|By Debu Panda, Arvind Maheshwari||
|December 23, 2008 06:45 AM EST||
Service-oriented architecture (SOA) has become mainstream technology for integrating disparate systems and applications. For building composite applications, Business Process Execution Language (BPEL) has emerged as the standard for business process flow orchestration and application integration within organizations. Many IT organizations are deploying composite applications that use BPEL to automate critical business processes. If your application needs to be available 24/7, you need a reliable BPEL infrastructure. In this article, we first present a typical BPEL engine architecture and then outline its manageable entities, and the typical management functions you need to worry about as an administrator. Finally, we conclude with approaches and best practices for managing your complete BPEL infrastructure.
To effectively manage BPEL infrastructure, administrators need to manage all the components that make up the BPEL ecosystem. Before diving into management functions you need to concern yourself with as an administrator, let's look briefly at the BPEL ecosystem.
The BPEL Ecosystem
By BPEL ecosystem, we mean the system components and resources used for executing the BPEL process and those that ensure guaranteed availability of the BPEL process. Although some of those systems may not be directly under your control, you need to take some action to monitor their availability. Unequivocally, if one or more of these components go down, it will put the business process execution at risk. Figure 1 depicts the architecture of a typical BPEL engine.
A BPEL process typically depends on one or more partner links. A partner link might be a service owned by the organization, such as an EJB running an application server or a database stored procedure over which you have immediate control. It might also be a Web service provided by a partner or vendor over which you do not have any control.
The BPEL processes are run in a runtime environment called a BPEL server or engine. The availability and service level associated with BPEL processes are completely dependent on the BPEL engine. BPEL engines can run in an application server environment. You can have a single instance or a clustered application server environment if your composite application requires high availability. A BPEL server may depend on some of the resources, such as data sources or messaging services, made available by the application server environment.
BPEL processes are long running, and hence they need a persistent store to persist state. Typically the persistence store is a relational database management system. This persistence store is called a dehydration store.
All of these software components run on several nodes, or hosts, that constitute the critical piece of your BPEL ecosystem.
To summarize, a BPEL ecosystem comprises the following:
- BPEL processes and partner links
- BPEL engine
- Dehydration store
- Gateway to BPEL engine
- The application server on which the BPEL server is running
- Hosts on which the BPEL server, the dehydration store, and the gateway are running
The health of a BPEL process that you manage is a function of any of the entities that constitute the BPEL ecosystem, and you need to concern yourself with managing and monitoring all of these entities.
Let's dive down into the management functions for each component and which approaches you should follow to manage them, starting with the management aspects of BPEL processes.
Software AG helps organizations transform into Digital Enterprises, so they can differentiate from competitors and better engage customers, partners and employees. Using the Software AG Suite, companies can close the gap between business and IT to create digital systems of differentiation that drive front-line agility. We offer four on-ramps to the Digital Enterprise: alignment through collaborative process analysis; transformation through portfolio management; agility through process automation and integration; and visibility through intelligent business operations and big data.
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