The document discusses the significance of containers in the context of DevOps and cloud environments, comparing them to virtual machines and highlighting their advantages such as rapid innovation, resource efficiency, and isolation. It explains how technologies like Mesos and Docker can be used to optimize resource utilization, facilitate service deployment, and manage multiple clusters effectively. Additionally, the document addresses potential challenges related to container networking and performance.

1. AWS AND SOFTSERVE
CONTAINERIZATION:
THE DEVOPS REVOLUTION
Tuesday, April 12, 2016
8 AM PST/4 PM BST/5 PM CEST
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2. SPEAKERS
Valentyn Kropov
Solutions Architect,
SoftServe
Craig Williams
Solutions Architect,
Amazon Web Services
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3. WHY CONTAINERS ARE SUCH A BIG DEAL
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4. • Own Process Space
• Can run commands as root
• Can install ad-hoc packages
• Can run Services
• You can get a shell on it (e.g. through SSH)
CONTAINER IS LIKE A LIGHTWEIGHT VM:
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5. • It uses hosts kernel
• Can’t boot different OS
• Can’t have it’s own kernel modules
• From host OS perspective Containers are the bunch of processes
DIFFERENCES TO VM:
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CONTAINERS vs VMs

7. • Resource Metering and Limiting
• CPU and CPUSet
• Memory
• Network
• Block I/O
• /dev/*
CGROUPS (CONTROL GROUPS):
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8. • Provides containers with their own view of the system
• Limits what you can see (and use)
• Multiple namespaces: pid, net, mnt, uts, ipc, user
NAMESPACE:
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9. • Create new container instantly instead of
copying whole system
• Storage keeps tracking of what has change
(AUFS, ZFS, etc)
• Reduces footprint and overhead
• Decreases boot time
COPY-ON-WRITE STORAGE:
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10. • LXC
• systemd-nspawn
• Docker Engine
• rkt/runC
• OpenVZ
• Jails (FreeBSD), Zones (Solaris)
CONTAINER RUNTIMES:
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11. • Uses the same kernel features => Performance will be the same
• What matters is:
Design
Features
Ecosystem (e.g. 100.000+ apps in Docker Hub)
Support
WHAT’S THE DIFFERENCE BETWEEN THEM?
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12. THE STORY OF SUCCESS
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13. PROBLEM & OPPORTUNITY
• Rapid innovation in computing and application development services
• No single service is optimal for all solutions
• Customers want to run multiple services in a single cluster and run
multiple clusters in Intercloud environment
...to maximize utilization
...to share data between services
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14. DATACENTER AND SOLUTION TODAY
VM7 VM8
VM4 VM5 VM6
VM1 VM2 VM3
VM1 VM2 VM2
Visualization Service
Data Ingestion
Service
Analytics Service
• Configuration and
management of 3 separate
clusters
• Resources stay idle if
service is not active
• Need to move data
between clusters for each
service
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15. WHAT DO WE WANT TO DO?
Data
Ingestion
Service
Analytics
Service
Visualization
Service
….to maximize utilization
...to share data between services
Shared cluster
Multiple clusters
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16. SHARED CLUSTER
AWS
VM1 VM2 VM3 VM4 VM5
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17. WHAT IS IN IT FOR CUSTOMERS?
Maximize utilization
Deliver more services with smaller footprint
Shared clusters for all services
Easier deployment and management with unified service platform
Shared data between services
Faster and more competitive services and solutions
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18. HOW DOES THIS WORK?
Mesos Master
Mesos Slave
Spark Task Executor Mesos Executor
Task #1 Task #2 ./python XYZ
Mesos Slave
Docker Executor Docker Executor
java -jar XYZ.jar ./xyz
Mesos Master Mesos Master
Spark Service Scheduler Marathon Service Scheduler
Zookeeper
quorum
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19. HOW DOES THIS WORK?
Mesos provides fine grained resource isolation
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Mesos Slave Process
Spark Task Executor Mesos Executor
Task #1 Task #2 ./python XYZ
COMPUTE NODE
Executor
Container
(cgroups)

20. HOW DOES THIS WORK?
Mesos provides scalability
Mesos Slave Process
./python XYZ
COMPUTE NODE
Python executor finished,
more available resources
more Spark
Container
(cgroups)
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Spark Task Executor
Task #1 Task #2 Task #3 Task #4

21. Mesos has no single point of failure Services keep running if VM fails!
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HOW DOES THIS WORK?
Mesos MasterMesos Master
Mesos Master
VM1 VM2 VM3 VM4 VM5
!

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HOW DOES THIS WORK?
Mesos MasterMesos Master
Mesos Master
VM1 VM2 VM3 VM4 VM5
!
Master node can failover Services keep running if Mesos Master fails!

23. HOW DOES THIS WORK?
Mesos Slave Process
./python XYZ
COMPUTE NODE
Python executor finished,
more available resources
more Spark
Container
(cgroups)
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Spark Task Executor
Task #1 Task #2 Task #3 Task #4
!
Slave process can failover Tasks keep running if Mesos Slave Process fails!

24. Can deploy in many environments
Get orchestrated by Openstack, Ansible
(scripts), Cloudbreak
True Hybrid Cloud deployment: AWS, CIS, UCS,
vSphere, other
AWS
VM5VM1 VM2 VM3 VM4
Terraform
REST API
(policy, auto-scaling)
REST API
(direct provisioning)
Scripted provisioning
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HOW DOES THIS WORK?

25. CONTAINERS:
Service Product
Cloud/Virtualization AWS/CIS/vSphere/Metacloud/UCS…
Provisioning Terraform
Automation Ansible
Clustering & Resource
Management
Mesos, Marathon, Docker
Load Balancing Avi Networks
ETL & Data Shaping StreamSets
Log Data Gathering Logstash
Metrics Gathering CollectD, Avi Networks
Messaging Kafka, Solace
Data Storing (Batch) HDFS
Data Storing (OLTP/Real-time) Cassandra
Data Storing (Indexing) Elastic search
Data Processing Apache Spark
Visualization Zoomdata
*Subset example
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26. ISSUES
• Service Discovering
• Networking for Containers
• Persistent Storage
• Docker Performance
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27. MORE DETAILS
https://mantl.io
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