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Designing SOA Web Services Services for Performance

Continued discussion of the secrets of building and operating a realistic SOA

As we discussed last month, performance is often an afterthought when building new systems, including SOAs. We're finding that services and SOAs fall victim to this oversight as well. Indeed, there is a right way and a wrong way to design a service and an SOA. Also, there are things that are out of your control that you must consider during your design.

This month let's continue our discussion with some important performance concepts, including how to create a performance model, as well as more service and SOA design tips when considering performance.

Creation of a Performance Model
SOAs are not unlike any other distributed computing systems, and thus designing a performance model should be nothing too new. At this point we understand exactly how each service behaves under an increasing load, and we have enough data to plug into a model. Now, it's just a matter of building a model.

There are very expensive performance monitoring and simulation tools that are for sale in the market, but sometimes the least expensive and most simple tools work best...in many cases, just a spreadsheet will do. For our purposes, we need to consider both information and behavior in the context of performance, as well as core features of an SOA.

Information Movement Modeling, typically asynchronous in nature, means we're attempting to simulate how information moves from point to point, point to many points, or many points to many points. Based on the information we accumulated we know the:

  • Information production rate from a service
  • Information consumption rate from a service
For example, an instance of a service is able to produce 52 messages (or similar groupings of information) per second...the source service. An instance of a service is able to consume 34 messages per second...the target service. This is a simple point-to-point relationship, but keep in mind that multi-points to multi-points are always possible, and those are a bit more complex to model since you have to determine patterns of movement between multiple points vs. all messages produced by a single service that are consumed by another.

Moreover, keep in mind transformation and routing latency is typically an issue here as well, and needs to be modeled along with consumption and production. You should have test data from these services, but the performance of transformation and routing services will be largely dependent upon the complexities of the transformations and logic associated with the routing. What many do when creating performance models is to model very complex, complex, and simple transformation scenarios, and the percentages of each.

Service Invocation Modeling, typically more synchronous in nature, means we're attempting to determine the number of times a service is able to provide a behavior (application function) in an instance of time, typically a second.

For instance, you may have a service that provides a risk calculation for the insurance business, and is perhaps abstracted into several different applications (composites). We know through testing that each composite can invoke the service up to 100 times a second before it hits a saturation point, meaning the performance of the service quickly diminishes as additional load is placed upon it. This saturation number plugs into the model, as well as the number of applications that are abstracting this service. You have to model all of these services in the same way.

Models are important because they allow you to predict performance under changing needs without having to actually build and test the system. Models, of course, are not perfect, and you must constantly adjust assumptions and modeling information as you learn more about the behavior of the architecture.

Designing for Performance, Monitoring, and Optimizing
So, now that we know how to diagnose the performance of an SOA, as well as model for it to determine how it will behave in a changing environment, how do we design a service and SOA with optimized performance? Here are a few tips.

  • The more processing you can place at the origin of the service, the better your SOA will perform. In many SOAs, the architects abstract the services to a single server, and performance can be somewhat problematic in larger implementations.
  • Many services are built on top of more traditional legacy APIs, and as such the translations between legacy APIs to expose them as services may cause performance problems. The ability to leverage existing legacy systems as services is a powerful notion. However, you must be careful in selecting the proper enabling technology to do this. Service invocations that take a second or more to produce behavior, or information bound to behavior, will cause big problems when you align them with hundreds of other services that are doing the same thing.
  • The use of too many fine-grained services may cause performance problems. Indeed, you should not be afraid to leverage fine-grained services within your SOA. However, you need to understand the performance issues associated with doing so, and take the network bandwidth and how other applications leverage the services into careful consideration.
  • Make sure to consider performance when selecting your orchestration layer. Many BPEL engines are notoriously poor performers, and can become the bottleneck for the SOA.
  • Understand the basic rule that, while the value of an SOA is the ability to leverage many remote services, the more services you leverage, the more problematic your SOA will become.
Core Issues
Making solutions scale is nothing new. However, the SOA technology and approaches recently employed are largely untested with higher application and information and service management traffic loads. SOA implementers were happy to get their solutions up and running, yet, in many cases, scalability is simply not a consideration within the SOA, nor was load testing or other performance fundamentals. We are seeing the results of this neglect now that SOA problem domains are exceeding the capacity of their architectures and the technology. It does not have to be this way.

What is more, many SOA technology vendors have not focused on scalability within their solutions. Instead, feature/function enhancements are the rule of the day. Architects feel it's more important to add orchestration features and more adapters to their solution than to figure out how to reliably pump more information, and manage more services, with their product. It's time for that focus to change.

More Stories By David Linthicum

David Linthicum is the Chief Cloud Strategy Officer at Deloitte Consulting, and was just named the #1 cloud influencer via a recent major report by Apollo Research. He is a cloud computing thought leader, executive, consultant, author, and speaker. He has been a CTO five times for both public and private companies, and a CEO two times in the last 25 years.

Few individuals are true giants of cloud computing, but David's achievements, reputation, and stellar leadership has earned him a lofty position within the industry. It's not just that he is a top thought leader in the cloud computing universe, but he is often the visionary that the wider media invites to offer its readers, listeners and viewers a peek inside the technology that is reshaping businesses every day.

With more than 13 books on computing, more than 5,000 published articles, more than 500 conference presentations and numerous appearances on radio and TV programs, he has spent the last 20 years leading, showing, and teaching businesses how to use resources more productively and innovate constantly. He has expanded the vision of both startups and established corporations as to what is possible and achievable.

David is a Gigaom research analyst and writes prolifically for InfoWorld as a cloud computing blogger. He also is a contributor to “IEEE Cloud Computing,” Tech Target’s SearchCloud and SearchAWS, as well as is quoted in major business publications including Forbes, Business Week, The Wall Street Journal, and the LA Times. David has appeared on NPR several times as a computing industry commentator, and does a weekly podcast on cloud computing.

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