Feedback on AWS re:invent 2016

•

3 recomendaciones•1,462 vistas

Laurent Bernaille

Overview of the new services announced at re:invent

Ingeniería

Reinvent
2016

•  Monday:
gameday
/
hackaton

•  Tuesday:
sessions
/
Keynote
by
Hamilton

•  Wednesday:
Keynote
by
Jassy,
sessions

•  Thurdsay:
Keynote
by
Vogels,
sessions

•  Friday:
sessions

•  30000
persons

•  400+
sessions

•  4
places

•  5
days
(instead
of
4)

Lightsail

us-‐east
for
the
moment

Shadow
VPC
(peering
possible)

New
instances

C5:
Skylake,
up
to
72
vCPU
/
144GB
RAM

IPv6
support
for
ec2

•  Instances
can
have
publicly
routable
ipv6
address

•  Dual
stack
instances
(ipv4
+
ipv6)

•  To
create
private
subnets

Ø New
resource:
Egress-‐only
Internet
Gateway

Opsworks
for
chef
automate

Managed
chef
server

Chef
automate
suite

Personal
Health
dashboard

Personalized
view
of
status

Available
via
API

Can
send
noGﬁcaGons

EC2
system
manager

Extension
of
Run
Command
and
Simple
System
Management

Integrated
with
AWS
conﬁg

Athena

Process
S3
data
with
SQL

Uses
Presto

5$
/
TB
processed

Glue

Create
data
sources

Generate
transformaGons
(python)

Run:
scheduled
or
event-‐based

Uses
Spark
under
the
hood

Batch

HPC
as
as
service

Compute
env
(“grids”)

On-‐demand
or
spot

Queues
to
submit
jobs

Code
Build

Fully
managed
CI

Priced
per
build-‐minutes

Integrated
with
CodePipeline

Lex

Natural
language
understanding

Based
on
tech
powering
Alexa

X-‐Ray

APM
soluGon

Analyze
call
graph

View
call
latencies

VMWare
on
AWS

Pat
Gelsinger
on
stage

with
Jassy

AWS
Snowmobile

100
PB

350KW

1Tb/s
(Nx40Gb/s)

“We’re
gonna
need
a
bigger
box”

Pinpoint

Send
targeted
noGﬁcaGons

Real
Gme
analyGcs:
engagement,
moneGzaGon,
demographics

Greengrass

Run
lambda
code
on
IOT
devices

Snowball
edge

Bigger
snowball:
100TB

Horizontal
scaling
via
clustering

In
addiGon
to
s3
import/export,
local
storage
+
compute

Ø  Process
data
using
lambda

Lambda
edge

Run
lambda
code
on
Cloudfront
edges

Limited
capabiliGes

Ø  50ms
maximum

Ø  No
access
to
external
services

Use
cases:
redirect
/
modify
process
headers
and
cookies

Lambda
Step
funcZons

Coordinate
lambda
funcGons

Ø  Sequences

Ø  Parallel
processing

Ø  Branch
based
on
data

Ø  Synchronize

Ø  Manage
errors
and
retries

Conclusion

•  More
compeZZon
in
the
cloud

Ø  AWS
is
developing
some
services
others
had
before
them

•  AWS
now
talks
“Hybrid”
which
is
very
new

•  Not
many
news
in
mobile
/
containers
/
database

•  More
and
more
high-‐level
services

•  AWS
goes
on
the
market
of
soeware
vendors

Ø  APM,
Grid,
CI,
ETL

•  Big
push
in
AI

•  Many
things
around
Lambda

The
amount
of
new
services
this
year
is
crazy

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Kube-proxy enables access to Kubernetes services (virtual IPs backed by pods) by configuring client-side load-balancing on nodes. The first implementation relied on a userspace proxy which was not very performant. The second implementation used iptables and is still the one used in most Kubernetes clusters. Recently, the community introduced an alternative based on IPVS. This talk will start with a description of the different modes and how they work. It will then focus on the IPVS implementation, the improvements it brings, the issues we encountered and how we fixed them as well as the remaining challenges and how they could be addressed. Finally, the talk will present alternative solutions based on eBPF such as Cilium.

