John F. Morar

Web services navigator: visualizing the execution of web services

The Web Services standard is becoming the lingua franca for loosely coupled distributed applications. As the number of nodes and the complexity of these applications grow over the coming years, it will become more challenging for developers to understand, debug, and optimize them. In this paper, we describe Web Services Navigator, a visualization tool that fosters better understanding of service-oriented architecture (SOA) applications. We draw on our experience with real SOA applications to show how this tool has been applied to practical problems ranging from business logic misunderstandings to performance bottlenecks to syntax and semantic errors. Web Services Navigator helps to solve these problems by visualizing how applications really execute, enabling business owners, application designers, project managers, programmers, and operations staff to understand how their applications actually behave. We sketch the architecture of Web Services Navigator, outline how it reconstructs application execution from event logs, and describe how users interactively explore their applications using its five linked views.

Execution patterns for visualizing web services

Web Services are well on their way to becoming the Lingua Franca for distributed computing. Although tools for building and monitoring web services applications are more powerful and easier to use than ever, they do not yet fully address the horizontal complexity of mature applications built as large nets of interconnected web services. We present a pattern-based visualization that enables business owners, application designers, programmers, and operations staff to quickly understand the behavior of complex web services applications. We describe a novel pattern extraction algorithm that captures important trends from web services execution traces. We demonstrate a new way to visualize these patterns that shows the behavior of web services applications at different levels of abstraction. Finally, we explain how this can help developers with performance analysis by showing both the averages and variations in the data contained in each pattern.

Implementing a high-volume, low-latency market data processing system on commodity hardware using IBM middleware

A stock market data processing system that can handle high data volumes at low latencies is critical to market makers. Such systems play a critical role in algorithmic trading, risk analysis, market surveillance, and many other related areas. We show that such a system can be built with general-purpose middleware and run on commodity hardware. The middleware we use is IBM System S, which has been augmented with transport technology from IBM WebSphere MQ Low Latency Messaging. Using eight commodity x86 blades connected with Ethernet and Infiniband, this system can achieve 80 μsec average latency at 3 times the February 2008 options market data rate and 206 μsec average latency at 15 times the February 2008 rate.