11th EAI International Conference on Performance Evaluation Methodologies and Tools

Research Article

The Back End is Only One Part of the Picture: Mobile-Aware Application Performance Monitoring and Problem Diagnosis

  • @INPROCEEDINGS{10.4108/eai.5-12-2017.2274717,
        author={Katrin  Angerbauer and Dušan  Okanović and Andr\^{e}  van Hoorn and Christoph  Heger},
        title={The Back End is Only One Part of the Picture: Mobile-Aware Application Performance Monitoring and Problem Diagnosis},
        proceedings={11th EAI International Conference on Performance Evaluation Methodologies and Tools},
        publisher={ACM},
        proceedings_a={VALUETOOLS},
        year={2018},
        month={8},
        keywords={mobile computing application performance management performance anti patterns},
        doi={10.4108/eai.5-12-2017.2274717}
    }
    
  • Katrin Angerbauer
    Dušan Okanović
    André van Hoorn
    Christoph Heger
    Year: 2018
    The Back End is Only One Part of the Picture: Mobile-Aware Application Performance Monitoring and Problem Diagnosis
    VALUETOOLS
    ACM
    DOI: 10.4108/eai.5-12-2017.2274717
Katrin Angerbauer1,*, Dušan Okanović1, André van Hoorn1, Christoph Heger2
  • 1: University of Stuttgart
  • 2: NovaTec GmbH
*Contact email: angerbkn@studi.informatik.uni-stuttgart.de

Abstract

The success of modern businesses relies on the quality of their supporting application systems. Continuous application performance management is mandatory to enable efficient problem detection, diagnosis, and resolution during production. In today’s age of ubiquitous computing, large fractions of users access application systems from mobile devices, such as phones and tablets. For detecting, diagnosing, and resolving performance and availability problems, an end-to-end view, i.e., traceability of requests starting on the (mobile) clients’ devices, is becoming increasingly important. In this paper, we propose an approach for end-to-end monitoring of applications from the users’ mobile devices to the back end, and diagnosing root-causes of detected performance problems. We extend our previous work on diagnosing performance anti-patterns from execution traces by new metrics and rules. The evaluation of this work shows that our approach successfully detects and diagnoses performance anti-patterns in applications with iOS-based mobile clients. While there are threats to validity to our experiment, our research is a promising starting point for future work.