Ehlers, Jens (2012) Self-Adaptive Performance Monitoring for Component-Based Software Systems. (PhD/ Doctoral thesis), Kiel University, Kiel, 252 pp. . Kiel Computer Science Series, 2012/1 .

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Abstract

Effective monitoring of a software system’s runtime behavior is necessary to
evaluate the compliance of performance objectives. In addition to studying
the construction and evolution of software systems, the software engineering
discipline needs to emphasize the interest on robust and flexible software
system operation, including means for continuous monitoring.
Though performance is a critical characteristic for software systems, tools
addressing application performance monitoring, i.e. monitoring the operation
of software systems at application level, are rarely used in practice. This
prevalent negligence is expressed by the following symptoms: (1) a
posteriori failure analysis, i.e. appropriate monitoring data is seldom
collected and evaluated systematically before a failure or performance
anomaly occurs, (2) inflexible instrumentation, i.e. probes are injected
only at a limited number of fixed measuring points such that component
recompilation and redeployment are required for future modifications, and
(3) inability of tracing in distributed systems, i.e. tracing of user requests or
transactions beyond the borders of components or their execution containers
is not supported or not applied.
This thesis has emerged in the context of the Kieker monitoring framework,
which targets the above shortcomings. A finding of our experimental
overhead evaluation is that it is feasible to instrument probes at a multitude
of possibly relevant measuring points, as long as not all of them are
active at the same time during operation. Therefore, this thesis proposes
a self-adaptive performance monitoring approach allowing for dynamic
activation of probes and measuring points. As typical for autonomic
systems, a control feedback cycle manages the adaptation of the monitoring
coverage at runtime. The solution is based on OCL-based monitoring rules

that refine the monitoring granularity for those components that show
anomalous responsiveness.
The monitoring data includes performance measures such as throughput
and response time statistics, the utilization of system resources, as well as
the inter- and intra-component control flow. Based on this data, performance
anomaly scores for each provided service and component-inherent
operation are computed. The presented anomaly scoring algorithms are
based on time series analysis and distribution clustering, respectively.
This self-adaptive performance monitoring approach for component-based
software systems reduces the business-critical failure diagnosis time, as it
saves time-consuming manual debugging activities. The approach and its
underlying anomaly scores are extensively evaluated in lab experiments, e.g.
using the SPECjEnterprise2010 industry benchmark. The evaluation results,
in combination with the implementation of the Kieker tool, demonstrate
the feasibility and the practicability of the approach.

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