Evolution of kube-proxy (Brussels, Fosdem 2020)

Laurent Bernaille

The Kubernetes audit logs are a rich source of information: all of the calls made to the API server are stored, along with additional metadata such as usernames, timings, and source IPs. They help to answer questions such as “What is overloading my control plane?” or “Which sequence of events led to this problematic situation?”. These questions are hard to answer otherwise—especially in large clusters. At Datadog, we have been running clusters with 1000+ nodes for more than a year and during that time, the audit logs have proved invaluable. In this presentation, we will first introduce the audit logs, explain how they are configured, and review the type of data they store. Finally, we will describe in detail several scenarios where they have helped us to diagnose complex problems.

Making the most out of kubernetes audit logs

Laurent Bernaille

Running large Kubernetes clusters is difficult. Datadog has been running large-scale Kubernetes clusters (thousands of nodes) for more than a year and has learned several lessons the hard way. Laurent Bernaille examines the challenges Datadog faced during this journey. He dives into problems that arise when you run large clusters—and, crucially, how to address them—by providing detailed examples based on Datadog’s experience across different cloud providers. You’ll explore complex runtime and networking issues: at scale you discover complex issues in low-level components that are very rare but happen regularly when you have a large number of nodes. Additionally, Laurent provides examples of how to improve the architecture of clusters to increase scalability and performance, both on the control plane and the data plane (communication between pods and ingress traffic). If scale can be hard on the control plane, it’s even harder on tools from the ecosystem, which have rarely been tested on very large clusters. He explains several examples of the tools Datadog uses and how it had to improve them to handle its scale. And you’ll leave with practical advice on how to build a good relationship with the community and start contributing back.

Kubernetes the Very Hard Way. Velocity Berlin 2019

Laurent Bernaille

Running large Kubernetes clusters is challenging. At large scales, practitioners need to adapt and tune both their architectures and component configurations in specialized ways. Our organisation has been running large scale Kubernetes clusters (up to 2000 nodes, and growing) for more than a year, and we have learned several lessons the hard way. This talk will dive into complex runtime and networking issues that occur when running Kubernetes in production at scale. We will provide examples of how to improve the architecture of clusters to increase scalability and performance, both on the control plane and the data plane. Further, tools from the greater ecosystem will be examined, as they are rarely tested within the context of very large clusters. Finally, the talk will also discuss the mutually beneficial relationship we built with the larger Kubernetes community by providing feedback on the tools and contributing both fixes and improvements upstream.

Kubernetes the Very Hard Way. Lisa Portland 2019

Laurent Bernaille

Kubernetes is a very powerful and complicated system, and many users don’t understand the underlying systems. Come learn how your users can abuse container runtimes, overwhelm your control plane, and cause outages - it’s actually quite easy! In the last year, we have containerized hundreds of applications and deployed them in large scale clusters (more than 1000 nodes). The journey was eventful and we learned a lot along the way. We’ll share stories of our ten favorite Kubernetes foot guns, including the dangers of cargo culting, rolling updates gone wrong, the pitfalls of initContainers, and nightmarish daemonset upgrades. The talk will present solutions we adopted to avoid or work around some these problems and will finally show several improvements we plan deploy in the future. Similar to the Kubecon talk with the same title with a few new incidents.

10 ways to shoot yourself in the foot with kubernetes, #9 will surprise you! ...

Laurent Bernaille

10 ways to shoot yourself in the foot with kubernetes, #9 will surprise you!

Laurent Bernaille

Optimizing kubernetes networking

Laurent Bernaille

Kubernetes at Datadog the very hard way

Laurent Bernaille

The Docker network overlay driver relies on several technologies: network namespaces, VXLAN, Netlink and a distributed key-value store. This talk will present each of these mechanisms one by one along with their userland tools and show hands-on how they interact together when setting up an overlay to connect containers. The talk will continue with a demo showing how to build your own simple overlay using these technologies.

Deep Dive in Docker Overlay Networks

Laurent Bernaille

Deeper dive in Docker Overlay Networks

Laurent Bernaille

Discovering OpenBSD on AWS

Laurent Bernaille

Deep dive in Docker Overlay Networks

Laurent Bernaille

Most tools to recognize the application associated with network connections use well-known signatures as basis for their classification. This approach is very effective in enterprise and campus networks to pinpoint forbidden applications (peer to peer, for instance) or security threats. However, it is easy to use encryption to evade these mechanisms. In particular, Secure Sockets Layer (SSL) libraries such as OpenSSL are widely available and can easily be used to encrypt any type of traffic. In this paper, we propose a method to detect applications in SSL encrypted connections. Our method uses only the size of the first few packets of an SSL connection to recognize the application, which enables an early classification. We test our method on packet traces collected on two campus networks and on manually-encrypted traces. Our results show that we are able to recognize the application in an SSL connection with more than 85% accuracy.

