951 resultados para Application performance monitoring.


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Wednesday 23rd April 2014 Speaker(s): Willi Hasselbring Organiser: Leslie Carr Time: 23/04/2014 14:00-15:00 Location: B32/3077 File size: 802Mb Abstract The internal behavior of large-scale software systems cannot be determined on the basis of static (e.g., source code) analysis alone. Kieker provides complementary dynamic analysis capabilities, i.e., monitoring/profiling and analyzing a software system's runtime behavior. Application Performance Monitoring is concerned with continuously observing a software system's performance-specific runtime behavior, including analyses like assessing service level compliance or detecting and diagnosing performance problems. Architecture Discovery is concerned with extracting architectural information from an existing software system, including both structural and behavioral aspects like identifying architectural entities (e.g., components and classes) and their interactions (e.g., local or remote procedure calls). In addition to the Architecture Discovery of Java systems, Kieker supports Architecture Discovery for other platforms, including legacy systems, for instance, inplemented in C#, C++, Visual Basic 6, COBOL or Perl. Thanks to Kieker's extensible architecture it is easy to implement and use custom extensions and plugins. Kieker was designed for continuous monitoring in production systems inducing only a very low overhead, which has been evaluated in extensive benchmark experiments. Please, refer to http://kieker-monitoring.net/ for more information.

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6PANview[1] is a Wireless Sensor Network(WSN) monitoring system for 6LoWPAN/RPL networks which we developed as an overlay network for a WSN application. A monitoring system, while performing its operations for maintaining the health of the monitored network, must also be conscious of its impact on the application performance, and must strive to minimize this impact. To this end, we propose a centralized scheduling algorithm within 6PANview which non-intrusively analyzes application traffic arrival patterns at the base station, identifies network idle periods and schedules monitoring activities. The proposed algorithm finds those periodic sequences which are likely to have given rise to the pattern of arrivals seen at the base station. Parts of those sequences are then extended to coarsely predict future traffic and find epochs where low traffic is predicted, in order to schedule monitoring traffic or other activities at these times. We present simulation results for the proposed prediction and scheduling algorithm and its implementation as part of 6PANview. As an enhancement, we briefly talk about using 6PANview's overlay network architecture for distributed scheduling.

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Approximately half of the houses in Northern Ireland were built before any form of minimum thermal specification (U-value) or energy efficiency standard were available. At present, 44% of households are categorised as being in fuel poverty; spending more than 10% of the household income to heat the house to an acceptable level. This paper presents the results from long term performance monitoring of 4 case study houses that have undergone retrofits to improve energy efficiency in Northern Ireland. There is some uncertainty associated with some of the marketed retrofit measures in terms of their effectiveness in reducing energy usage and their potential to cause detrimental impacts on the internal environment of a house. Using wireless sensor technology internal conditions such as temperature and humidity were measured alongside gas and electricity usage for a year. External weather conditions were also monitored. The paper considers the effectiveness of the different retrofit measures implemented based on the long term data monitoring and short term building performance evaluation tests that were completed.

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