Introduction to Cloudify for OpenStack users

Sep 11, 2014Download as PPTX, PDF4 likes3,190 views

Introduction for Cloudify as a deployment and management framework for OpenStack covering also Tosca, Integration with Heat, Docker, Chef

Introduction to Cloudify 3.0
Adding New Intelligence to Cloud
Copyright © GigaSpaces 2014. All rights reserved.
Orchestration

GigaSpaces OpenStack Activities
Cloudify bridges between OpenStack and other clouds
Leading the OpenStack
community in Israel
Leading OpenSource
project to simplify the
deployment and
management of
applications on
OpenStack
Contributing the
TOSCA specification
within the OpenStack
Heat project

Mapping the Cloud Space
Higher-level Services Do it Yourself
AWS Elastic
Beanstalk
AWS
OpsWorks
AWS
CloudFormation
Amazon EC2 +
CloudWatch
OpenShift/
CloudFoundry
**Solum**
Nova, Cinder,
Neutron, etc.
Convenience Control
Cloudify is the OpsWorks
equivalent for any environment.

Motivation Behind 3.0
• OpenStack Native
• Support for VMware and other
clouds
• Standard / TOSCA
• Suited for NFV
• Single Manager Scalability
• Network Orchestration
• Bring together BI, Monitoring
and Orchestration

“Intelligent Orchestration”
Send
Metrics
Real Time Analytics Cloud Infrastructure
Execute
Policy
Feedback
Real Time
Analytics
Correlate
with
Historical
Events
Deploy
Fail-over
Scale
Intelligent Orchestration
11111111111
000000000
11111111111
000000000
11111111111
000000000
Historical Data

Network & Application Orchestration
Apache Server DB Server
NodeJS
NodeCeller
• App Network
• App Subnet
• App Port
• Security Group
• Apache Floating IP
• Router Gateway
• Data Network
• Data Subnet
• Data Port
• Security Group
Router
Monitoring, Logging CI
MongoDB

What Do We See Here?
Host
Middleware
App module
connection

TOSCA –
The Glue for
Putting all This
Together
Application
Topologies
Workflows
Policies

TOSCA (Like) Blueprint
Host
Middleware
module: DB
Use Bash for
configuration and
life cycle
management

TOSCA (Like) Blueprint
Middleware module:
node JS Server
App module: node JS
connection

Plugin Architecture
14
Cloudify
Manager
Cloudify
Agent
Chef,Docker
,.. Endpoint
NodeJS
Container
MongoD
Container
Blueprint
Agent PlugIn

Chef Integration
Where to find the
plugin type definition
Using the Chef types
for app_module
Passing the specific
properties for the Chef
type

Docker Integration
Using the Docker type
for db_server
Passing the Docker file
inline

Deploy a Heat stack
Template Heat
VM VM
VM

Discovery of Heat stack
Cloudify
Discoverer
Cloudify
Blueprint
VM VM
VM

Augmenting The Blueprint
Cloudify
Discoverer
Infra
Blueprint
App
Blueprint
complete
Blueprint

Learn More
getcloudify.org
Documentation
Architecture
Community Forum
Blog

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NOSQL are often limited in the type of queries that they can support due to the distributed nature of the data. In this session we would learn patterns on how we can overcome this limitation and combine multiple query semantics with NoSQL based engines. We will demonstrate specifically a combination of key/value, SQL like, Document model and Graph based queries as well as more advanced topic such as handling partial update and query through projection. We will also demonstrate how we can create a meshaup between those API's i.e. write fast through Key/Value API and execute complex queries on that same data through SQL query. - See more at: http://nosql2014.dataversity.net/sessionPop.cfm?confid=81&proposalid=6335#sthash.PNSZi5TJ.dpuf

When networks meets apps (open stack atlanta)Nati Shalom

Recent advancements in OpenStack capabilities have made the cloud better tuned to enterprise needs by introducing much more flexible network designs and networking services, with the tradeoff of making the cloud more complex. In this session we will describe how we can leverage the power of the new networking advancement without exposing the complexity to the end user. We will present alternative approaches and their tradeoffs for automating the deployment of a typical n-tier enterprise application that include multi-tenant environment, separate network for admin and applications, cross region network, attach a floating IP, setup security groups etc. all through a combination of Heat, TOSCA, Chef, Puppet, and more.

