Novell ZENworks Orchestrator is an advanced datacenter management solution designed to manage all network resources. It provides the infrastructure that manages group of ten, one hundred, or thousands of of physical or virtual resources.
Orchestrator is equally apt at performing a number of distributed processing problems. From high performance computing, the breaking down of work into lots of small chucks that can be processed in parallel through distributed job scheduling. The following figure shows the product’s high-level architecture:
Figure 2-1 ZENworks Orchestrator
Agents are installed on all managed resources as part of the product deployment. For more detailed information about these components, see Novell ZENworks Orchestrator 1.2 Installation and Getting Started Guide.
The agent connects every managed resource to its configured server and advertises to the ZENworks Orchestrator Server that the resource is available for tasks. This persistent and auto-reestablishing connection is important because it provides a message bus for the distribution of work, collection of information about the resource, per-job messaging, health checks, and resource failover control.
After resources are enabled, Orchestrator can discover, access, and store detailed abstracted information—called “facts”—about every resource. Managed resources, referred to as “nodes,” are addressable members of the of the Orchestrator Server “grid” (also sometimes called the “matrix”). When integrated into the grid, nodes can be deployed, monitored, and managed by the Orchestrator Server, as discussed in Section 2.2, Understanding ZENworks Orchestrator Functionality.
An overview of the Orchestrator grid architecture is illustrated in the figure below, much of which is explained in this developer’s guide:
Figure 2-2 Orchestrator Server Architecture
For additional information about job architecture, see Section 7.0, Job Architecture.
ZENworks Orchestrator enables you to monitor your system computing resources using the built-in Resource Monitor. To open the Resource Monitor in the console, see “Monitoring Server Resources” in the ZENworks Orchestrator Administration Guide.
The following entities are some of key components involved in the Orchestrator Server:
All managed resources, which are called nodes, have an agent with a socket connection to the Orchestrator Server. All resource use is metered, controlled, and audited by the Orchestrator Server. Policies govern the use of resources.
Orchestrator allocates resources by reacting as load is increased on a resource. As soon as we go above a threshold that was set in a policy, a new resource is allocated and consequently the load on that resource drops to an acceptable rate.
You can also write and jobs that perform cost accounting to account for the cost of a resource up through the job hierarchy, periodically, about every 20 seconds. For more information, see Section 4.5, Auditing and Accounting Jobs.
A collection of jobs, all under the same hierarchy, can cooperate with each other so that when one job offers to give up a resource it is reallocated to another similar priority job. Similarly, when a higher priority job becomes overloaded and is waiting on a resource, the system “steals” a resource from a lower priority job, thus increasing load on the low priority job and allocating it to the higher priority job. This process satisfies the policy, which specifies that a higher priority job must complete at the expense of a low priority job.
Orchestrator users must authenticate to access the system. Access and use of system resources are governed by policies.
A job definition is described in the embedded enhanced Python script that you create as a job developer. Each job instance runs a job that is defined by the Job Definition Lannguage (JDL). Job definitions might also contain usage policies. For more information, see the Job class.
Jobs are instantiated at runtime from job definitions that inherit policies from the entire context of the job (such as users, job definitions, resources, or groups). For more information, see JobInfo.
Policies are used to enforce quotas, job queuing, resource restrictions, permissions, and other job parameters. Policies can be associated with any Orchestrator object. For more information, see Section 2.2.2, Policy-Based Management.
Facts represent the state of any object in the Orchestrator grid. They can be discovered through a job or they can be explicitly set.
Facts control the behavior a job (or joblet) when it’s executing. Facts also detect and return information about that job in various UIs and server functions. For example, a job description that is set through its policy and has a specified value might do absolutely nothing except return immediately after network latency.
There are three basic types of facts:
Static: Facts that require you to set a value. For example, in a policy, you might set a value to be False. Static facts can be modified through policies.
Dynamic: Facts produced by the Orchestrator system itself. Policies cannot override dynamic facts. They are read only and their value is determined by the orchestrator itself.
Computed: Facts derived from a value, like that generated from the cell of a spreadsheet. Computed facts have some kind of logic behind them which derive their values.
For example, you might have two numeric facts that you want expressed in another fact as an average of the two. You could compose a computed fact which averages two other facts and express it as an average value under a certain fact name. This enables you to create facts that represent other metrics on the system that aren’t necessarily available in the default set, or are not static to anything that might impact other dynamic facts.
For more information about facts, see Section 5.1.2, Facts.
In order for the Orchestrator to choose resources for a job, it uses resource constraints. A resource constraint is some Boolean logic that executes against facts in the system. Based upon this evaluation, it will only consider resources that match the criteria that have been set up by use of constraints.
For more detailed information, see Section 7.7, Working with Facts and Constraints and the following JDL constraint definitions:
Resources, users, job definitions and virtual machines (VM) are managed in groups with group policies that are inherited by members of the group. For more information, see ***see “Job Info/Groups” in the ZENworks Orchestrator Administration Guide
A virtual machine host is a resource that is able to run guest operating systems. Attributes (facts) associated with the VM host control its limitations and functionality within the Orchestrator Server. A VM image is a resource image that can be cloned and/or provisioned. A VM instance represents a running copy of a VM image.
Templates are images that are meant to be cloned (copied) prior to provisioning the new copy. For more information, see
Building and Discovering and VMs in the Novell ZENworks Orchestrator 1.2 Virtual Machine Management Guide.
The Orchestrator server manages all nodes by administering jobs (and the functional control of jobs at the resource level by using joblets), which control the properties (facts) associated with every resource. In other words, jobs are units of functionality that dispatch data center tasks to resources on the network such as management, migration, monitoring, load balancing, etc.
