Post on 15-Apr-2017
Containerization: The DevOps Revolution
Why do we need containers?
Shipping Containers• Standardized dimensions• Mechanized handling system• Remote sorting and packing• Remote customs services• Greatly decreases cost and
speed of international trade
Software Container is like a VM• Own Process Space• Can run commands• Packages can be installed• Can run services/daemons• Isolated root privileges• Shell access
Software Container is not like a VM• Uses host kernel• Restricted to host OS• Can’t have it’s own kernel modules• Is plain user-space process
VM vs Container
Containers Chronology• 1982 - chroot• 2000 - FreeBSD Jail• 2001 – Linux VServer• 2004 – Solaris Containers• 2007 – HP-UX Containers• 2008 – LXC (Linux Containers)• 2013 - Docker
Linux cgroups (control groups)• Resource limiting• Prioritization• Accounting• Control
• Used by• LXC• libvirt• systemd• Docker• Kubernetes• Mesos
Linux namespaces• Isolate and virtualize resources• Every process (group) has its own
view of the system• 6 kinds of namespaces:
• mnt – mount points• pid – process IDs• net – network stack• ipc – POSIX mq filesystem• uts - hostname• user – users and groups
• Resource Metering and Limiting• CPU and CPUSet• Memory• Network• Block I/O• /dev/*
cgroups (control groups):
• 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:
• 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:
• LXC• systemd-nspawn• Docker Engine• rkt/runC• OpenVZ• Jails (FreeBSD), Zones (Solaris)
Container Runtimes:
• Uses the same kernel features => Performance will be the same
• What matters is:DesignFeaturesEcosystem (e.g. 100.000+ apps in Docker
Hub)Support
What’s the difference between them?
The Story of Success
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
Datacenter and solution todayVM7 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
What do we want to do?
Data Ingestion Service
Analytics Service
Visualization Service
….to maximize utilization...to share data between services
Shared clusterMultiple clusters
Shared Cluster
AWS
VM1 VM2 VM3 VM4 VM5
What is in it for customers?
Maximize utilizationDeliver more services with smaller footprint
Shared clusters for all servicesEasier deployment and management with unified service platform
Shared data between servicesFaster and more competitive services and solutions
How does this work?
Mesos Slave
Spark Task Executor Mesos Executor
Mesos Slave
Docker Executor Docker Executor
Mesos Master
Task #1 Task #2 ./python XYZ java -jar XYZ.jar ./xyz
Mesos Master Mesos Master
Spark Service Scheduler Marathon Service Scheduler Zookeeper quorum
How does this work? Mesos provides fine grained resource isolation
Mesos Slave Process
Spark Task Executor Mesos Executor
Task #1 Task #2 ./python XYZ
Compute Node
ExecutorContainer(cgroups)
How does this work? Mesos provides scalability
Mesos Slave Process
Spark Task Executor
Task #1 Task #2 ./ruby XYZ
Compute Node
Python executor finished,more available resourcesmore Spark
Container(cgroups)
Task #3 Task #4
How does this work? Mesos has no single point of failure
Mesos MasterMesos MasterMesos Master
VM1 VM2 VM3 VM4 VM5
Services keep running if VM fails!
How does this work? Master node can failover
Mesos MasterMesos MasterMesos Master
VM1 VM2 VM3 VM4 VM5
Services keep running if Mesos Master fails!
How does this work? Slave process can failover
Tasks keep running if Mesos Slave Process fails!
Mesos Slave Process
Spark Task Executor
Task #1 Task #2 ./ruby XYZ
Compute Node
Task #3 Task #4
How does this work? 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
Containers: Service Product
Cloud/VirtualizationAWS/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
Issues• Service Discovering• Networking for Containers• Persistent Storage• Docker Performance