Heat Orchestration

modify instrumentalityChapeter 5 fire up instrumentation5.1 Brief Details of oestrusing mania is the main project for orchestration part of returnStack. Implementation of orchestration railway locomotive for multiple composite cloud application. It is the sequence of lines code in text file dress. A native estrus initialize can be evolving, but heat withal endeavors to provide compatibility with AWS cloud information pathfinder format so that many existing cloud formation usher can be launch on OpenStack. soup up provide some(prenominal) open stretch along difference API and cloud formation compatible wonder API. The orchestration is essentially for the softw are application. To manage configuration. Instead of manipulation of practical(prenominal) infrastructure by hand or with the script set off focuses to employment with the declarative model. hotness works out on the sequence of lines to put to death and to bring reality in to model. The model takes the heat usher and the resulting embodied of infrastructure imaginativenesss is known as the muckle. Orches,ration allows you to treat your infrastructure like code. because you can store your templates version control system, such as skunk to track changes then you update the stack with the new template and heat do the rest of the actions. The main interface of heat is the OpenStack native rest API. commove factually is between the user and the API of the core OpenStack services. In frequently the same way as the dashboard or the horizon does. Heat can be access through the horizon or the dashboard. Heat template describes the cloud application infrastructure in the code format that is changeable. The heat infrastructure resources implicate servers, float IP, volume, security concourses, and users.5.2 machine markingHeat also provides auto marking that integrates with ceilometer. Ceilometer adds scaling group as the resource within templates. Furthermore, the template, defin es the relationship between two Auto scaling by heat integrated with ceilometer that leads to add scaling group in template. The templates defines the relationship between two resources. It also able heat to shout OpenStack API in order to make everything systematic. Openstack also manage the whole lifecycle of the application. You convey to do the modification in the code for existing stack and heat deals with the rest in order to change something.Heat architecture comp championnts include Heat API It is use for processing API request to Heat engine via AMQP. It implements an Open stack-native RESTful APIHEAT-api-cfnit is used API compatibility with AWS cloud formation.HEAT ENGINE is main orchestration functionality.Heat uses back-end infobase for maintaining state information as other OpenStack services. Both die with heat engine via ANQ. The heat engine is the actual layer where actual integration is implemented. Furthermore, for high availability, Auto scaling abstraction is also d nonpareil.Auto Scaling Heat ushersIn this auto scaling example, Heat and Ceilometer pull up stakes be used to scale CPU bound virtual machines. Heat has the c erstpt of a stack which is simply the environment itself. The Heat stack template describes the process or logic around how a Heat stack willing be built and managed. Thisis where you can crap an auto-scaling group and configure Ceilometer thresholds. The environment template explains how to create the stack itself, what image or volume to use, network configuration, software to install and everything an authority or instances requisite to properly function. You can put everything into the Heat stack template, but separating the Heat stack template from the environment is much cleaner, at least in more complex configurations such as auto scaling.5.3 Deployment of Heat Orchestration5.3.1ENVIRONMENT TEMPLATEBelow we will create an environment template for a cirros image.As shown in shape.5.3. The Cirros image wil l create the instance template, configure a cinder volume, add IP from the private network, add floating IP from the public network, add the security group, private ssh- make out and generate atomic number 6% CPU load through user-data.Hot is the new template format that to replace the Heat CloudFormation-Compatible format as native format support by heat.They are written in YAML format and JSON. Hot templates createStack in Heat. Structure for Hot consist of Heat Template version, description, parameter groups, parameters, resources, and outputs.Heat Template Version Is just value with the reveal that indicates that the YAML document is a hot template of the specific version, if the date is 2013-05-23 or later date.Shown in design 5.1Fig.5.1 (Heat Template version)Description Its an nonobligatory key allows for giving a description of the template.Fig.5.2 (Description of heat template)Parameters_groups This section allows for specifying how the input parameters should be groupe d and order to provide the parameter in. This option is also optionalParameter This section allows for specifying input parameters that have to provide when instantiating the templates. This option is also optional as wellOutputs This part allows for specifying output parameters available to users once the template has been instantiated.Resources It defines actual resources that are real stack from HOT template (instance for Compute, Network, Storage Volume).Each resource is defined as a separate glut in input parameters. As shown in design 5.3 there are five separate sections. Servers, port, volume, floating IPResource ID must perpetually be unique for every sectionResource Types Must unite to the service that section of template define Such as the followersNova ServerNeutron PortNeutron FloatingIPNeutron FloatingIPAssociationCinder VolumeProperties It is a list of resource specific property defines via the function.FIG.5.3 (Heat Template Resources)Now that we have an environ ment template, we motif to create a Heat resource type and link it to a higher place file /etc/heat/templates/cirros_base.yaml.resource_registryOSNovaServerCirros file///etc/heat/templates/cirros_base.yaml5.3.2Heat TemplateThe below template in FIG.5.4 defines the behavior of the stack e.g when and under what conditions the stack will scale up and scale tweak. cpu_alarm_high and cpu_alarm_low are used in the template to scale up and scale down our environment.FIG.5.4 (Behavior Of Stack) modify Ceilometer Collection IntervalBy default, Ceilometer will collect CPU data from instances every 10 minutes. For this example, we want to change that to 60 seconds. Change the breakup to 60 in the pipeline.YAML file and restartOpenStack services.Check the status of the stack in Horizon DashboardHeat will create one instance as per defined insurance5.3.3RUNNING THE STACK stay the following command to release the stackemailprotected (keystone_admin) heat stack-create heat_autoscale -f / asc endent/heat_autoscale.YAML -e /root/environment.yamlCheck the status of the stack in Horizon Dashboard as in FIG 5.4FIG5.5 (Heat stack status )In FIG 5.5 and FIG 5.6 shows the heat stack topology and resources, Events are also shown in FIG 5.6FIG 5.6 (Heat Stack Topology)FIG 5.7 (Heat Stack Resources)FIG 5.8 (Heat Stack Events)Heat will create one instance as per defined polity in FIG 5.7FIG 5.9 (Heat Stack Instance)Automatic Scale UPNow we will increase the cpu utilization on one of the instances and will verify if heat autoscalesthe environment or not. To do that run the following commands one the instance that heat created from the stack. As shown in FIG 5.8.FIG 5.10 (Heat Autoscaling)The heat created two more instances based defined policy in the orchestration template. This is because the maximum scale up policy is 3 instances. As shown in FIG 5.8.FIG 5.11 (Two Instance base on policy)List of volumes that heat created based on defined policy threshold as shown in FIG 5.9FIG.5. 12 (Volumes that heat created based on defined policy)New Network Topology after adding instances to the private network in FIG 5.10 FIG 5.13 (Heat Topology after 2 instances) 5.3.4SCALE tidy sumScale down is the process in heat. Heat automatically scales down once the CPU utilization goes down on the instances. As the load goes back to normal and CPU cools down. The extra instances that were appeared to overcome the load will go back to one instance and all instances will be used efficiently through this way. In our scenario instance aw7blqnbabc2 is the original instance and the rest instances are to overcome the load.