Orchestrator provides a unique job development, debugging, and deployment environment that expands with the demands of growing data centers.
As a job developer, your task is to develop jobs to perform a wide array of work that can be deployed and managed by ZENworks Orchestrator.
Jobs, which run on the Orchestrator server, can provide functions within the Orchestrator environment that might last from seconds to months. Job and joblet code exist in the same script file and are indentified by the .jdl extension. The Python script contains only one job definition and zero or rmore joblet definitions.
A job file also might have policies associated with it to define and control the job’s behavior and to define and to define certain constraints restricting its execution. A .jdl script that is accompanied by a policy file is typically packaged in a job archive file (.job). Because a .job file is physically equivalent to a Java archive file (.jar), you can use the JDK JAR tool to create the job archive.
Multiple job archives can be delivered as a management pack in a service archive file (SAR) identified with the .sar extension. Typically, a group of related files are delivered this way. For example, the xen30 management pack is a SAR.
As shown in the following illustration, jobs include all of the code, policy, and data elements necessary to execute specific, predetermined tasks administered either through the ZENworks Orchestrator console, or from the zos command line tool.
Figure 2-3 Components of an Orchestrator Job (my.job, )
Because each job has specific, predefined elements, jobs can be scripted and delivered to any agent, which ultimately can lead to automating almost any datacenter task. Jobs provide the following functionality:
For more information, see Section 4.0, Orchestrator Job Classifications and the JDL job class definitions:
Jobs can written to control all operations and processes of managed resources. Through jobs, the Orchestrator Server manages resources to perform work. Automated jobs (written in JDL), are broken down into joblets, which are distributed among multiple resources.
By managing many small joblets, the Orchestrator server can enhance system performance and maximize resource use.
Jobs can detect demand and monitor health of system resources, then modify clusters automatically to maximize system performance and provide failover services.
Some jobs provide inspection of resources to more effectively management assets. These jobs enable all agents to periodically report basic resource facts and performance metrics. In essence, these metrics are stored as facts consisting of a key word and typed-value pairs like the following example:
Jobs can poll resources and automatically trigger other jobs if resource performance values reach certain levels.
The system job scheduler is used to run resource discovery jobs to augment resource facts as demands change on resources. This can be done on a routine, scheduled basis or whenever new resources are provisioned, new software is installed, bandwidth changes occur, OS patches are deployed, or other events occur that might impact the system.
Consequently, resource facts form a capabilities database for the entire system. Jobs can be written that apply constraints to facts in policies, thus providing very granular control of all resources as required. All active resources are searchable and records are retained for all off-line resources.
The following osInfo.job example shows how a job sets operating system facts for specific resources:
resource.cpu.mhz (integer) e.g., "800" (in Mhz) resource.cpy.vendor (string) e.g. "GenuineIntel" resource.cpu.model (string) e.g. "Pentium III" resource.cpu.family (string) e.g. "i686"
osInfo.job is packaged as a single cross-platform job and includes the Python-based JDL and a policy to set the timeout. It is run each time a new resource appears and once every 24 hours to ensure validity of the resources. For a more detailed review of this example, see osInfo.job.
Jobs can be scheduled to to periodically trigger specific system resources based on specific time constraints or events. As shown in the following figure, Orchestrator provides a built-in job scheduler that enables you or system administrators to flexibly deploy and run jobs.
Figure 2-4 Orchestrator Job Scheduler
Jobs also drive provisioning for virtual machines and blade servers. Provisioning adapter jobs are deployed and organized into appropriate job groups for management convenience. Provisioning adapters are deployed as part of your VMM license. For more information, see Section 9.2.1, VM Provisioning and “Provisioning Adapter Jobs” in the ZENworks Orchestrator Administration Guide.
The Orchestrator Server is a “broker” that can distribute jobs to every “partner” agent on the grid. Based on assigned policies, jobs have priorities and are executed based on the following contexts:
Resource User Constraints
Each object in a job context contains the following elements:
Figure 2-5 Constraint-Based Resource Brokering
For more information, see Section 7.7, Working with Facts and Constraints.
There are three API interfaces available to the Orchestrator Server:
Orchestrator Server Management Interface: The ZENworks Orchestrator server, written entirely in Java using the JMX (Java MBean) interface for management, leverages this API for the ZENworks Orchestrator Console. The console is a robust desktop GUI designed for administrators to apply, manage, and monitor usage-based policies on all infrastructure resources. The console also provides at-a-glance grid health and capacity checks.
For more information, see “Using the ZENworks Orchestrator Console” in the ZENworks Orchestrator Administration Guide.
Figure 2-6 Novell ZENworks Orchestrator Console
Job Interface: Includes a customizable/replaceable Web application and the zosadmin command line tool. The Web-based User Portal built with this API provides a universal job viewer from which job logs and progress can be monitored. The job interface is accessible via a Java API or CLI. A subset is also available as a Web Service. The default Orchestrator Developer Portal web application leverages this API. It can be customized or alternative J2EE* application can be written.
ZENworks Monitoring System: Monitors all aspects of the data center through an open source, Eclipse*-based interrface.This interface operates in conjunction with the Orchestrator Server and monitors the following objects:
Deployed jobs that teach Orchestrator and provide the control logic that Orchestrator runs when performing its management tasks.
Users and Groups
For more information, see the Novell ZENworks Orchestrator 1.2 Virtual Machine Management Guide.
Figure 2-7 Novell ZENworks Orchestrator Virtual Machine Manager interface.