Early recognition of encryted applications

Laurent Bernaille

The automatic detection of applications associated with network traffic is an essential step for network security and traffic engineering. Unfortunately, simple port-based classification methods are not always efficient and systematic analysis of packet payloads is too slow. Most recent research proposals use flow statistics to classify traffic flows once they are finished, which limit their applicability for online classification. In this paper, we evaluate the feasibility of application identification at the beginning of a TCP connection. Based on an analysis of packet traces collected on eight different networks, we find that it is possible to distinguish the behavior of an application from the observation of the size and the direction of the first few packets of the TCP connection. We apply three techniques to cluster TCP connections: K-Means, Gaussian Mixture Model and spectral clustering. Resulting clusters are used together with assignment and labeling heuristics to design classifiers. We evaluate these classifiers on different packet traces. Our results show that the first four packets of a TCP connection are sufficient to classify known applications with an accuracy over 90% and to identify new applications as unknown with a probability of 60%.

Early application identification. CONEXT 2006

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Feedback on AWS re:invent 2016

2. Reinvent 2016 •  Monday: gameday / hackaton •  Tuesday: sessions / Keynote by Hamilton •  Wednesday: Keynote by Jassy, sessions •  Thurdsay: Keynote by Vogels, sessions •  Friday: sessions •  30000 persons •  400+ sessions •  4 places •  5 days (instead of 4)

4. Amazon Global Network

5. Inside an AZ

6. Custom routers

8. Announcements

9. COMPUTE

10. Lightsail us-‐east for the moment Shadow VPC (peering possible)

11. New instances C5: Skylake, up to 72 vCPU / 144GB RAM

12. ElasZc GPU

13. IPv6 support for ec2 •  Instances can have publicly routable ipv6 address •  Dual stack instances (ipv4 + ipv6) •  To create private subnets Ø New resource: Egress-‐only Internet Gateway

14. MANAGEMENT TOOLS

15. Opsworks for chef automate Managed chef server Chef automate suite

16. Personal Health dashboard Personalized view of status Available via API Can send noGﬁcaGons

17. EC2 system manager Extension of Run Command and Simple System Management Integrated with AWS conﬁg

18. ANALYTICS

19. Athena Process S3 data with SQL Uses Presto 5$ / TB processed

20. Glue Create data sources Generate transformaGons (python) Run: scheduled or event-‐based Uses Spark under the hood

21. Batch HPC as as service Compute env (“grids”) On-‐demand or spot Queues to submit jobs

22. DATABASE

23. PostgreSQL compaZble Aurora

24. DEVELOPER TOOL

25. Code Build Fully managed CI Priced per build-‐minutes Integrated with CodePipeline

26. ARTIFICIAL INTELLIGENCE

27. RekogniZon

28. Polly 24 languages 47 voices

29. Lex Natural language understanding Based on tech powering Alexa

30. MONITORING AND SECURITY

31. Shield

32. X-‐Ray APM soluGon Analyze call graph View call latencies

33. MIGRATION AND HYBRID

34. VMWare on AWS Pat Gelsinger on stage with Jassy

35. AWS Snowmobile 100 PB 350KW 1Tb/s (Nx40Gb/s) “We’re gonna need a bigger box”

36. MOBILE

37. Pinpoint Send targeted noGﬁcaGons Real Gme analyGcs: engagement, moneGzaGon, demographics

38. CONTAINERS

39. Blox Opensource

40. LAMBDA

41. Greengrass Run lambda code on IOT devices

42. Snowball edge Bigger snowball: 100TB Horizontal scaling via clustering In addiGon to s3 import/export, local storage + compute Ø  Process data using lambda

43. Lambda edge Run lambda code on Cloudfront edges Limited capabiliGes Ø  50ms maximum Ø  No access to external services Use cases: redirect / modify process headers and cookies

44. Lambda c# support

45. Lambda Step funcZons Coordinate lambda funcGons Ø  Sequences Ø  Parallel processing Ø  Branch based on data Ø  Synchronize Ø  Manage errors and retries

46. Conclusion •  More compeZZon in the cloud Ø  AWS is developing some services others had before them •  AWS now talks “Hybrid” which is very new •  Not many news in mobile / containers / database •  More and more high-‐level services •  AWS goes on the market of soeware vendors Ø  APM, Grid, CI, ETL •  Big push in AI •  Many things around Lambda The amount of new services this year is crazy

Feedback on AWS re:invent 2016

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