Application Centric Approach to DevopsNati Shalom

This document discusses application centric DevOps and orchestration tools. It contrasts infrastructure centric versus application centric approaches. Infrastructure centric tools like configuration management focus on defining directives and scripts to configure master and child nodes. Application centric tools like Cloudify focus on mapping application dependencies, deploying applications across infrastructure, and automating monitoring, healing and scaling of applications. The document provides an example of how PaddyPower uses Cloudify for continuous delivery and discusses how Cloudify integrates with configuration tools like Chef to provide deployment, post-deployment and advanced orchestration capabilities.

Case Studies for moving apps to the cloud - DLD 2013Nati Shalom

This document discusses four case studies of organizations moving mission critical applications to the cloud using Cloudify. The first case study is of an investment bank moving thousands of applications to their private cloud. The second is of a telecommunications company using Cloudify to build a carrier-grade PaaS offering on their cloud platform. The third case study is of a systems integrator using Cloudify to automate deployment of an IBM big data stack for customers on OpenStack. The fourth case study is of a company using Cloudify for multi-cloud disaster recovery across regions.

Real-Time Big Data at In-Memory Speed, Using StormNati Shalom

Storm, a popular framework from Twitter, is used for real-time event processing. The challenge presented is how to manage the state of your real-time data processing at all times. In addition, you need Storm to integrate with your batch processing system (such as Hadoop) in a consistent manner. This session will demonstrate how to integrate Storm with an in-memory database/grid, and explore various strategies for integrating the data grid with Hadoop and Cassandra, seamlessly. By achieving smooth integration with consistent management, you will be able to easily manage all the tiers of you Big Data stack in a consistent and effective way. - See more at: http://nosql2013.dataversity.net/sessionPop.cfm?confid=74&proposalid=5526#sthash.FWIdqRHh.dpuf

Disaster Recovery on Demand on the CloudNati Shalom

Disaster recovery on demand on the cloudNati Shalom

The document discusses outages on cloud platforms like AWS and strategies for disaster recovery. It notes that AWS has experienced several outages in 2011, 2012, and 2013, costing users millions per hour. To prepare for outages and enable fast disaster recovery, the document recommends having redundancy across multiple cloud regions and availability zones using automation and data replication. It presents a case study of a company that used the Cloudify platform to clone their application environment and database across different AWS regions, enabling failover between regions in seconds in the event of an outage. This approach significantly reduced recovery time objectives while keeping costs lower than maintaining a dedicated hot disaster recovery site.

Big Data on OpenStackNati Shalom

The massive computing and storage resources that are needed to support big data applications make cloud environments an ideal fit. In this session, you'll learn how to build your big data "database on-demand" using MongoDB, Cassandra, Solr, MySQL, or any other big data solution, as well as manage your big data application using a new open source framework called “Cloudify.” All this, on top of the OpenStack cloud.

Avoiding Cloud OutageNati Shalom

Building cross-region and cross could high availability into your app, a real life use case by Gigaspaces, Nati Shalom, Funder & CTO, Gigaspaces Achieving high levels of availability and disaster recovery in a cloud environment requires the implementation of patterns and practices that introduce redundancy through multi-zone, multi-region, and multi-cloud deployments. As we move towards implementing higher availability, we cannot escape the direct increase in the accidental complexity of the deployment architecture resulting from lack of cloud portability and deployment lifecycle automation. We present how high availability and disaster recovery were achieved in reality by using the Cloudify open source framework on top of AWS. This approach applies to not just AWS but also other public clouds and private cloud environments such as Eucalyptus. The resulting reference architecture provides portable PostgreSQL replication and disaster recovery as well as application tier scalability across zones, regions, and public/private clouds through a unified deployment workflow.

