ppbench - a visualizing network benchmark for microservices

ppbench A Visualizing Network Benchmark for Microservices

Nane Kratzke and Peter-Christian Quint

1 Prof. Dr. rer. nat. Nane Kratzke Computer Science and Business Information Systems

The next 15 to 20 minutes are about ...

•  The cloud-native application stack and microservices, containers and container clusters (Docker Swarm, Mesos, Kubernetes, etc.)

•  Possible technical and performance impacts of above mentioned approaches

•  A benchmarking tool for figuring out performance impacts in the upfront of a microservice design.

•  Some evaluation results of ppbench. •  Some performance insights you might not know about Ruby, Java,

Go, Dart, Docker (container) and overlay networks like Weave!

Prof. Dr. rer. nat. Nane Kratzke Computer Science and Business Information Systems 2

Microservices


„The microservice architectural style is an approach to developing a single application as a suite of small services, each running in its own process and communicating with lightweight mechnisms, often an HTTP resource API.“

Martin Fowler

The Cloud-Native Stack (ClouNS)


(1) Different Programming Languages

(2) Overlay Networks

(3) Container

(4) VM Types

However, there is missing a tool ....


to figure out possible impacts of z  different programming languages

z  overlay networks

z  additional container layer

z  different VM types

z  ...

in the upfront of a microservice design.

Reference Experiment


Experiment to measure reference network performance of REST-like HTTP-based communication protocols.

Ping and pong services are implemented in different TIOBE TOP 50 programming languages.

And we get visualizations ...


Data Transfer Rates(bigger is better)

Message Size (kB)

Tran

sfer

Rat

e (M

B/se

c)

0 kB 50 kB 150 kB 250 kB 350 kB 450 kB

0 M

B/se

c16

MB/

sec

32 M

B/se

c48

MB/

sec

64 M

B/se

c80

MB/

sec

● bare on m3.2xlarge For identifying -  problematic areas -  skewness of distributions -  across a wide range of

message sizes

In that case. Dart‘s http standard libraries seem to have problems around message sizes bigger than three times TCP standard receive window (dotted lines).

Skewness is shifted to bottom (not preferable).

Skewness is shifted to top (preferable).

Impact of Containers


Experiment to measure container impact on network performance of REST-like HTTP-based communication protocols.

Impact of Software Defined Networks


Experiment to measure additional SDVN impact on network performance of REST-like HTTP-based communication protocols.

weave

Language Impact


Data Transfer Rates(bigger is better)

Message Size (kB)

Tran

sfer

Rat

e (M

B/se

c)

0 kB 50 kB 150 kB 250 kB 350 kB 450 kB

0 M

B/se

c22

MB/

sec

44 M

B/se

c66

MB/

sec

88 M

B/se

c

●

●

●

●

Go (Exp. P1) on m3.2xlargeJava (Exp. P2) on m3.2xlargeRuby (Exp. P3) on m3.2xlargeDart (Exp. P4) on m3.2xlarge

Requests per second in relative comparison(bigger is better)

Message Size (kB)

Rat

io (%

)0kB 50kB 150kB 250kB 350kB 450kB

0%20

%40

%60

%80

%11

0%14

0%17

0%20

0% ●

●

●

●

Reference: Go (Exp. P1) on m3.2xlargeJava (Exp. P2) on m3.2xlargeRuby (Exp. P3) on m3.2xlargeDart (Exp. P4) on m3.2xlarge

(1) Different Programming Languages

(1) Language impact should not be undererstimated

(2) Do not overestimate Go.

Overlay Network Impact


Data transfer in relative comparison(bigger is better)

Message Size (kB)

Rat

io (%

)

0kB 50kB 150kB 250kB 350kB 450kB

20%

50%

80%

120%

●

●

●

Reference (Exp. S1, S3) on m3.largeGo, SDN deployed (Exp. S2 compared with S1) on m3.largeRuby, SDN deployed (Exp. S4 compared with S3) on m3.large

(2) Overlay Networks

(1) OK, SDN decreases performance. Not astonishing.

(2) But, SDN can sometimes improve performance!

weave

Container Impact is likely to be hardly measurable in real world scenarios


Round−trip latency in relative comparison(smaller is better)

Message Size (kB)

Rat

io (%

)

0kB 50kB 150kB 250kB 350kB 450kB

90%

110%

130%

●

●

●

Reference on m3.xlarge (Exp. C1/C3)Dockerized Go (Exp. C2 compared with C1) on m3.xlargeDockerized Dart (Exp. C4 compared with C3) on m3.xlarge

(3) Container

(1) Container impact might be language dependent.

(2) Container impact is most of the times below 10%.

Overlay Network and VM Type Language impact is more severe than SDN


Data transfer in relative comparison(bigger is better)

Message Size (kB)

Rat

io (%

)

0kB 50kB 150kB 250kB 350kB 450kB

40%

60%

80%

100%

120%

●

●

●

●

Reference: (Exp. V1, V3, V5)SDN loss on m3.large (2−core, V2 compared with V1)SDN loss on m3.xlarge (4−core, V4 compared with V3)SDN loss on m3.2xlarge (8−core, V6 compared with V5)

(4) VM Types

Decreasing loss with increase of cores due to a reduction of CPU contention effects.

(1)  SDN deployed on high core machines decreases performance down to 80% - 90%.

(2)  That is far less than a potential language impact (40% - 190%).

Conclusion


•  ppbench is a benchmarking tool designed to be used in the upfront of cloud-native application design processes.

•  ppbench is provided via GitHub: https://github.com/nkratzke/pingpong

•  You can run arbitrary experiments with ppbench to inspect possible performance impacts in the upfront of a service design/implementation.

•  We presented some evaluation results of ppbench •  SDN should always be used with high core machine types •  Programming Language might be more performance

relevant than a SDN

Acknowledgement

•  Stonehenge: Wikipedia, http://pt.wikipedia.org/wiki/Stonehenge •  Soldier: Pixabay (CC0 Public Domain) •  Microservices: Robert Morschel, http://www.soa-probe.com/2015/03/microservices-

summary.html


This study was funded by German Federal Ministry of Education and Research (Project Cloud TRANSIT, 03FH021PX4). The author thanks Lübeck University (Institute of Telematics) and fat IT solution GmbH (Kiel) for their support of Cloud TRANSIT.

Picture Reference

About


Nane Kratzke

CoSA: http://cosa.fh-luebeck.de/en/contact/people/n-kratzke

Blog: http://www.nkode.io

Twitter: @NaneKratzke

GooglePlus: +NaneKratzke

LinkedIn: https://de.linkedin.com/in/nanekratzke

GitHub: https://github.com/nkratzke

ResearchGate: https://www.researchgate.net/profile/Nane_Kratzke

SlideShare: http://de.slideshare.net/i21aneka

ppbench - a visualizing network benchmark for microservices

Technology