Cloud computing provides scalable computing resources on demand. Monitoring cloud computing resources is important so that resources can be dynamically allocated, migrated, or shut down to meet users' requirements...
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Cloud computing provides scalable computing resources on demand. Monitoring cloud computing resources is important so that resources can be dynamically allocated, migrated, or shut down to meet users' requirements. Challenges in cloud computing monitoring systems include detecting patterns that might lead to failure of the cloud system, detecting malfunctioning problems within the cloud platform after they occur, and issues related to the fact that existing monitoring solutions are tightly coupled to specific cloud platforms, while there is an emerging need for monitoring solutions that are independent of underlying subsystems. To address these challenges, we designed a hybrid cloud monitoring software architecture that can work as an add-on layer on top of existing cloud computing platforms. The proposed architecture is designed based on facade software design pattern and utilizes complex event processing concept in which data from various primitive metrics streams is processed to detect previously defined patterns. Prototype applications have been developed to demonstrate the architecture's usability. Performance tests were applied to prototype applications. Computation times required for the operation of the proposed architecture were negligible.
The real-timedata analysis requires an integrated approach to know the last known state of variables of a concept under monitoring. Thereby, the Internet-of-Thing (IoT) devices have provided alternatives to address d...
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The real-timedata analysis requires an integrated approach to know the last known state of variables of a concept under monitoring. Thereby, the Internet-of-Thing (IoT) devices have provided alternatives to address distributed data collection strategies. However, the autonomy of IoT devices represents one of the main challenges to implement the collecting strategy. Battery autonomy is affected directly by the energy consumption derived from data transmissions. The data Stream processing Strategy (DSPS) is an architecture oriented to the implementation of measurement projects based on a measurement and evaluation framework. Its online processing is guided by the measurement metadata informed from IoT devices associated with a component named Measurement Adapter (MA). This paper presents a new data buffer organization based on measurement metadata articulated with online data filtering to optimize the data transmissions from MA. As contributions, a weighted data change detection approach is incorporated, while a new local buffer based on logical windows is proposed for MA. Also, an articulation among the data buffer, a temporal barrier, and data change detectors is introduced. The proposal was implemented and released on the pabmmCommons library. A discrete simulation on the library is here described to provide initial applicability patterns. The data buffer consumed 568 Kb for monitoring 100 simultaneous metrics. The online estimation of the mean and variance based on the Statistical Process Control consumed 238 ns. However, as a limitation, other scenarios need to be addressed before generalizing results. As future work, new alternatives to filter noise online will be addressed.
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