A framework for detecting performance design and deployment antipatterns in component based enterprise systems

Abstract

In this paper a framework to automatically detect design and deployment antipatterns in component based enterprise systems is presented. The approach taken monitors a running system and makes use of statistical analysis and techniques from the field of data mining to summarise the performance data collected. Performance antipatterns are detected from the summarised data using a rule-engine approach and are assessed in terms of their performance impact using performance models. Any antipatterns found are presented to the user in a diagramatic format.Poor performance is a major obstacle that has to be overcome when delivering large internet-enabled enterprise systems. Often projects fail or face serious delays when they do not meet their performance goals. As shown in the literature [11] the design of enterprise systems plays a major role in how they meet their performance requirements. However, bad design from performance perspective is very common in such systems. This is quite evident from the number of common performance design mistakes which have been well documented [6] [5] [7]. Design mistakes generally results from the fact that these multi-tier distributed systems are extremely complex and often developers do not have a complete understanding of the entire application. As a result developers can be oblivious to the performance implications of their design decisions. Fixing serious design-level performance problems late in developent is very expensive and can not be achieved through "code optimizations". While the approach of "developing first, optimize later" may work at a class level, re-engineering large parts of an enterprise architecture may prove extremely expensive.Furthermore, while good design decisions are imperative for attaining performance goals, there are also important decisions that need to be taken during deployment of enterprise systems which affect performance significantly. This stems from the fact that in the case of enterprise frameworks (such as EJB), many decisions, that were inherent in the design and coding of systems in the past, have been abstracted out of the application source code into the deployment settings of the system. In EJB for example the granularity and type of transactions can be specified in the XML deployment descriptors of the application. Many such decisions can now be made at the deployment level. As a result, when configuring deployment time settings, different design trade-offs that can significantly impact performance must be considered. However, a real problem for system deployers is that component based frameworks can require a complex deployment strategy in order to make use of the many services provided. There are numerous different settings that need to be tuned correctly in order to have an efficiently running system. Examples include caching policies, object pools, database settings etc. Again a lack of understanding of enterprise applications (due to their complex nature) means that developers are often unsure as to how to go about configuring them co...

Specification and Detection of SOA Antipatterns

“…For example, a utility function may report an anomalous behavior corresponding to spam sending if it detects a large number of threads. In another relevant work, Parsons et al [21] introduced an approach for the detection of performance antipatterns specifically in component-based enterprise systems (in particular, JEE applications) using a rule-based approach relying on static and dynamic analysis.…”

QVTR2: A Rational and Performance-Aware Extension to the Relations Language

“…However, solutions are proposed only for trivial performance issues and, if this is the case, changes apply to the lowest abstraction layer without propagation along the modeling stack. Parsons in [7] describes a similar approach tailored to enterprise Java application. More recent work is also available in [6], where a methodology is proposed to rank performance anti-patterns and identify the most reasonable cause of a performance problem after a design of the system has been created.…”

A Hybrid Approach for Multi-attribute QoS Optimisation in Component Based Software Systems

“…Parsons et al [30] present a framework for detecting performance anti-patterns in Java EE architectures. The method requires an implementation of a component-based system, which can be monitored for performance properties.…”