !24 Reason 1: Move towards guaranteed hardware
Chalmers !25 (anno ~ 2015) Heterogenous Hardware? Source (Leitner and
Cito): https://arxiv.org/pdf/1411.2429.pdf
Chalmers !26 (anno ~ 2015) Heterogenous Hardware? Source (Leitner and
Cito): https://arxiv.org/pdf/1411.2429.pdf (now) (Largely) guaranteed hardware
Chalmers !27 Reason 2: Move towards SLAs and credit systems
over best-effort delivery
Chalmers !28 (anno ~ 2015) Best-Effort Delivery? Source (Leitner and
Cito): https://arxiv.org/pdf/1411.2429.pdf
Chalmers !29 (anno ~ 2015) Best-Effort Delivery? Source (Leitner and
Cito): https://arxiv.org/pdf/1411.2429.pdf (unpublished data) (now) 0 5 10 15 20 25 0 50 100 150 200 Benchmark Runtime [h] Benchmark Value c5−large / IO
Chalmers !30 Credit Models - General Idea Resources are distributed
fairly between tenants based on usage tokens Available for: CPU (in case of shared CPU instance types) IO (some providers)
Chalmers !31 Credit Models at Runtime Source (Leitner and Scheuner):
https://www.zora.uzh.ch/id/eprint/112940/
Chalmers !32 Summary Public clouds are not all that unpredictable
(anymore)
Chalmers !33 Summary Public clouds are not all that unpredictable
(anymore) … useful even for workloads sensitive to performance variation … but it’s still virtualized infrastructure
Chalmers !34 Summary Public clouds are not all that unpredictable
(anymore) New developments have changed the game: Specialized hardware, credit models, provisioned IOPS
Chalmers !35 Cloud Workbench Tool for scheduling cloud experiments Code:
https://github.com/sealuzh/cloud-workbench Demo: https://www.youtube.com/watch? v=0yGFGvHvobk
Chalmers !36 Questions? Source: https://dilbert.com/strip/2008-05-08