Dont call me cache april 17Nati Shalom

The document describes how an in-memory data grid (IMDG) like GigaSpaces XAP can be used to power a payment authorization application. Key elements include: 1) Payment objects are written to the IMDG, with annotations defining the primary key, routing attribute, and secondary indexes. 2) Validation tasks read from the IMDG in parallel to check the user profile and merchant profile. 3) A payment authorization object is updated in the IMDG to track validation status. 4) The fully validated payment object results in an authorization being written to the IMDG.

Big data (reversim)Nati Shalom

Adopting Hadoop to manage your Big Data is an important step, but not the end-solution to your Big Data challenges. Here are some of the additional considerations you must face: Choosing the right cloud for the job: The massive computing and storage resources that are needed to support Big Data applications make cloud environments an ideal fit, and more than ever, there is a growing number of choices of cloud infrastructure types and providers. Given the diverse options, and the dynamic environments involved, it becomes ever more important to maintain the flexibility for all your IT needs. Big Data is a complex beast: It involves many and different moving parts, in large clusters, and is continually growing and evolving. Managing such an environment manually is not a viable option. The question is, how can you achieve automation of all this complexity? The world beyond Hadoop: Big Data is not just Hadoop – there is a whole rapidly growing ecosystem to contend with, including NoSQL, data processing, analytics tools… As well as your own application services. How can you manage deployment, configuration, scaling and failover of all the different pieces, in a consistent way? In this session, you'll learn how to deploy and manage your Hadoop cluster on any Cloud, as well as manage the rest of your big data application stack using a new open source framework called Cloudify.

Scaling and Managing Big Data Apps in the CloudNati Shalom

Title: Scaling and Managing Big Data Apps on Public Clouds Abstract: The massive computing and storage resources that are needed to support big data applications make on-demand, elastic cloud environments an ideal fit. However, managing your big data app on the cloud is no walk in the park - configuration, orchestration, H/A, auto-scaling are all quite complex when it comes to choosing the right cloud for you, whether it’s public, private or a hybrid cloud - which is where Cloudify and Eucalyptus come together. In this session, you'll learn how to deploy, manage, monitor and scale your big data apps on the open source Eucalyptus cloud platform using Cloudify, as well as easily test drive your apps locally and then migrate the workload to Amazon Web Services EC2.

Big Data in the CloudNati Shalom

Adopting Hadoop to manage your Big Data is an important step, but not the end-solution to your Big Data challenges. Here are some of the additional considerations you must face: Choosing the right cloud for the job: The massive computing and storage resources that are needed to support Big Data applications make cloud environments an ideal fit, and more than ever, there is a growing number of choices of cloud infrastructure types and providers. Given the diverse options, and the dynamic environments involved, it becomes ever more important to maintain the flexibility for all your IT needs. Big Data is a complex beast: It involves many and different moving parts, in large clusters, and is continually growing and evolving. Managing such an environment manually is not a viable option. The question is, how can you achieve automation of all this complexity? The world beyond Hadoop: Big Data is not just Hadoop – there is a whole rapidly growing ecosystem to contend with, including NoSQL, data processing, analytics tools… As well as your own application services. How can you manage deployment, configuration, scaling and failover of all the different pieces, in a consistent way? In this session, you’ll learn how to deploy and manage your Hadoop cluster on any Cloud, as well as manage the rest of your big data application stack using a new open source framework called Cloudify.

Cloudify and terraform integrationNati Shalom

Why NFV and Digital Transformation Projects Fail! Nati Shalom

Cloudify and terraform integrationNati Shalom

Open Stack Days israel Keynote 2017Nati Shalom

What A No Compromises Hybrid Cloud Looks Like Nati Shalom

Orchestration tool roundup kubernetes vs. docker vs. heat vs. terra form vs...Nati Shalom

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Complex Analytics with NoSQL Data Store in Real TimeNati Shalom

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Case Studies for moving apps to the cloud - DLD 2013Nati Shalom

Real-Time Big Data at In-Memory Speed, Using StormNati Shalom

Disaster Recovery on Demand on the CloudNati Shalom

Disaster recovery on demand on the cloudNati Shalom

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Avoiding Cloud OutageNati Shalom

Dont call me cache april 17Nati Shalom

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Scaling and Managing Big Data Apps in the CloudNati Shalom

Big Data in the CloudNati Shalom

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