André van Hoorn

Kieker: a framework for application performance monitoring and dynamic software analysis

Kieker is an extensible framework for monitoring and analyzing the runtime behavior of concurrent or distributed software systems. It provides measurement probes for application performance monitoring and control-flow tracing. Analysis plugins extract and visualize architectural models, augmented by quantitative observations. Configurable readers and writers allow Kieker to be used for online and offline analysis. This paper reviews the Kieker framework focusing on its features, its provided extension points for custom components, as well the imposed monitoring overhead.

Generating Probabilistic and Intensity-Varying Workload for Web-Based Software Systems

This paper presents an approach and a corresponding tool for generating probabilistic and intensity-varying workload for Web-based software systems. The workload to be generated is specified in two types of models. An application model specifies the possible interactions with the Web-based software system, as well as all required low-level protocol details by means of a hierarchical finite state machine. Based on the application model, the probabilistic usage is specified in corresponding user behavior models by means of Markov chains. Our tool Markov4JMeter implements our approach to probabilistic workload generation by extending the popular workload generation tool JMeter. A case study demonstrates how probabilistic workload for a sample Web application can be modeled and executed using Markov4JMeter.

Self-adaptive software system monitoring for performance anomaly localization

Autonomic computing components and services require continuous monitoring capabilities for collecting and analyzing data of runtime behavior. Particularly for software systems, a trade-off between monitoring coverage and performance overhead is necessary.n In this paper, we propose an approach for localizing performance anomalies in software systems employing self-adaptive monitoring. Time series analysis of operation response times, incorporating architectural information about the diagnosed software system, is employed for anomaly localization. Comprising quality of service data, such as response times, resource utilization, and anomaly scores, OCL-based monitoring rules specify the adaptive monitoring coverage. This enables to zoom into a systems or components internal realization in order to locate root causes of software failures and to prevent failures by early fault determination and correction.n The approach has been implemented as part of the Kieker monitoring and analysis framework. The evaluation presented in this paper focuses on monitoring overhead, response time forecasts, and the anomaly detection process.

Automatic Failure Diagnosis Support in Distributed Large-Scale Software Systems Based on Timing Behavior Anomaly Correlation

Manual failure diagnosis in large-scale software systems is time-consuming and error-prone. Automatic failure diagnosis support mechanisms can potentially narrow down, or even localize faults within a very short time which both helps to preserve system availability. A large class of automatic failure diagnosis approaches consists of two steps: 1) computation of component anomaly scores; 2) global correlation of the anomaly scores for fault localization. In this paper, we present an architecture-centric approach for the second step. In our approach, component anomaly scores are correlated based on architectural dependency graphs of the software system and a rule set to address error propagation. Moreover, the results are graphically visualized in order to support fault localization and to enhance maintainability. The visualization combines architectural diagrams automatically derived from monitoring data with failure diagnosis results. In a case study, the approach is applied to a distributed sample Web application which is subject to fault injection.

Modeling run-time adaptation at the system architecture level in dynamic service-oriented environments

Today, software systems are more and more executed in dynamic, virtualized environments. These environments host diverse applications of different parties, sharing the underlying resources. The goal of this resource sharing is to utilize resources efficiently while ensuring that quality-of-service requirements are continuously satisfied. In such scenarios, complex adaptations to changes in the system environment are still largely performed manually by humans. Over the past decade, autonomic self-adaptation techniques aiming to minimize human intervention have become increasingly popular. However, given that adaptation processes are usually highly system-specific, it is a challenge to abstract from system details, enabling the reuse of adaptation strategies. In this paper, we present S/T/A, a modeling language to describe system adaptation processes at the system architecture level in a generic, human-understandable and reusable way. We apply our approach to multiple different realistic contexts (dynamic resource allocation, run-time adaptation planning, etc.). The results show how a holistic model-based approach can close the gap between complex manual adaptations and their autonomous execution.

An adaptation framework enabling resource-efficient operation of software systems

This paper gives an overview about our current work on a framework which aims at operating component-based software systems more efficiently. Efficiency, in terms of the number of allocated data center resources, is improved by executing architecture-level runtime adaptations based on current workload situations. The proposed framework, called SLAstic, is described and open questions to be answered in future work are raised.

Performance simulation of runtime reconfigurable component-based software architectures

Architectural runtime reconfiguration is a promising means for controlling the quality of service (QoS) of distributed software systems. Particularly self-adaptation approaches rely on runtime reconfiguration capabilities provided by the systems under control. For example, our online capacity management approach SLAstic employs changing component deployments and server allocations to control the performance and resource efficiency of component-based (C-B) software systems at runtime. In this context, we developed a performance simulator for runtime configurable C-B software systems, called SLAstic.SIM. The system architectures to be simulated are specified as instances of the Palladio Component Model (PCM). The simulation is driven by external workload traces and reconfiguration plans which can be requested during simulation based on continuously accessible monitoring data of the simulated systems. This paper demonstrates SLAstic.SIM including a quantitative evaluation of its performance.

Automatic extraction of probabilistic workload specifications for load testing session-based application systems

Workload generation is essential to systematically evaluate performance properties of application systems under controlled conditions, e.g., in load tests or benchmarks. The definition of workload specifications that represent the real workload as accurately as possible is one of the biggest challenges in this area. This paper presents our approach for the modeling and automatic extraction of probabilistic workload specifications for load testing session-based application systems. The approach, called Wessbas, comprises (i.) a domain-specific language (DSL) enabling layered modeling of workload specifications as well as support for (ii.) automatically extracting instances of the DSL from recorded sessions logs and (iii.) transforming instances of the DSL to workload specifications of existing load testing tools. During the extraction process, different groups of customers with similar navigational patterns are identified using clustering techniques. We developed corresponding tool support including a transformation to probabilistic test scripts for the Apache JMeter load testing tool. The evaluation of the proposed approach using the industry standard benchmark SPECjEnterprise2010 demonstrates its applicability and the representativeness of the extracted workloads.

Micro-Benchmarking BPMN 2.0 Workflow Management Systems with Workflow Patterns

Although Workflow Management Systems (WfMSs) are a key component in workflow technology, research work for assessing and comparing their performance is limited. This work proposes the first micro-benchmark for WfMSs that can execute BPMN 2.0 workflows. To this end, we focus on studying the performance impact of well-known workflow patterns expressed in BPMN 2.0 with respect to three open source WfMSs. We executed all the experiments under a reliable environment and produced a set of meaningful metrics. This paper contributes to the area of workflow technology by defining building blocks for more complex BPMN 2.0 WfMS benchmarks. The results have shown bottlenecks on architectural design decisions, resource utilization, and limits on the load a WfMS can sustain, especially for the cases of complex and parallel structures. Experiments on a mix of workflow patterns indicated that there are no unexpected performance side effects when executing different workflow patterns concurrently, although the duration of the individual workflows that comprised the mix was increased.

Workload-intensity-sensitive timing behavior analysis for distributed multi-user software systems

In many multi-user software systems, such as online shopping systems, varying workload intensity causes high statistical variance in timing behavior distributions. However, this major impact on timing behavior is often ignored. This paper introduces our approach WITiBA (Workload-Intensity-Sensitive Timing Behavior Analysis) to consider inter-dependencies between concurrent executions of software operations within a distributed system to reduce the standard deviation for succeeding analysis steps. This can be beneficial for analysis methods or simulation methods in terms of tighter confidence intervals, or shorter simulations.