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.
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.
Software faults are a major threat for the dependability of software systems. When we intend to study the impact of software faults on software behavior, examine the quality of fault tolerance mechanisms, or evaluate diagnostic techniques, the issue of distinguishing fault categories and their frequency distribution arises immediately. This article surveys the literature that provides quantitative data on categories of software faults and discusses the applicability of these software fault category distributions to fault injection case studies.
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